Gaussian Process Regression for Materials and Molecules

Abstract

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

References 420

1. 1.

   Brown T. E.; LeMay H. E.; Bursten B. E.; Murphy C.; Woodward P.; Stoltzfus M. E. Chemistry: The Central Science, 14th ed.; Pearson: New York, 2018.

   Not in our library

2. 2.
   The Centrality of Chemistry · 2015

   Not checked
3. 3.

   Alberi K.; Nardelli M. B.; Zakutayev A.; Mitas L.; Curtarolo S.; Jain A.; Fornari M.; Marzari N.; Takeuchi I.; Green M. L.; et al. The 2019 Materials by Design Roadmap. J. Phys. D: Appl. Phys. 2019, 52, 013001.

   Not in our library10.1088/1361-6463/aad926 (opens in a new tab)

4. 4.

   Oganov A. R.; Pickard C. J.; Zhu Q.; Needs R. J. Structure Prediction Drives Materials Discovery. Nat. Rev. Mater. 2019, 4, 331–348..

   Not in our library10.1038/s41578-019-0101-8 (opens in a new tab)

5. 5.

   Zhang W.; Mazzarello R.; Wuttig M.; Ma E. Designing Crystallization in Phase-Change Materials for Universal Memory and Neuro-Inspired Computing. Nat. Rev. Mater. 2019, 4, 150–168..

   Not in our library10.1038/s41578-018-0076-x (opens in a new tab)

6. 6.

   Tabor D. P.; Roch L. M.; Saikin S. K.; Kreisbeck C.; Sheberla D.; Montoya J. H.; Dwaraknath S.; Aykol M.; Ortiz C.; Tribukait H.; Amador-Bedolla C.; Brabec B.; Maruyama C. J.; Persson K. A.; Aspuru-Guzik A. Accelerating the Discovery of Materials for Clean Energy in the Era of Smart Automation. Nat. Rev. Mater. 2018, 3, 5–20..

   Not in our library10.1038/s41578-018-0005-z (opens in a new tab)

7. 7.

   Louie S. G.; Chan Y.-H.; da Jornada F. H.; Li Z.; Qiu D. Y. Discovering and Understanding Materials Through Computation. Nat. Mater. 2021, 20, 728–735..

   Not in our library10.1038/s41563-021-01015-1 (opens in a new tab)

8. 8.

   Marzari N.; Ferretti A.; Wolverton C. Electronic-Structure Methods for Materials Design. Nat. Mater. 2021, 20, 736–749..

   Not in our library10.1038/s41563-021-01013-3 (opens in a new tab)

9. 9.

   Burke K. Perspective on Density Functional Theory. J. Chem. Phys. 2012, 136, 150901.

   Not in our library10.1063/1.4704546 (opens in a new tab)

10. 10.

    Jones R. O. Density Functional Theory: Its Origins, Rise to Prominence, and Future. Rev. Mod. Phys. 2015, 87, 897–923..

    Not in our library10.1103/revmodphys.87.897 (opens in a new tab)

11. 11.

    Maurer R. J.; Freysoldt C.; Reilly A. M.; Brandenburg J. G.; Hofmann O. T.; Björkman T.; Lebègue S.; Tkatchenko A. Advances in Density-Functional Calculations for Materials Modeling. Annu. Rev. Mater. Res. 2019, 49, 1–30..

    Not in our library10.1146/annurev-matsci-070218-010143 (opens in a new tab)

12. 12.

    Bonhomme C.; Gervais C.; Babonneau F.; Coelho C.; Pourpoint F.; Azaïs T.; Ashbrook S. E.; Griffin J. M.; Yates J. R.; Mauri F.; Pickard C. J. First-Principles Calculation of NMR Parameters Using the Gauge Including Projector Augmented Wave Method: A Chemist’s Point of View. Chem. Rev. 2012, 112, 5733–5779..

    Not in our library10.1021/cr300108a (opens in a new tab)

13. 13.

    Lejaeghere K.; Bihlmayer G.; Björkman T.; Blaha P.; Blügel S.; Blum V.; Caliste D.; Castelli I. E.; Clark S. J.; Dal Corso A.; et al. Reproducibility in Density Functional Theory Calculations of Solids. Science 2016, 351, aad3000.

    Not in our library10.1126/science.aad3000 (opens in a new tab)

14. 14.

    Prentice J. C. A.; Aarons J.; Womack J. C.; Allen A. E. A.; Andrinopoulos L.; Anton L.; Bell R. A.; Bhandari A.; Bramley G. A.; Charlton R. J.; et al. The ONETEP linear-scaling density functional theory program. J. Chem. Phys. 2020, 152, 174111.

    Not in our library10.1063/5.0004445 (opens in a new tab)

15. 15.

    Del Ben M.; Schönherr M.; Hutter J.; Vandevondele J. Bulk Liquid Water at Ambient Temperature and Pressure from Mp2 Theory. J. Phys. Chem. Lett. 2013, 4, 3753–3759..

    Not in our library10.1021/jz401931f (opens in a new tab)

16. 16.

    Zen A.; Luo Y.; Mazzola G.; Guidoni L.; Sorella S. Ab Initio Molecular Dynamics Simulation of Liquid Water by Quantum Monte Carlo. J. Chem. Phys. 2015, 142, 144111.

    Not in our library10.1063/1.4917171 (opens in a new tab)

17. 17.

    Wilhelm J.; VandeVondele J.; Rybkin V. V. Dynamics of the Bulk Hydrated Electron from Many-Body Wave-Function Theory. Angew. Chem., Int. Ed. 2019, 58, 3890–3893..

    Not in our library10.1002/anie.201814053 (opens in a new tab)

18. 18.

    Zhou Y.; Sun L.; Zewdie G. M.; Mazzarello R.; Deringer V. L.; Ma E.; Zhang W. Bonding Similarities and Differences between Y-Sb-Te and Sc-Sb-Te Phase-Change Memory Materials. J. Mater. Chem. C 2020, 8, 3646–3654..

    Not in our library10.1039/d0tc00096e (opens in a new tab)

19. 19.

    George J.; Hautier G. Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques. Trends Chem. 2021, 3, 86–95..

    Not in our library10.1016/j.trechm.2020.10.007 (opens in a new tab)

20. 20.

    Butler K. T.; Davies D. W.; Cartwright H.; Isayev O.; Walsh A. Machine Learning for Molecular and Materials Science. Nature2018, 559, 547–555.

    Not in our library10.1038/s41586-018-0337-2 (opens in a new tab)

21. 21.
    Data‐Driven Materials Science: Status, Challenges, and Perspectives · 2019

    Corrected
22. 22.

    Batra R.; Song L.; Ramprasad R. Emerging Materials Intelligence Ecosystems Propelled by Machine Learning. Nat. Rev. Mater. 2020, 1–24..

    Not in our library10.1038/s41578-020-00255-y (opens in a new tab)

23. 23.

    Ramprasad R.; Batra R.; Pilania G.; Mannodi-Kanakkithodi A.; Kim C. Machine Learning in Materials Informatics: Recent Applications and Prospects. npj Comput. Mater. 2017, 3, 54.

    Not in our library10.1038/s41524-017-0056-5 (opens in a new tab)

24. 24.

    Schmidt J.; Marques M. R. G.; Botti S.; Marques M. A. L. Recent Advances and Applications of Machine Learning in Solid-State Materials Science. npj Comput. Mater. 2019, 5, 83.

    Not in our library10.1038/s41524-019-0221-0 (opens in a new tab)

25. 25.

    Schleder G. R.; Padilha A. C. M.; Acosta C. M.; Costa M.; Fazzio A. From DFT to Machine Learning: Recent Approaches to Materials Science - A Review. J. Phys.: Mater. 2019, 2, 032001.

    Not in our library10.1088/2515-7639/ab084b (opens in a new tab)

26. 26.

    Dral P. O. Quantum Chemistry in the Age of Machine Learning. J. Phys. Chem. Lett. 2020, 11, 2336–2347..

    Not in our library10.1021/acs.jpclett.9b03664 (opens in a new tab)

27. 27.

    Carleo G.; Cirac I.; Cranmer K.; Daudet L.; Schuld M.; Tishby N.; Vogt-Maranto L.; Zdeborová L. Machine Learning and the Physical Sciences. Rev. Mod. Phys. 2019, 91, 045002.

    Not in our library10.1103/revmodphys.91.045002 (opens in a new tab)

28. 28.

    Behler J. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. Angew. Chem., Int. Ed. 2017, 56, 12828–12840..

    Not in our library10.1002/anie.201703114 (opens in a new tab)

29. 29.

    Deringer V. L.; Caro M. A.; Csányi G. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Adv. Mater. 2019, 31, 1902765.

    Not in our library10.1002/adma.201902765 (opens in a new tab)

30. 30.

    Noé F.; Tkatchenko A.; Müller K.-R.; Clementi C. Machine Learning for Molecular Simulation. Annu. Rev. Phys. Chem. 2020, 71, 361–390..

    Not in our library10.1146/annurev-physchem-042018-052331 (opens in a new tab)

31. 31.

    Friederich P.; Häse F.; Proppe J.; Aspuru-Guzik A. Machine-Learned Potentials for Next-Generation Matter Simulations. Nat. Mater. 2021, 20, 750–761..

    Not in our library10.1038/s41563-020-0777-6 (opens in a new tab)

32. 32.

    Kamath A.; Vargas-Hernández R. A.; Krems R. V.; Carrington T.; Manzhos S. Neural Networks vs Gaussian Process Regression for Representing Potential Energy Surfaces: A Comparative Study of Fit Quality and Vibrational Spectrum Accuracy. J. Chem. Phys. 2018, 148, 241702.

    Not in our library10.1063/1.5003074 (opens in a new tab)

33. 33.

    Zuo Y.; Chen C.; Li X.; Deng Z.; Chen Y.; Behler J.; Csányi G.; Shapeev A. V.; Thompson A. P.; Wood M. A.; Ong S. P. Performance and Cost Assessment of Machine Learning Interatomic Potentials. J. Phys. Chem. A 2020, 124, 731–745..

    Not in our library10.1021/acs.jpca.9b08723 (opens in a new tab)

34. 34.

    Bartók A. P.; Csányi G. Gaussian Approximation Potentials: A Brief Tutorial Introduction. Int. J. Quantum Chem. 2015, 115, 1051–1057..

    Not in our library10.1002/qua.24927 (opens in a new tab)

35. 35.

    Behler J. Perspective: Machine Learning Potentials for Atomistic Simulations. J. Chem. Phys. 2016, 145, 170901.

    Not in our library10.1063/1.4966192 (opens in a new tab)

36. 36.

    Chan H.; Narayanan B.; Cherukara M. J.; Sen F. G.; Sasikumar K.; Gray S. K.; Chan M. K. Y.; Sankaranarayanan S. K. R. S. Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data. J. Phys. Chem. C 2019, 123, 6941–6957..

    Not in our library10.1021/acs.jpcc.8b09917 (opens in a new tab)

37. 37.

    Mueller T.; Hernandez A.; Wang C. Machine Learning for Interatomic Potential Models. J. Chem. Phys. 2020, 152, 050902.

    Not in our library10.1063/1.5126336 (opens in a new tab)

38. 38.

    Miksch A. M.; Morawietz T.; Kästner J.; Urban A.; Artrith N. Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations. Mach. Learn.: Sci. Technol.2021,.

    Not in our library10.1088/2632-2153/abfd96 (opens in a new tab)

39. 39.

    Rasmussen C. E.; Williams C. K. I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning); The MIT Press, 2006.

    Not in our library

40. 40.

    Schölkopf B.; Smola A. J. Learning with kernels: support vector machines, regularization, optimization, and beyond; MIT Press: Cambridge, Mass, 2002.

    Not in our library

41. 41.

    Tikhonov A. N. Solution of incorrectly formulated problems and the regularization method. Sov. Math. 1963, 4, 1035.

    Not in our library

42. 42.

    Snelson E.; Ghahramani Z. Sparse Gaussian Processes Using Pseudo-Inputs. Adv. Neural Inf. Process. Syst. 2005, 18, 1257–1264.

    Not in our library

43. 43.

    Quinonero-Candela J. Q.; Rasmussen C. E. A unifying view of sparse approximate Gaussian process regression. J. Machine Learn. Res. 2005, 6, 1939–1959.

    Not in our library

44. 44.

    Mackay D. Information Theory, Inference, and Learning Algorithms; Cambridge Univ. Press: Cambridge, United Kingdom, 2003.

    Not in our library

45. 45.

    Williams C. K. I. On a Connection between Kernel PCA and Metric Multidimensional Scaling. Mach. Learn. 2002, 46, 11–19..

    Not in our library10.1023/a:1012485807823 (opens in a new tab)

46. 46.

    Cuturi M. Positive Definite Kernels in Machine Learning. 2009; ArXiv Prepr. ArXiv09115367.

    Not in our library

47. 47.

    Schölkopf B.; Smola A.; Müller K.-R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Comput. 1998, 10, 1299–1319..

    Not in our library10.1162/089976698300017467 (opens in a new tab)

48. 48.

    Williams C. K. I.; Seeger M. In Advances in Neural Information Processing Systems 13; Leen T. K., Dietterich T. G., Tresp V., Eds.; MIT Press, 2001; pp 682–688.

    Not in our library

49. 49.

    Bartók A. P.; Payne M. C.; Kondor R.; Csányi G. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, Without the Electrons. Phys. Rev. Lett. 2010, 104, 136403.

    Not in our library10.1103/physrevlett.104.136403 (opens in a new tab)

50. 50.

    Solak E.; Murray-Smith R.; Leithead W.; Leith D.; Rasmussen C. Derivative Observations in Gaussian Process Models of Dynamic Systems. Advances in Neural Information Processing Systems; 2003; pp 1057–1064.

    Not in our library

51. 51.

    Musil F.; Grisafi A.; Bartók A. P.; Ortner C.; Csányi G.; Ceriotti M. Physics-inspired structural representations for molecules and materials. Chem. Rev. 2021,.

    Not in our library10.1021/acs.chemrev.1c00021 (opens in a new tab)

52. 52.

    Bartók A. P.; Kondor R.; Csányi G. On Representing Chemical Environments. Phys. Rev. B: Condens. Matter Mater. Phys. 2013, 87, 184115.

    Not in our library10.1103/physrevb.87.184115 (opens in a new tab)

53. 53.

    Bernstein N.; Bhattarai B.; Csányi G.; Drabold D. A.; Elliott S. R.; Deringer V. L. Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon. Angew. Chem., Int. Ed. 2019, 58, 7057–7061..

    Not in our library10.1002/anie.201902625 (opens in a new tab)

54. 54.

    Abraham F. F.; Walkup R.; Gao H.; Duchaineau M.; De La Rubia T. D.; Seager M. Simulating materials failure by using up to one billion atoms and the world’s fastest computer: Work-hardening. Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 5783–5787..

    Not in our library10.1073/pnas.062054999 (opens in a new tab)

55. 55.

    Fumi F. G.; Tosi M. P. Ionic sizes and born repulsive parameters in the NaCl-type alkali halides—I. J. Phys. Chem. Solids 1964, 25, 31–43..

    Not in our library10.1016/0022-3697(64)90159-3 (opens in a new tab)

56. 56.

    Herzbach D.; Binder K.; Müser M. H. Comparison of model potentials for molecular-dynamics simulations of silica. J. Chem. Phys. 2005, 123, 124711.

    Not in our library10.1063/1.2038747 (opens in a new tab)

57. 57.

    Li Z.; Kermode J. R.; De Vita A. Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces. Phys. Rev. Lett. 2015, 114, 096405.

    Not in our library10.1103/physrevlett.114.096405 (opens in a new tab)

58. 58.

    Glielmo A.; Zeni C.; De Vita A. Efficient Nonparametric n-Body Force Fields from Machine Learning. Phys. Rev. B: Condens. Matter Mater. Phys. 2018, 97, 184307.

    Not in our library10.1103/physrevb.97.184307 (opens in a new tab)

59. 59.

    Zeni C.; Rossi K.; Glielmo A.; Fekete A.; Gaston N.; Baletto F.; De Vita A. Building Machine Learning Force Fields for Nanoclusters. J. Chem. Phys. 2018, 148, 241739.

    Not in our library10.1063/1.5024558 (opens in a new tab)

60. 60.

    Lindsey R. K.; Fried L. E.; Goldman N. ChIMES: A Force Matched Potential with Explicit Three-Body Interactions for Molten Carbon. J. Chem. Theory Comput. 2017, 13, 6222–6229..

    Not in our library10.1021/acs.jctc.7b00867 (opens in a new tab)

61. 61.

    Vandermause J.; Torrisi S. B.; Batzner S.; Xie Y.; Sun L.; Kolpak A. M.; Kozinsky B. On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events. npj Comput. Mater. 2020, 6, 20.

    Not in our library10.1038/s41524-020-0283-z (opens in a new tab)

62. 62.

    Davidson E. R. M.; Daff T.; Csanyi G.; Finnis M. W. Grand Canonical Approach to Modeling Hydrogen Trapping at Vacancies in α-Fe. Phys. Rev. Materials 2020, 4, 063804.

    Not in our library10.1103/physrevmaterials.4.063804 (opens in a new tab)

63. 63.

    Braams B. J.; Bowman J. M. Permutationally Invariant Potential Energy Surfaces in High Dimensionality. Int. Rev. Phys. Chem. 2009, 28, 577–606..

    Not in our library10.1080/01442350903234923 (opens in a new tab)

64. 64.

    Allen A. E. A.; Dusson G.; Ortner C.; Csányi G. Atomic permutationally invariant polynomials for fitting molecular force fields. Mach. Learn.: Sci. Technol. 2021, 2, 025017.

    Not in our library10.1088/2632-2153/abd51e (opens in a new tab)

65. 65.

    van der Oord C.; Dusson G.; Csányi G.; Ortner C. Regularised Atomic Body-Ordered Permutation-Invariant Polynomials for the Construction of Interatomic Potentials. Mach. Learn. Sci. Technol. 2020, 1, 015004.

    Not in our library10.1088/2632-2153/ab527c (opens in a new tab)

66. 66.

    Behler J.; Parrinello M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Phys. Rev. Lett. 2007, 98, 146401.

    Not in our library10.1103/physrevlett.98.146401 (opens in a new tab)

67. 67.

    Shapeev A. Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials. Multiscale Model. Simul. 2016, 14, 1153–1173..

    Not in our library10.1137/15m1054183 (opens in a new tab)

68. 68.

    Drautz R. Atomic Cluster Expansion for Accurate and Transferable Interatomic Potentials. Phys. Rev. B: Condens. Matter Mater. Phys. 2019, 99, 014104.

    Not in our library10.1103/physrevb.99.014104 (opens in a new tab)

69. 69.

    Willatt M. J.; Musil F.; Ceriotti M. Feature Optimization for Atomistic Machine Learning Yields a Data-Driven Construction of the Periodic Table of the Elements. Phys. Chem. Chem. Phys. 2018, 20, 29661–29668..

    Not in our library10.1039/c8cp05921g (opens in a new tab)

70. 70.
    Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors · 2020

    Not checked
71. 71.

    Pozdnyakov S. N.; Willatt M. J.; Bartók A. P.; Ortner C.; Csányi G.; Ceriotti M. Incompleteness of Atomic Structure Representations. Phys. Rev. Lett. 2020, 125, 166001.

    Not in our library10.1103/physrevlett.125.166001 (opens in a new tab)

72. 72.

    Imbalzano G.; Anelli A.; Giofré D.; Klees S.; Behler J.; Ceriotti M. Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials. J. Chem. Phys. 2018, 148, 241730.

    Not in our library10.1063/1.5024611 (opens in a new tab)

73. 73.

    Nigam J.; Pozdnyakov S.; Ceriotti M. Recursive Evaluation and Iterative Contraction of N -Body Equivariant Features. J. Chem. Phys. 2020, 153, 121101.

    Not in our library10.1063/5.0021116 (opens in a new tab)

74. 74.

    Natarajan S. K.; Caro M. A. Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials. 2020; arxiv.org/abs/2101.00049.

    Not in our library

75. 75.

    Seko A.; Takahashi A.; Tanaka I. Sparse Representation for a Potential Energy Surface. Phys. Rev. B: Condens. Matter Mater. Phys. 2014, 90, 024101.

    Not in our library10.1103/physrevb.90.024101 (opens in a new tab)

76. 76.

    Seko A.; Takahashi A.; Tanaka I. First-Principles Interatomic Potentials for Ten Elemental Metals via Compressed Sensing. Phys. Rev. B: Condens. Matter Mater. Phys. 2015, 92, 054113.

    Not in our library10.1103/physrevb.92.054113 (opens in a new tab)

77. 77.

    Takahashi A.; Seko A.; Tanaka I. Conceptual and Practical Bases for the High Accuracy of Machine Learning Interatomic Potentials: Application to Elemental Titanium. Phys. Rev. Mater. 2017, 1, 063801.

    Not in our library10.1103/physrevmaterials.1.063801 (opens in a new tab)

78. 78.

    Thompson A. P.; Swiler L. P.; Trott C. R.; Foiles S. M.; Tucker G. J. Spectral Neighbor Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials. J. Comput. Phys. 2015, 285, 316–330..

    Not in our library10.1016/j.jcp.2014.12.018 (opens in a new tab)

79. 79.

    Willatt M. J.; Musil F.; Ceriotti M. Atom-Density Representations for Machine Learning. J. Chem. Phys. 2019, 150, 154110.

    Not in our library10.1063/1.5090481 (opens in a new tab)

80. 80.

    Glielmo A.; Sollich P.; De Vita A. Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels. Phys. Rev. B: Condens. Matter Mater. Phys. 2017, 95, 214302.

    Not in our library10.1103/physrevb.95.214302 (opens in a new tab)

81. 81.

    Bereau T.; Andrienko D.; Von Lilienfeld O. A. Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules. J. Chem. Theory Comput. 2015, 11, 3225–3233..

    Not in our library10.1021/acs.jctc.5b00301 (opens in a new tab)

82. 82.

    Christensen A. S.; Faber F. A.; Anatole von Lilienfeld O. Operators in Quantum Machine Learning: Response Properties in Chemical Space. J. Chem. Phys. 2019, 150, 064105.

    Not in our library10.1063/1.5053562 (opens in a new tab)

83. 83.

    Liang C.; Tocci G.; Wilkins D. M.; Grisafi A.; Roke S.; Ceriotti M. Solvent Fluctuations and Nuclear Quantum Effects Modulate the Molecular Hyperpolarizability of Water. Phys. Rev. B: Condens. Matter Mater. Phys. 2017, 96, 041407.

    Not in our library10.1103/physrevb.96.041407 (opens in a new tab)

84. 84.
    Machine learning molecular dynamics for the simulation of infrared spectra · 2017

    No notice
85. 85.

    Litman Y.; Behler J.; Rossi M. Temperature Dependence of the Vibrational Spectrum of Porphycene: a Qualitative Failure of Classical-Nuclei Molecular Dynamics. Faraday Discuss. 2020, 221, 526.

    Not in our library10.1039/c9fd00056a (opens in a new tab)

86. 86.

    Sommers G. M.; Calegari Andrade M. F.; Zhang L.; Wang H.; Car R. Raman Spectrum and Polarizability of Liquid Water From Deep Neural Networks. Phys. Chem. Chem. Phys. 2020, 22, 10592.

    Not in our library10.1039/d0cp01893g (opens in a new tab)

87. 87.

    Drautz R. Atomic Cluster Expansion of Scalar, Vectorial, and Tensorial Properties Including Magnetism and Charge Transfer. Phys. Rev. B: Condens. Matter Mater. Phys. 2020, 102, 024104.

    Not in our library10.1103/physrevb.102.024104 (opens in a new tab)

88. 88.

    Kondor R. N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials. 2018; arxiv.org/abs/1803.01588.

    Not in our library

89. 89.

    Thomas N.; Smidt T.; Kearnes S. M.; Yang L.; Li L.; Kohlhoff K.; Riley P. Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds. 2018; arxiv.org/abs/1802.08219.

    Not in our library

90. 90.

    Anderson B.; Hy T. S.; Kondor R. Cormorant: Covariant Molecular Neural Networks. Advances in Neural Information Processing Systems 2019, 14537–14546.

    Not in our library

91. 91.

    Grisafi A.; Wilkins D. M.; Csányi G.; Ceriotti M. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems. Phys. Rev. Lett. 2018, 120, 036002.

    Not in our library10.1103/physrevlett.120.036002 (opens in a new tab)

92. 92.

    Grisafi A.; Wilkins D. M.; Willatt M. J.; Ceriotti M. In Machine Learning in Chemistry; Pyzer-Knapp E. O., Laino T., Eds.; American Chemical Society: Washington, DC, 2019; Vol. 1326, pp 1–21.

    Not in our library

93. 93.

    Stone A. J. Transformation between Cartesian and Spherical Tensors. Mol. Phys. 1975, 29, 1461–1471..

    Not in our library10.1080/00268977500101281 (opens in a new tab)

94. 94.

    Partridge H.; Schwenke D. W. The Determination of an Accurate Isotope Dependent Potential Energy Surface for Water from Extensive Ab Initio Calculations and Experimental Data. J. Chem. Phys. 1997, 106, 4618.

    Not in our library10.1063/1.473987 (opens in a new tab)

95. 95.

    Musil F.; Veit M.; Goscinski A.; Fraux G.; Willatt M. J.; Stricker M.; Ceriotti M. Efficient Implementation of Atom-Density Representations. J. Chem. Phys. 2021, 154, 114109.

    Not in our library10.1063/5.0044689 (opens in a new tab)

96. 96.

    Musil F.; Veit M.; Junge T.; Stricker M.; Goscinki A.; Fraux G.; Ceriotti M. LIBRASCAL. https://github.com/cosmo-epfl/librascal.

    Not in our library10.1063/5.0044689 (opens in a new tab)

97. 97.

    Hjorth Larsen A.; Mortensen J. J.; Blomqvist J.; Castelli I. E.; Christensen R.; D M.; Friis J.; Groves M. N.; Hammer B.; Hargus C.; et al. The Atomic Simulation Environment—a Python Library for Working with Atoms. J. Phys.: Condens. Matter 2017, 29, 273002.

    Not in our library10.1088/1361-648x/aa680e (opens in a new tab)

98. 98.

    Kermode J. R.; Bartók A. P.; Csányi G. QUIP. http://www.libatoms.org/.

    Not in our library

99. 99.

    Wilkins D. M.; Grisafi A.; Yang Y.; Lao K. U.; DiStasio R. A.; Ceriotti M. Accurate Molecular Polarizabilities with Coupled Cluster Theory and Machine Learning. Proc. Natl. Acad. Sci. U. S. A. 2019, 116, 3401–3406..

    Not in our library10.1073/pnas.1816132116 (opens in a new tab)

100. 100.

     Blank T. B.; Brown S. D.; Calhoun A. W.; Doren D. J. Neural Network Models of Potential Energy Surfaces. J. Chem. Phys. 1995, 103, 4129–4137..

     Not in our library10.1063/1.469597 (opens in a new tab)

101. 101.

     Manzhos S.; Carrington T. Jr A random-sampling high dimensional model representation neural network for building potential energy surfaces. J. Chem. Phys. 2006, 125, 084109.

     Not in our library10.1063/1.2336223 (opens in a new tab)

102. 102.

     Braams B. J.; Bowman J. M. Permutationally invariant potential energy surfaces in high dimensionality. Int. Rev. Phys. Chem. 2009, 28, 577–606..

     Not in our library10.1080/01442350903234923 (opens in a new tab)

103. 103.

     Lorenz S.; Groß A.; Scheffler M. Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks. Chem. Phys. Lett. 2004, 395, 210–215..

     Not in our library10.1016/j.cplett.2004.07.076 (opens in a new tab)

104. 104.

     Smith J. S.; Isayev O.; Roitberg A. E. ANI-1: an Extensible Neural Network Potential with DFT Accuracy at Force Field Computational Cost. Chem. Sci. 2017, 8, 3192–3203..

     Not in our library10.1039/c6sc05720a (opens in a new tab)

105. 105.
     Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning · 2019

     No notice
106. 106.

     Artrith N.; Urban A. An Implementation of Artificial Neural-Network Potentials for Atomistic Materials Simulations: Performance for TiO2. Comput. Mater. Sci. 2016, 114, 135–150..

     Not in our library10.1016/j.commatsci.2015.11.047 (opens in a new tab)

107. 107.

     Khorshidi A.; Peterson A. A. Amp: A modular approach to machine learning in atomistic simulations. Comput. Phys. Commun. 2016, 207, 310–324..

     Not in our library10.1016/j.cpc.2016.05.010 (opens in a new tab)

108. 108.

     Zhang L.; Han J.; Wang H.; Car R.; E W. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics. Phys. Rev. Lett. 2018, 120, 143001.

     Not in our library10.1103/physrevlett.120.143001 (opens in a new tab)

109. 109.

     Lot R.; Pellegrini F.; Shaidu Y.; Küçükbenli E. PANNA: Properties from Artificial Neural Network Architectures. Comput. Phys. Commun. 2020, 256, 107402.

     Not in our library10.1016/j.cpc.2020.107402 (opens in a new tab)

110. 110.

     Ghasemi S. A.; Hofstetter A.; Saha S.; Goedecker S. Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network. Phys. Rev. B: Condens. Matter Mater. Phys. 2015, 92, 045131.

     Not in our library10.1103/physrevb.92.045131 (opens in a new tab)

111. 111.

     Behler J. Chem. Rev.2021,.

     Not in our library10.1021/acs.chemrev.0c00868 (opens in a new tab)

112. 112.

     Miwa K.; Ohno H. Molecular Dynamics Study on β-Phase Vanadium Monohydride with Machine Learning Potential. Phys. Rev. B: Condens. Matter Mater. Phys. 2016, 94, 184109.

     Not in our library10.1103/physrevb.94.184109 (opens in a new tab)

113. 113.

     Zong H.; Pilania G.; Ding X.; Ackland G. J.; Lookman T. Developing an Interatomic Potential for Martensitic Phase Transformations in Zirconium by Machine Learning. npj Comput. Mater. 2018, 4, 48.

     Not in our library10.1038/s41524-018-0103-x (opens in a new tab)

114. 114.

     Botu V.; Ramprasad R. Adaptive Machine Learning Framework to Accelerate Ab Anitio Molecular Dynamics. Int. J. Quantum Chem. 2015, 115, 1074–1083..

     Not in our library10.1002/qua.24836 (opens in a new tab)

115. 115.

     Botu V.; Ramprasad R. Learning Scheme to Predict Atomic Forces and Accelerate Materials Simulations. Phys. Rev. B: Condens. Matter Mater. Phys. 2015, 92, 094306.

     Not in our library10.1103/physrevb.92.094306 (opens in a new tab)

116. 116.

     Khaliullin R. Z.; Eshet H.; Kühne T. D.; Behler J.; Parrinello M. Graphite-Diamond Phase Coexistence Study Employing a Neural-Network Mapping of the Ab Initio Potential Energy Surface. Phys. Rev. B: Condens. Matter Mater. Phys. 2010, 81, 100103.

     Not in our library10.1103/physrevb.81.100103 (opens in a new tab)

117. 117.

     Khaliullin R.; Eshet H.; Kühne T.; Behler J.; Parrinello M. Nucleation Mechanism for the Direct Graphite-to-Diamond Phase Transition. Nat. Mater. 2011, 10, 693–697..

     Not in our library10.1038/nmat3078 (opens in a new tab)

118. 118.

     Li W.; Ando Y.; Minamitani E.; Watanabe S. Study of Li Atom Diffusion in Amorphous Li3PO4 with Neural Network Potential. J. Chem. Phys. 2017, 147, 214106.

     Not in our library10.1063/1.4997242 (opens in a new tab)

119. 119.

     Fujikake S.; Deringer V. L.; Lee T. H.; Krynski M.; Elliott S. R.; Csányi G. Gaussian Approximation Potential Modeling of Lithium Intercalation in Carbon Nanostructures. J. Chem. Phys. 2018, 148, 241714.

     Not in our library10.1063/1.5016317 (opens in a new tab)

120. 120.

     Wang X.; Tan J.; Han C.; Wang J.-J.; Lu L.; Du H.; Jia C.-L.; Deringer V. L.; Zhou J.; Zhang W. Sub-Angstrom Characterization of the Structural Origin for High In-Plane Anisotropy in 2D GeS2. ACS Nano 2020, 14, 4456–4462..

     Not in our library10.1021/acsnano.9b10057 (opens in a new tab)

121. 121.

     Szlachta W. J.; Bartók A. P.; Csányi G. Accuracy and Transferability of Gaussian Approximation Potential Models for Tungsten. Phys. Rev. B: Condens. Matter Mater. Phys. 2014, 90, 104108.

     Not in our library10.1103/physrevb.90.104108 (opens in a new tab)

122. 122.

     Deringer V. L.; Csányi G. Machine Learning Based Interatomic Potential for Amorphous Carbon. Phys. Rev. B: Condens. Matter Mater. Phys. 2017, 95, 094203.

     Not in our library10.1103/physrevb.95.094203 (opens in a new tab)

123. 123.

     Jinnouchi R.; Lahnsteiner J.; Karsai F.; Kresse G.; Bokdam M. Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference. Phys. Rev. Lett. 2019, 122, 225701.

     Not in our library10.1103/physrevlett.122.225701 (opens in a new tab)

124. 124.

     Jinnouchi R.; Karsai F.; Kresse G. On-the-Fly Machine Learning Force Field Generation: Application to Melting Points. Phys. Rev. B: Condens. Matter Mater. Phys. 2019, 100, 014105.

     Not in our library10.1103/physrevb.100.014105 (opens in a new tab)

125. 125.

     Jinnouchi R.; Miwa K.; Karsai F.; Kresse G.; Asahi R. On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations. J. Phys. Chem. Lett. 2020, 11, 6946–6955..

     Not in our library10.1021/acs.jpclett.0c01061 (opens in a new tab)

126. 126.

     Podryabinkin E. V.; Shapeev A. V. Active Learning of Linearly Parametrized Interatomic Potentials. Comput. Mater. Sci. 2017, 140, 171–180..

     Not in our library10.1016/j.commatsci.2017.08.031 (opens in a new tab)

127. 127.

     Podryabinkin E. V.; Tikhonov E. V.; Shapeev A. V.; Oganov A. R. Accelerating Crystal Structure Prediction by Machine-Learning Interatomic Potentials with Active Learning. Phys. Rev. B: Condens. Matter Mater. Phys. 2019, 99, 064114.

     Not in our library10.1103/physrevb.99.064114 (opens in a new tab)

128. 128.

     Artrith N.; Behler J. High-Dimensional Neural Network Potentials for Metal Surfaces: A Prototype Study for Copper. Phys. Rev. B: Condens. Matter Mater. Phys. 2012, 85, 045439.

     Not in our library10.1103/physrevb.85.045439 (opens in a new tab)

129. 129.

     Zhang L.; Lin D.-Y.; Wang H.; Car R.; E W. Active Learning of Uniformly Accurate Interatomic Potentials for Materials Simulation. Phys. Rev. Materials 2019, 3, 023804.

     Not in our library10.1103/physrevmaterials.3.023804 (opens in a new tab)

130. 130.

     Sivaraman G.; Krishnamoorthy A. N.; Baur M.; Holm C.; Stan M.; Csányi G.; Benmore C.; Vázquez-Mayagoitia A. Machine-Learned Interatomic Potentials by Active Learning: Amorphous and Liquid Hafnium Dioxide. npj Comput. Mater. 2020, 6, 104.

     Not in our library10.1038/s41524-020-00367-7 (opens in a new tab)

131. 131.

     Sivaraman G.; Gallington L.; Krishnamoorthy A. N.; Stan M.; Csányi G.; Vázquez-Mayagoitia A.; Benmore C. J. Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide. Phys. Rev. Lett. 2021, 126, 156002.

     Not in our library10.1103/physrevlett.126.156002 (opens in a new tab)

132. 132.

     Kresse G.; Furthmüller J. Efficient Iterative Schemes for Ab Initio Total-Energy Calculations Using a Plane-Wave Basis Set. Phys. Rev. B: Condens. Matter Mater. Phys. 1996, 54, 11169–11186..

     Not in our library10.1103/physrevb.54.11169 (opens in a new tab)

133. 133.

     Brivio F.; Frost J. M.; Skelton J. M.; Jackson A. J.; Weber O. J.; Weller M. T.; Goñi A. R.; Leguy A. M. A.; Barnes P. R. F.; Walsh A. Lattice Dynamics and Vibrational Spectra of the Orthorhombic, Tetragonal, and Cubic Phases of Methylammonium Lead Iodide. Phys. Rev. B: Condens. Matter Mater. Phys. 2015, 92, 144308.

     Not in our library10.1103/physrevb.92.144308 (opens in a new tab)

134. 134.

     Tong Q.; Xue L.; Lv J.; Wang Y.; Ma Y. Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface. Faraday Discuss. 2018, 211, 31–43..

     Not in our library10.1039/c8fd00055g (opens in a new tab)

135. 135.

     Zong H.; Robinson V. N.; Hermann A.; Zhao L.; Scandolo S.; Ding X.; Ackland G. J. Free electron to electride transition in dense liquid potassium. Nat. Phys. 2021, 1–6..

     Not in our library10.1038/s41567-021-01244-w (opens in a new tab)

136. 136.

     Pickard C. J.; Needs R. J. High-Pressure Phases of Silane. Phys. Rev. Lett. 2006, 97, 045504.

     Not in our library10.1103/physrevlett.97.045504 (opens in a new tab)

137. 137.

     Pickard C. J.; Needs R. J. Ab Initio Random Structure Searching. J. Phys.: Condens. Matter 2011, 23, 053201.

     Not in our library10.1088/0953-8984/23/5/053201 (opens in a new tab)

138. 138.

     Deringer V. L.; Csányi G.; Proserpio D. M. Extracting Crystal Chemistry from Amorphous Carbon Structures. ChemPhysChem 2017, 18, 873–877..

     Not in our library10.1002/cphc.201700151 (opens in a new tab)

139. 139.

     Bartók A. P.; Kermode J.; Bernstein N.; Csányi G. Machine Learning a General-Purpose Interatomic Potential for Silicon. Phys. Rev. X 2018, 8, 041048.

     Not in our library10.1103/physrevx.8.041048 (opens in a new tab)

140. 140.

     Deringer V. L.; Pickard C. J.; Csányi G. Data-Driven Learning of Total and Local Energies in Elemental Boron. Phys. Rev. Lett. 2018, 120, 156001.

     Not in our library10.1103/physrevlett.120.156001 (opens in a new tab)

141. 141.

     Tong Q.; Gao P.; Liu H.; Xie Y.; Lv J.; Wang Y.; Zhao J. Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery. J. Phys. Chem. Lett. 2020, 11, 8710–8720..

     Not in our library10.1021/acs.jpclett.0c02357 (opens in a new tab)

142. 142.

     Deringer V. L.; Proserpio D. M.; Csányi G.; Pickard C. J. Data-Driven Learning and Prediction of Inorganic Crystal Structures. Faraday Discuss. 2018, 211, 45–59..

     Not in our library10.1039/c8fd00034d (opens in a new tab)

143. 143.

     Bernstein N.; Csányi G.; Deringer V. L. De Novo Exploration and Self-Guided Learning of Potential-Energy Surfaces. npj Comput. Mater. 2019, 5, 99.

     Not in our library10.1038/s41524-019-0236-6 (opens in a new tab)

144. 144.

     Mahoney M. W.; Drineas P. CUR Matrix Decompositions for Improved Data Analysis. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 697–702..

     Not in our library10.1073/pnas.0803205106 (opens in a new tab)

145. 145.

     Mavračić J.; Mocanu F. C.; Deringer V. L.; Csányi G.; Elliott S. R. Similarity Between Amorphous and Crystalline Phases: The Case of TiO2. J. Phys. Chem. Lett. 2018, 9, 2985–2990..

     Not in our library10.1021/acs.jpclett.8b01067 (opens in a new tab)

146. 146.

     Glass C. W.; Oganov A. R.; Hansen N. USPEX—Evolutionary Crystal Structure Prediction. Comput. Phys. Commun. 2006, 175, 713–720..

     Not in our library10.1016/j.cpc.2006.07.020 (opens in a new tab)

147. 147.

     Kolsbjerg E. L.; Peterson A. A.; Hammer B. Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles. Phys. Rev. B: Condens. Matter Mater. Phys. 2018, 97, 195424.

     Not in our library10.1103/physrevb.97.195424 (opens in a new tab)

148. 148.

     Wang Y.; Lv J.; Zhu L.; Ma Y. Crystal Structure Prediction via Particle-Swarm Optimization. Phys. Rev. B: Condens. Matter Mater. Phys. 2010, 82, 094116.

     Not in our library10.1103/physrevb.82.094116 (opens in a new tab)

149. 149.

     Wang Y.; Lv J.; Zhu L.; Ma Y. CALYPSO: A Method for Crystal Structure Prediction. Comput. Phys. Commun. 2012, 183, 2063–2070..

     Not in our library10.1016/j.cpc.2012.05.008 (opens in a new tab)

150. 150.

     Eldar Y.; Lindenbaum M.; Porat M.; Zeevi Y. Y. The Farthest Point Strategy for Progressive Image Sampling. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 1997, 6, 1305–15..

     Not in our library10.1109/83.623193 (opens in a new tab)

151. 151.

     Bartók A. P.; De S.; Poelking C.; Bernstein N.; Kermode J. R.; Csányi G.; Ceriotti M. Machine Learning Unifies the Modeling of Materials and Molecules. Sci. Adv. 2017, 3, e1701816.

     Not in our library10.1126/sciadv.1701816 (opens in a new tab)

152. 152.

     Rowe P.; Deringer V. L.; Gasparotto P.; Csányi G.; Michaelides A. An Accurate and Transferable Machine Learning Potential for Carbon. J. Chem. Phys. 2020, 153, 034702.

     Not in our library10.1063/5.0005084 (opens in a new tab)

153. 153.

     Browning N. J.; Ramakrishnan R.; von Lilienfeld O. A.; Roethlisberger U. Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties. J. Phys. Chem. Lett. 2017, 8, 1351–1359..

     Not in our library10.1021/acs.jpclett.7b00038 (opens in a new tab)

154. 154.

     Cersonsky R. K.; Helfrecht B.; Engel E. A.; Kliavinek S.; Ceriotti M. Improving Sample and Feature Selection with Principal Covariates Regression. Machine Learn.: Sci. Technol.2021,.

     Not in our library10.1088/2632-2153/abfe7c (opens in a new tab)

155. 155.

     de Jong S.; Kiers H. A. Principal Covariates Regression. Chemom. Intell. Lab. Syst. 1992, 14, 155–164..

     Not in our library10.1016/0169-7439(92)80100-i (opens in a new tab)

156. 156.

     Justo J. F.; Bazant M. Z.; Kaxiras E.; Bulatov V. V.; Yip S. Interatomic potential for silicon defects and disordered phases. Phys. Rev. B: Condens. Matter Mater. Phys. 1998, 58, 2539–2550..

     Not in our library10.1103/physrevb.58.2539 (opens in a new tab)

157. 157.

     Tersoff J. Empirical interatomic potential for silicon with improved elastic properties. Phys. Rev. B: Condens. Matter Mater. Phys. 1988, 38, 9902–9905..

     Not in our library10.1103/physrevb.38.9902 (opens in a new tab)

158. 158.

     Purja Pun G. P.; Mishin Y. Optimized interatomic potential for silicon and its application to thermal stability of silicene. Phys. Rev. B: Condens. Matter Mater. Phys. 2017, 95, 224103.

     Not in our library10.1103/physrevb.95.224103 (opens in a new tab)

159. 159.

     Lenosky T. J.; Sadigh B.; Alonso E.; Bulatov V. V.; Díaz de la Rubia T.; Kim J.; Voter A. F.; Kress J. D. Highly optimized empirical potential model of silicon. Modell. Simul. Mater. Sci. Eng. 2000, 8, 825–841..

     Not in our library10.1088/0965-0393/8/6/305 (opens in a new tab)

160. 160.

     Stillinger F. H.; Weber T. A. Computer simulation of local order in condensed phases of silicon. Phys. Rev. B: Condens. Matter Mater. Phys. 1985, 31, 5262–5271..

     Not in our library10.1103/physrevb.31.5262 (opens in a new tab)

161. 161.

     Buehler M. J.; Van Duin A. C. T.; William A Goddard I. Multiparadigm Modeling of Dynamical Crack Propagation in Silicon Using a Reactive Force Field. Phys. Rev. Lett. 2006, 96, 095505.

     Not in our library10.1103/physrevlett.96.095505 (opens in a new tab)

162. 162.

     Frauenheim T.; Weich F.; Kohler T.; Uhlman S.; Porezag D.; Seifert G. Density-functional-based construction of transferable nonorthogonal tight-binding potentials for Si and SiH. Phys. Rev. B: Condens. Matter Mater. Phys. 1995, 52, 11492–11501..

     Not in our library10.1103/physrevb.52.11492 (opens in a new tab)

163. 163.
     A general-purpose machine-learning force field for bulk and nanostructured phosphorus · 2020

     Not checked
164. 164.

     Deringer V. L.; Bernstein N.; Csányi G.; Ben Mahmoud C.; Ceriotti M.; Wilson M.; Drabold D. A.; Elliott S. R. Origins of Structural and Electronic Transitions in Disordered Silicon. Nature 2021, 589, 59–64..

     Not in our library10.1038/s41586-020-03072-z (opens in a new tab)

165. 165.

     Ruck M.; Hoppe D.; Wahl B.; Simon P.; Wang Y.; Seifert G. Fibrous Red Phosphorus. Angew. Chem., Int. Ed. 2005, 44, 7616–7619..

     Not in our library10.1002/anie.200503017 (opens in a new tab)

166. 166.

     Bachhuber F.; von Appen J.; Dronskowski R.; Schmidt P.; Nilges T.; Pfitzner A.; Weihrich R. The Extended Stability Range of Phosphorus Allotropes. Angew. Chem., Int. Ed. 2014, 53, 11629–11633..

     Not in our library10.1002/anie.201404147 (opens in a new tab)

167. 167.

     Liu H.; Neal A. T.; Zhu Z.; Luo Z.; Xu X.; Tománek D.; Ye P. D. Phosphorene: An Unexplored 2D Semiconductor with a High Hole Mobility. ACS Nano 2014, 8, 4033–4041..

     Not in our library10.1021/nn501226z (opens in a new tab)

168. 168.

     Watts M. C.; Picco L.; Russell-Pavier F. S.; Cullen P. L.; Miller T. S.; Bartuś S. P.; Payton O. D.; Skipper N. T.; Tileli V.; Howard C. A. Production of Phosphorene Nanoribbons. Nature 2019, 568, 216–220..

     Not in our library10.1038/s41586-019-1074-x (opens in a new tab)

169. 169.

     Jinnouchi R.; Karsai F.; Verdi C.; Asahi R.; Kresse G. Descriptors Representing Two- and Three-Body Atomic Distributions and Their Effects on the Accuracy of Machine-Learned Inter-Atomic Potentials. J. Chem. Phys. 2020, 152, 234102.

     Not in our library10.1063/5.0009491 (opens in a new tab)

170. 170.

     Morawietz T.; Sharma V.; Behler J. A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges. J. Chem. Phys. 2012, 136, 064103.

     Not in our library10.1063/1.3682557 (opens in a new tab)

171. 171.

     Morawietz T.; Behler J. A density-functional theory-based neural network potential for water clusters including van der Waals corrections. J. Phys. Chem. A2013, 117, 7356

     Not in our library10.1021/jp401225b (opens in a new tab)

172. 172.

     Veit M.; Jain S. K.; Bonakala S.; Rudra I.; Hohl D.; Csányi G. Equation of State of Fluid Methane from First Principles with Machine Learning Potentials. J. Chem. Theory Comput. 2019, 15, 2574–2586..

     Not in our library10.1021/acs.jctc.8b01242 (opens in a new tab)

173. 173.

     Babin V.; Leforestier C.; Paesani F. Development of a ”First Principles” Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient. J. Chem. Theory Comput. 2013, 9, 5395–5403..

     Not in our library10.1021/ct400863t (opens in a new tab)

174. 174.

     Bartók A. P.; Gillan M. J.; Manby F. R.; Csányi G. Machine-Learning Approach for One- and Two-Body Corrections to Density Functional Theory: Applications to Molecular and Condensed Water. Phys. Rev. B: Condens. Matter Mater. Phys. 2013, 88, 054104.

     Not in our library10.1103/physrevb.88.054104 (opens in a new tab)

175. 175.

     Wengert S.; Csányi G.; Reuter K.; Margraf J. T. Data-Efficient Machine Learning for Molecular Crystal Structure Prediction. Chem. Sci. 2021, 12, 4536–4546..

     Not in our library10.1039/d0sc05765g (opens in a new tab)

176. 176.
     Quantum chemical accuracy from density functional approximations via machine learning · 2020

     No notice
177. 177.

     Dral P. O.; Owens A.; Dral A.; Csányi G. Hierarchical Machine Learning of Potential Energy Surfaces. J. Chem. Phys. 2020, 152, 204110.

     Not in our library10.1063/5.0006498 (opens in a new tab)

178. 178.

     Sansone G.; Karttunen A. J.; Usvyat D.; Schütz M.; Brandenburg J. G.; Maschio L. On the Exfoliation and Anisotropic Thermal Expansion of Black Phosphorus. Chem. Commun. 2018, 54, 9793–9796..

     Not in our library10.1039/c8cc04855j (opens in a new tab)

179. 179.

     Chmiela S.; Tkatchenko A.; Sauceda H. E.; Poltavsky I.; Schütt K. T.; Müller K.-R. Machine Learning of Accurate Energy-Conserving Molecular Force Fields. Sci. Adv. 2017, 3, e1603015.

     Not in our library10.1126/sciadv.1603015 (opens in a new tab)

180. 180.
     Towards exact molecular dynamics simulations with machine-learned force fields · 2018

     No notice
181. 181.

     Chmiela S.; Sauceda H. E.; Poltavsky I.; Müller K.-R.; Tkatchenko A. sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning. Comput. Phys. Commun. 2019, 240, 38–45..

     Not in our library10.1016/j.cpc.2019.02.007 (opens in a new tab)

182. 182.

     Liu Y.-B.; Yang J.-Y.; Xin G.-M.; Liu L.-H.; Csányi G.; Cao B.-Y. Machine Learning Interatomic Potential Developed for Molecular Simulations on Thermal Properties of β-Ga2O3. J. Chem. Phys. 2020, 153, 144501.

     Not in our library10.1063/5.0027643 (opens in a new tab)

183. 183.

     Csányi G.; Albaret T.; Moras G.; Payne M. C.; De Vita A. Multiscale Hybrid Simulation Methods for Material Systems. J. Phys.: Condens. Matter 2005, 17, R691–R703..

     Not in our library10.1088/0953-8984/17/27/r02 (opens in a new tab)

184. 184.

     Partoens B.; Peeters F. M. From Graphene to Graphite: Electronic Structure around the K Point. Phys. Rev. B: Condens. Matter Mater. Phys. 2006, 74, 075404.

     Not in our library10.1103/physrevb.74.075404 (opens in a new tab)

185. 185.

     Morawietz T.; Singraber A.; Dellago C.; Behler J. How van Der Waals Interactions Determine the Unique Properties of Water. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, 8368–8373..

     Not in our library10.1073/pnas.1602375113 (opens in a new tab)

186. 186.

     Sivaraman G.; Guo J.; Ward L.; Hoyt N.; Williamson M.; Foster I.; Benmore C.; Jackson N. Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl. J. Phys. Chem. Lett. 2021, 12, 4278–4285..

     Not in our library10.1021/acs.jpclett.1c00901 (opens in a new tab)

187. 187.

     Yue S.; Muniz M. C.; Calegari Andrade M. F.; Zhang L.; Car R.; Panagiotopoulos A. Z. When do short-range atomistic machine-learning models fall short?. J. Chem. Phys. 2021, 154, 034111.

     Not in our library10.1063/5.0031215 (opens in a new tab)

188. 188.

     Medders G. R.; Babin V.; Paesani F. Development of a ”First-Principles” Water Potential with Flexible Monomers. III. Liquid Phase Properties. J. Chem. Theory Comput. 2014, 10, 2906–2910..

     Not in our library10.1021/ct5004115 (opens in a new tab)

189. 189.

     Himanen L.; Jäger M. O. J.; Morooka E. V.; Federici Canova F.; Ranawat Y. S.; Gao D. Z.; Rinke P.; Foster A. S. DScribe: Library of Descriptors for Machine Learning in Materials Science. Comput. Phys. Commun. 2020, 247, 106949.

     Not in our library10.1016/j.cpc.2019.106949 (opens in a new tab)

190. 190.

     Poelking C. SOAP++. http://github.com/capoe/benchml.

     Not in our library

191. 191.

     Caro M. A. Optimizing Many-Body Atomic Descriptors for Enhanced Computational Performance of Machine Learning Based Interatomic Potentials. Phys. Rev. B: Condens. Matter Mater. Phys. 2019, 100, 024112.

     Not in our library10.1103/physrevb.100.024112 (opens in a new tab)

192. 192.

     Morgan W. S.; Jorgensen J. J.; Hess B. C.; Hart G. L. W. Efficiency of Generalized Regular k-Point Grids. Comput. Mater. Sci. 2018, 153, 424–430..

     Not in our library10.1016/j.commatsci.2018.06.031 (opens in a new tab)

193. 193.

     Deringer V. L.; Bernstein N.; Bartók A. P.; Cliffe M. J.; Kerber R. N.; Marbella L. E.; Grey C. P.; Elliott S. R.; Csányi G. Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics. J. Phys. Chem. Lett. 2018, 9, 2879–2885..

     Not in our library10.1021/acs.jpclett.8b00902 (opens in a new tab)

194. 194.

     George J.; Hautier G.; Bartók A. P.; Csányi G.; Deringer V. L. Combining Phonon Accuracy with High Transferability in Gaussian Approximation Potential Models. J. Chem. Phys. 2020, 153, 044104.

     Not in our library10.1063/5.0013826 (opens in a new tab)

195. 195.
     Commentary: The Materials Project: A materials genome approach to accelerating materials innovation · 2013

     Not checked
196. 196.

     Babaei H.; Guo R.; Hashemi A.; Lee S. Machine-Learning-Based Interatomic Potential for Phonon Transport in Perfect Crystalline Si and Crystalline Si with Vacancies. Phys. Rev. Mater. 2019, 3, 074603.

     Not in our library10.1103/physrevmaterials.3.074603 (opens in a new tab)

197. 197.

     Togo A.; Tanaka I. First Principles Phonon Calculations in Materials Science. Scr. Mater. 2015, 108, 1–5..

     Not in our library10.1016/j.scriptamat.2015.07.021 (opens in a new tab)

198. 198.

     Rosenbrock C. W.; Gubaev K.; Shapeev A. V.; Partay L. B.; Bernstein N.; Csányi G.; Hart G. L. W. Machine-learned interatomic potentials for alloys and alloy phase diagrams. npj Comput. Mater. 2021, 7, 1–9..

     Not in our library10.1038/s41524-020-00477-2 (opens in a new tab)

199. 199.

     Tamura R.; Lin J.; Miyazaki T. Machine Learning Forces Trained by Gaussian Process in Liquid States: Transferability to Temperature and Pressure. J. Phys. Soc. Jpn. 2019, 88, 044601.

     Not in our library10.7566/jpsj.88.044601 (opens in a new tab)

200. 200.

     Caro M. A.; Csányi G.; Laurila T.; Deringer V. L. Machine Learning Driven Simulated Deposition of Carbon Films: From Low-Density to Diamondlike Amorphous Carbon. Phys. Rev. B: Condens. Matter Mater. Phys. 2020, 102, 174201.

     Not in our library10.1103/physrevb.102.174201 (opens in a new tab)

201. 201.

     Tran K.; Neiswanger W.; Yoon J.; Zhang Q.; Xing E.; Ulissi Z. W. Methods for Comparing Uncertainty Quantifications for Material Property Predictions. Mach. Learn.: Sci. Technol. 2020, 1, 025006.

     Not in our library10.1088/2632-2153/ab7e1a (opens in a new tab)

202. 202.

     Musil F.; Willatt M. J.; Langovoy M. A.; Ceriotti M. Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. J. Chem. Theory Comput. 2019, 15, 906–915..

     Not in our library10.1021/acs.jctc.8b00959 (opens in a new tab)

203. 203.

     Raimbault N.; Grisafi A.; Ceriotti M.; Rossi M. Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals. New J. Phys. 2019, 21, 105001.

     Not in our library10.1088/1367-2630/ab4509 (opens in a new tab)

204. 204.

     Imbalzano G.; Zhuang Y.; Kapil V.; Rossi K.; Engel E. A.; Grasselli F.; Ceriotti M. Uncertainty Estimation by Committee Models for Molecular Dynamics and Thermodynamic Averages. J. Chem. Phys. 2021, 154, 074102.

     Not in our library10.1063/5.0036522 (opens in a new tab)

205. 205.

     Tibshirani R. A comparison of some error estimates for neural network models. Neural Comput. 1996, 8, 152.

     Not in our library10.1162/neco.1996.8.1.152 (opens in a new tab)

206. 206.

     Janet J. P.; Duan C.; Yang T.; Nandy A.; Kulik H. J. A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery. Chem. Sci. 2019, 10, 7913–7922..

     Not in our library10.1039/c9sc02298h (opens in a new tab)

207. 207.

     Novikov I. S.; Shapeev A. V. Improving Accuracy of Interatomic Potentials: More Physics or More Data? A Case Study of Silica. Materials Today Communications 2019, 18, 74–80..

     Not in our library10.1016/j.mtcomm.2018.11.008 (opens in a new tab)

208. 208.

     Peterson A. A.; Christensen R.; Khorshidi A. Addressing Uncertainty in Atomistic Machine Learning. Phys. Chem. Chem. Phys. 2017, 19, 10978–10985..

     Not in our library10.1039/c7cp00375g (opens in a new tab)

209. 209.

     Breiman L. Bagging Predictors. Mach. Learn. 1996, 24, 123.

     Not in our library10.1007/bf00058655 (opens in a new tab)

210. 210.

     Politis D. N.; Romano J. P. Large Sample Confidence Regions Based on Subsamples under Minimal Assumptions. Ann. Stat. 1994, 22, 2031–2050..

     Not in our library10.1214/aos/1176325770 (opens in a new tab)

211. 211.

     Heskes T. Practical Confidence and Prediction Intervals. Adv. Neural Inf. Process. Syst. 1997, 176–182.

     Not in our library

212. 212.

     Ben Mahmoud C.; Anelli A.; Csányi G.; Ceriotti M. Learning the Electronic Density of States in Condensed Matter. Phys. Rev. B: Condens. Matter Mater. Phys. 2020, 102, 235130.

     Not in our library10.1103/physrevb.102.235130 (opens in a new tab)

213. 213.

     Torrie G. M.; Valleau J. P. Nonphysical Sampling Distributions in Monte Carlo Free-Energy Estimation: Umbrella Sampling. J. Comput. Phys. 1977, 23, 187–199..

     Not in our library10.1016/0021-9991(77)90121-8 (opens in a new tab)

214. 214.

     Ceriotti M.; Brain G. A.; Riordan O.; Manolopoulos D. E. The Inefficiency of Re-Weighted Sampling and the Curse of System Size in High-Order Path Integration. Proc. R. Soc. London, Ser. A 2012, 468, 2–17..

     Not in our library10.1098/rspa.2011.0413 (opens in a new tab)

215. 215.

     Cui J.; Krems R. V. Efficient Non-Parametric Fitting of Potential Energy Surfaces for Polyatomic Molecules with Gaussian Processes. J. Phys. B: At., Mol. Opt. Phys. 2016, 49, 224001.

     Not in our library10.1088/0953-4075/49/22/224001 (opens in a new tab)

216. 216.

     Cresswell A. J.; Wheatley R. J.; Wilkinson R. D.; Graham R. S. Molecular Simulation of the Thermophysical Properties and Phase Behaviour of Impure CO2 Relevant to CCS. Faraday Discuss. 2016, 192, 415–436..

     Not in our library10.1039/c6fd00026f (opens in a new tab)

217. 217.

     Kolb B.; Marshall P.; Zhao B.; Jiang B.; Guo H. Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes. J. Phys. Chem. A 2017, 121, 2552–2557..

     Not in our library10.1021/acs.jpca.7b01182 (opens in a new tab)

218. 218.

     Uteva E.; Graham R. S.; Wilkinson R. D.; Wheatley R. J. Interpolation of Intermolecular Potentials Using Gaussian Processes. J. Chem. Phys. 2017, 147, 161706.

     Not in our library10.1063/1.4986489 (opens in a new tab)

219. 219.

     Dral P. O.; Owens A.; Yurchenko S. N.; Thiel W. Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels. J. Chem. Phys. 2017, 146, 244108.

     Not in our library10.1063/1.4989536 (opens in a new tab)

220. 220.

     Qu C.; Yu Q.; Van Hoozen B. L.; Bowman J. M.; Vargas-Hernández R. A. Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches To Represent High-Dimensional Potential Energy Surfaces. J. Chem. Theory Comput. 2018, 14, 3381–3396..

     Not in our library10.1021/acs.jctc.8b00298 (opens in a new tab)

221. 221.

     Käser S.; Koner D.; Christensen A. S.; von Lilienfeld O. A.; Meuwly M. Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet. J. Phys. Chem. A 2020, 124, 8853–8865..

     Not in our library10.1021/acs.jpca.0c05979 (opens in a new tab)

222. 222.

     Sugisawa H.; Ida T.; Krems R. V. Gaussian Process Model of 51-Dimensional Potential Energy Surface for Protonated Imidazole Dimer. J. Chem. Phys. 2020, 153, 114101.

     Not in our library10.1063/5.0023492 (opens in a new tab)

223. 223.

     Bowman G. R.; Beauchamp K. A.; Boxer G.; Pande V. S. Progress and Challenges in the Automated Construction of Markov State Models for Full Protein Systems. J. Chem. Phys. 2009, 131, 124101.

     Not in our library10.1063/1.3216567 (opens in a new tab)

224. 224.

     Babin V.; Medders G. R.; Paesani F. Development of a ”First Principles” Water Potential with Flexible Monomers. II: Trimer Potential Energy Surface, Third Virial Coefficient, and Small Clusters. J. Chem. Theory Comput. 2014, 10, 1599–1607..

     Not in our library10.1021/ct500079y (opens in a new tab)

225. 225.

     Cisneros G. A.; Wikfeldt K. T.; Ojamäe L.; Lu J.; Xu Y.; Torabifard H.; Bartók A. P.; Csányi G.; Molinero V.; Paesani F. Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions. Chem. Rev. 2016, 116, 7501–7528..

     Not in our library10.1021/acs.chemrev.5b00644 (opens in a new tab)

226. 226.

     Manzhos S.; Carrington T. Neural Network Potential Energy Surfaces for Small Molecules and Reactions. Chem. Rev.2020, PMID: 33021368.

     Not in our library10.1021/acs.chemrev.0c00665 (opens in a new tab)

227. 227.

     Schmitz G.; Godtliebsen I. H.; Christiansen O. Machine Learning for Potential Energy Surfaces: An Extensive Database and Assessment of Methods. J. Chem. Phys. 2019, 150, 244113.

     Not in our library10.1063/1.5100141 (opens in a new tab)

228. 228.

     Krems R. V. Bayesian Machine Learning for Quantum Molecular Dynamics. Phys. Chem. Chem. Phys. 2019, 21, 13392–13410..

     Not in our library10.1039/c9cp01883b (opens in a new tab)

229. 229.

     Schmitz G.; Christiansen O. Gaussian Process Regression to Accelerate Geometry Optimizations Relying on Numerical Differentiation. J. Chem. Phys. 2018, 148, 241704.

     Not in our library10.1063/1.5009347 (opens in a new tab)

230. 230.

     Proppe J.; Gugler S.; Reiher M. Gaussian Process-Based Refinement of Dispersion Corrections. J. Chem. Theory Comput. 2019, 15, 6046–6060..

     Not in our library10.1021/acs.jctc.9b00627 (opens in a new tab)

231. 231.

     Unke O. T.; Chmiela S.; Sauceda H. E.; Gastegger M.; Poltavsky I.; Schütt K. T.; Tkatchenko A.; Mueller K.-R. Chem. Rev.2021,.

     Not in our library10.1021/acs.chemrev.0c01111 (opens in a new tab)

232. 232.

     Ramakrishnan R.; Dral P. O.; Rupp M.; Von Lilienfeld O. A. Quantum Chemistry Structures and Properties of 134 Kilo Molecules. Sci. Data 2014, 1, 1–7..

     Not in our library10.1038/sdata.2014.22 (opens in a new tab)

233. 233.

     Faber F. A.; Christensen A. S.; Huang B.; Anatole von Lilienfeld O. Alchemical and Structural Distribution Based Representation for Universal Quantum Machine Learning. J. Chem. Phys. 2018, 148, 241717.

     Not in our library10.1063/1.5020710 (opens in a new tab)

234. 234.

     Huang B.; Anatole von Lilienfeld O. Communication: Understanding Molecular Representations in Machine Learning: The Role of Uniqueness and Target Similarity. J. Chem. Phys. 2016, 145, 161102.

     Not in our library10.1063/1.4964627 (opens in a new tab)

235. 235.

     Rupp M.; Tkatchenko A.; Müller K.-R.; von Lilienfeld O. A. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Phys. Rev. Lett. 2012, 108, 058301.

     Not in our library10.1103/physrevlett.108.058301 (opens in a new tab)

236. 236.

     Hansen K.; Biegler F.; Ramakrishnan R.; Pronobis W.; Anatole von Lilienfeld O.; Müller K. R.; Tkatchenko A. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space. J. Phys. Chem. Lett. 2015, 6, 2326–2331..

     Not in our library10.1021/acs.jpclett.5b00831 (opens in a new tab)

237. 237.

     Schütt K. T.; Sauceda H. E.; Kindermans P.-J.; Tkatchenko A.; Müller K.-R. SchNet - A Deep Learning Architecture for Molecules and Materials. J. Chem. Phys. 2018, 148, 241722.

     Not in our library10.1063/1.5019779 (opens in a new tab)

238. 238.

     Unke O. T.; Meuwly M. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. J. Chem. Theory Comput. 2019, 15, 3678–3693..

     Not in our library10.1021/acs.jctc.9b00181 (opens in a new tab)

239. 239.

     Zaverkin V.; Kästner J. Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials. J. Chem. Theory Comput. 2020, 16, 5410–5421..

     Not in our library10.1021/acs.jctc.0c00347 (opens in a new tab)

240. 240.
     Machine learning in chemical reaction space · 2020

     Not checked
241. 241.

     Dragoni D.; Daff T. D.; Csányi G.; Marzari N. Achieving DFT Accuracy with a Machine-Learning Interatomic Potential: Thermomechanics and Defects in Bcc Ferromagnetic Iron. Phys. Rev. Mater. 2018, 2, 013808.

     Not in our library10.1103/physrevmaterials.2.013808 (opens in a new tab)

242. 242.

     Maresca F.; Dragoni D.; Csányi G.; Marzari N.; Curtin W. A. Screw Dislocation Structure and Mobility in Body Centered Cubic Fe Predicted by a Gaussian Approximation Potential. npj Comput. Mater. 2018, 4, 69.

     Not in our library10.1038/s41524-018-0125-4 (opens in a new tab)

243. 243.

     Kostiuchenko T.; Körmann F.; Neugebauer J.; Shapeev A. Impact of Lattice Relaxations on Phase Transitions in a High-Entropy Alloy Studied by Machine-Learning Potentials. npj Comput. Mater. 2019, 5, 55.

     Not in our library10.1038/s41524-019-0195-y (opens in a new tab)

244. 244.

     Ismail-Beigi S.; Arias T. A. Ab InitioStudy of Screw Dislocations in Mo and Ta: A New Picture of Plasticity in bcc Transition Metals. Phys. Rev. Lett. 2000, 84, 1499–1502..

     Not in our library10.1103/physrevlett.84.1499 (opens in a new tab)

245. 245.

     Cereceda D.; Stukowski A.; Gilbert M. R.; Queyreau S.; Ventelon L.; Marinica M. C.; Perlado J. M.; Marian J. Assessment of interatomic potentials for atomistic analysis of static and dynamic properties of screw dislocations in W. J. Phys.: Condens. Matter 2013, 25, 085702.

     Not in our library10.1088/0953-8984/25/8/085702 (opens in a new tab)

246. 246.

     Lambert H.; Fekete A.; Kermode J. R.; Vita A. D. Imeall: A Computational Framework for the Calculation of the Atomistic Properties of Grain Boundaries. Comput. Phys. Commun. 2018, 232, 256–263..

     Not in our library10.1016/j.cpc.2018.04.029 (opens in a new tab)

247. 247.

     Novikov I.; Grabowski B.; Kormann F.; Shapeev A. Machine-learning interatomic potentials reproduce vibrational and magnetic degrees of freedom. 2020; arxiv.org/abs/2012.12763.

     Not in our library

248. 248.

     Zhang Z.; Csányi G.; Alfè D. Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials. Geochim. Cosmochim. Acta 2020, 291, 5–18..

     Not in our library10.1016/j.gca.2020.03.028 (opens in a new tab)

249. 249.

     Rowe P.; Csányi G.; Alfè D.; Michaelides A. Development of a Machine Learning Potential for Graphene. Phys. Rev. B: Condens. Matter Mater. Phys. 2018, 97, 054303.

     Not in our library10.1103/physrevb.97.054303 (opens in a new tab)

250. 250.

     Qian C.; McLean B.; Hedman D.; Ding F. A Comprehensive Assessment of Empirical Potentials for Carbon Materials. APL Mater. 2021, 9, 061102.

     Not in our library10.1063/5.0052870 (opens in a new tab)

251. 251.

     Thiemann F. L.; Rowe P.; Müller E. A.; Michaelides A. Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling. J. Phys. Chem. C 2020, 124, 22278–22290..

     Not in our library10.1021/acs.jpcc.0c05831 (opens in a new tab)

252. 252.

     Hoffmann R.; Kabanov A. A.; Golov A. A.; Proserpio D. M. Homo Citans and Carbon Allotropes: For an Ethics of Citation. Angew. Chem., Int. Ed. 2016, 55, 10962–10976..

     Not in our library10.1002/anie.201600655 (opens in a new tab)

253. 253.

     Pickard C. J.; Needs R. J. Hypothetical Low-Energy Chiral Framework Structure of Group 14 Elements. Phys. Rev. B: Condens. Matter Mater. Phys. 2010, 81, 014106.

     Not in our library10.1103/physrevb.81.014106 (opens in a new tab)

254. 254.

     Albert B.; Hillebrecht H. Boron: Elementary Challenge for Experimenters and Theoreticians. Angew. Chem., Int. Ed. 2009, 48, 8640–8668..

     Not in our library10.1002/anie.200903246 (opens in a new tab)

255. 255.

     Ogitsu T.; Schwegler E.; Galli G. β-Rhombohedral Boron: At the Crossroads of the Chemistry of Boron and the Physics of Frustration. Chem. Rev. 2013, 113, 3425–3449..

     Not in our library10.1021/cr300356t (opens in a new tab)

256. 256.

     Yang Q.; Lv J.; Tong Q.; Du X.; Wang Y.; Zhang S.; Yang G.; Bergara A.; Ma Y. Hard and Superconducting Cubic Boron Phase via Swarm-Intelligence Structural Prediction Driven by a Machine-Learning Potential. Phys. Rev. B: Condens. Matter Mater. Phys. 2021, 103, 024505.

     Not in our library10.1103/physrevb.103.024505 (opens in a new tab)

257. 257.

     Böcker S.; Häser M. Covalent Structures of Phosphorus: A Comprehensive Theoretical Study. Z. Anorg. Allg. Chem. 1995, 621, 258–286..

     Not in our library10.1002/zaac.19956210215 (opens in a new tab)

258. 258.

     Pfitzner A.; Bräu M. F.; Zweck J.; Brunklaus G.; Eckert H. Phosphorus Nanorods-Two Allotropic Modifications of a Long-Known Element. Angew. Chem., Int. Ed. 2004, 43, 4228–4231..

     Not in our library10.1002/anie.200460244 (opens in a new tab)

259. 259.

     Ahnert S. E.; Grant W. P.; Pickard C. J. Revealing and Exploiting Hierarchical Material Structure Through Complex Atomic Networks. npj Comput. Mater. 2017, 3, 1–8..

     Not in our library10.1038/s41524-017-0035-x (opens in a new tab)

260. 260.

     Deringer V. L.; Pickard C. J.; Proserpio D. M. Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration. Angew. Chem., Int. Ed. 2020, 59, 15880–15885..

     Not in our library10.1002/anie.202005031 (opens in a new tab)

261. 261.

     Schusteritsch G.; Uhrin M.; Pickard C. J. Single-Layered Hittorf’s Phosphorus: A Wide-Bandgap High Mobility 2D Material. Nano Lett. 2016, 16, 2975–2980..

     Not in our library10.1021/acs.nanolett.5b05068 (opens in a new tab)

262. 262.

     Zhang L.; Huang H.; Zhang B.; Gu M.; Zhao D.; Zhao X.; Li L.; Zhou J.; Wu K.; Cheng Y.; Zhang J. Structure and Properties of Violet Phosphorus and Its Phosphorene Exfoliation. Angew. Chem., Int. Ed. 2020, 59, 1074–1080..

     Not in our library10.1002/anie.201912761 (opens in a new tab)

263. 263.

     Caro M. A.; Deringer V. L.; Koskinen J.; Laurila T.; Csányi G. Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon. Phys. Rev. Lett. 2018, 120, 166101.

     Not in our library10.1103/physrevlett.120.166101 (opens in a new tab)

264. 264.

     Robertson J. Diamond-Like Amorphous Carbon. Mater. Sci. Eng., R 2002, 37, 129–281..

     Not in our library10.1016/s0927-796x(02)00005-0 (opens in a new tab)

265. 265.

     Shiell T. B.; McCulloch D. G.; McKenzie D. R.; Field M. R.; Haberl B.; Boehler R.; Cook B. A.; de Tomas C.; Suarez-Martinez I.; Marks N. A.; Bradby J. E. Graphitization of Glassy Carbon after Compression at Room Temperature. Phys. Rev. Lett. 2018, 120, 215701.

     Not in our library10.1103/physrevlett.120.215701 (opens in a new tab)

266. 266.

     Marks N. A. Generalizing the Environment-Dependent Interaction Potential for Carbon. Phys. Rev. B: Condens. Matter Mater. Phys. 2000, 63, 035401.

     Not in our library10.1103/physrevb.63.035401 (opens in a new tab)

267. 267.

     Marks N. Thin Film Deposition of Tetrahedral Amorphous Carbon: A Molecular Dynamics Study. Diamond Relat. Mater. 2005, 14, 1223–1231..

     Not in our library10.1016/j.diamond.2004.10.047 (opens in a new tab)

268. 268.

     Jana R.; Savio D.; Deringer V. L.; Pastewka L. Structural and Elastic Properties of Amorphous Carbon from Simulated Quenching at Low Rates. Modell. Simul. Mater. Sci. Eng. 2019, 27, 085009.

     Not in our library10.1088/1361-651x/ab45da (opens in a new tab)

269. 269.

     Palomäki T.; Caro M. A.; Wester N.; Sainio S.; Etula J.; Johansson L.-S.; Han J. G.; Koskinen J.; Laurila T. Effect of Power Density on the Electrochemical Properties of Undoped Amorphous Carbon (a-C) Thin Films. Electroanalysis 2019, 31, 746–755..

     Not in our library10.1002/elan.201800738 (opens in a new tab)

270. 270.

     Jana R.; von Lautz J.; Khosrownejad S. M.; Andrews W. B.; Moseler M.; Pastewka L. Constitutive Relations for Plasticity of Amorphous Carbon. J. Phys. Mater. 2020, 3, 035005.

     Not in our library10.1088/2515-7639/ab953c (opens in a new tab)

271. 271.

     Powles R. C.; Marks N. A.; Lau D. W. M. Self-Assembly of sp2-Bonded Carbon Nanostructures from Amorphous Precursors. Phys. Rev. B: Condens. Matter Mater. Phys. 2009, 79, 075430.

     Not in our library10.1103/physrevb.79.075430 (opens in a new tab)

272. 272.

     de Tomas C.; Aghajamali A.; Jones J. L.; Lim D. J.; López M. J.; Suarez-Martinez I.; Marks N. A. Transferability in Interatomic Potentials for Carbon. Carbon 2019, 155, 624–634..

     Not in our library10.1016/j.carbon.2019.07.074 (opens in a new tab)

273. 273.

     Deringer V. L.; Merlet C.; Hu Y.; Lee T. H.; Kattirtzi J. A.; Pecher O.; Csányi G.; Elliott S. R.; Grey C. P. Towards an Atomistic Understanding of Disordered Carbon Electrode Materials. Chem. Commun. 2018, 54, 5988–5991..

     Not in our library10.1039/c8cc01388h (opens in a new tab)

274. 274.

     Huang J.-X.; Csányi G.; Zhao J.-B.; Cheng J.; Deringer V. L. First-Principles Study of Alkali-Metal Intercalation in Disordered Carbon Anode Materials. J. Mater. Chem. A 2019, 7, 19070–19080..

     Not in our library10.1039/c9ta05453g (opens in a new tab)

275. 275.

     Deringer V. L.; Caro M. A.; Jana R.; Aarva A.; Elliott S. R.; Laurila T.; Csányi G.; Pastewka L. Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory. Chem. Mater. 2018, 30, 7438–7445..

     Not in our library10.1021/acs.chemmater.8b02410 (opens in a new tab)

276. 276.

     Wooten F.; Winer K.; Weaire D. Computer Generation of Structural Models of Amorphous Si and Ge. Phys. Rev. Lett. 1985, 54, 1392–1395..

     Not in our library10.1103/physrevlett.54.1392 (opens in a new tab)

277. 277.

     Barkema G. T.; Mousseau N. High-Quality Continuous Random Networks. Phys. Rev. B: Condens. Matter Mater. Phys. 2000, 62, 4985–4990..

     Not in our library10.1103/physrevb.62.4985 (opens in a new tab)

278. 278.

     Pantelides S. T. Defects in Amorphous Silicon: A New Perspective. Phys. Rev. Lett. 1986, 57, 2979–2982..

     Not in our library10.1103/physrevlett.57.2979 (opens in a new tab)

279. 279.

     Pham T. L.; Kino H.; Terakura K.; Miyake T.; Dam H. C. Novel Mixture Model for the Representation of Potential Energy Surfaces. J. Chem. Phys. 2016, 145, 154103.

     Not in our library10.1063/1.4964318 (opens in a new tab)

280. 280.

     Pedersen A.; Pizzagalli L.; Jónsson H. Optimal Atomic Structure of Amorphous Silicon Obtained from Density Functional Theory Calculations. New J. Phys. 2017, 19, 063018.

     Not in our library10.1088/1367-2630/aa732e (opens in a new tab)

281. 281.

     Li W.; Ando Y. Dependence of a Cooling rate on Structural and Vibrational Properties of Amorphous Silicon: A Neural Network Potential-Based Molecular Dynamics Study. J. Chem. Phys. 2019, 151, 114101.

     Not in our library10.1063/1.5114652 (opens in a new tab)

282. 282.

     Eckhoff M.; Behler J. From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5. J. Chem. Theory Comput. 2019, 15, 3793–3809..

     Not in our library10.1021/acs.jctc.8b01288 (opens in a new tab)

283. 283.

     Caro M. A.; Aarva A.; Deringer V. L.; Csányi G.; Laurila T. Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning. Chem. Mater. 2018, 30, 7446–7455..

     Not in our library10.1021/acs.chemmater.8b03353 (opens in a new tab)

284. 284.
     Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores · 2020

     Not checked
285. 285.

     Mocanu F. C.; Konstantinou K.; Lee T. H.; Bernstein N.; Deringer V. L.; Csányi G.; Elliott S. R. Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential. J. Phys. Chem. B 2018, 122, 8998–9006..

     Not in our library10.1021/acs.jpcb.8b06476 (opens in a new tab)

286. 286.

     Konstantinou K.; Mocanu F. C.; Lee T.-H.; Elliott S. R. Revealing the Intrinsic Nature of the Mid-Gap Defects in Amorphous Ge2Sb2Te5. Nat. Commun. 2019, 10, 3065.

     Not in our library10.1038/s41467-019-10980-w (opens in a new tab)

287. 287.

     Wuttig M.; Yamada N. Phase-Change Materials for Rewriteable Data Storage. Nat. Mater. 2007, 6, 824–832..

     Not in our library10.1038/nmat2009 (opens in a new tab)

288. 288.

     Wuttig M.; Bhaskaran H.; Taubner T. Phase-Change Materials for Non-Volatile Photonic Applications. Nat. Photonics 2017, 11, 465–476..

     Not in our library10.1038/nphoton.2017.126 (opens in a new tab)

289. 289.

     Akola J.; Jones R. O. Amorphous Structures of Ge/Sb/Te Alloys: Density Functional Simulations. Phys. Status Solidi B 2012, 249, 1851–1860..

     Not in our library10.1002/pssb.201200393 (opens in a new tab)

290. 290.

     Zhang W.; Deringer V. L.; Dronskowski R.; Mazzarello R.; Ma E.; Wuttig M. Density-Functional Theory Guided Advances in Phase-Change Materials and Memories. MRS Bull. 2015, 40, 856–869..

     Not in our library10.1557/mrs.2015.227 (opens in a new tab)

291. 291.

     Ronneberger I.; Zhang W.; Mazzarello R. Crystal Growth of Ge2Sb2Te5 at High Temperatures. MRS Commun. 2018, 8, 1018–1023..

     Not in our library10.1557/mrc.2018.131 (opens in a new tab)

292. 292.

     Kalikka J.; Akola J.; Jones R. O. Crystallization Processes in the Phase Change Material Ge2Sb2Te5: Unbiased Density Functional/Molecular Dynamics Simulations. Phys. Rev. B: Condens. Matter Mater. Phys. 2016, 94, 134105.

     Not in our library10.1103/physrevb.94.134105 (opens in a new tab)

293. 293.

     Sosso G. C.; Miceli G.; Caravati S.; Behler J.; Bernasconi M. Neural Network Interatomic Potential for the Phase Change Material GeTe. Phys. Rev. B: Condens. Matter Mater. Phys. 2012, 85, 174103.

     Not in our library10.1103/physrevb.85.174103 (opens in a new tab)

294. 294.

     Sosso G. C.; Donadio D.; Caravati S.; Behler J.; Bernasconi M. Thermal transport in phase-change materials from atomistic simulations. Phys. Rev. B: Condens. Matter Mater. Phys. 2012, 86, 104301.

     Not in our library10.1103/physrevb.86.104301 (opens in a new tab)

295. 295.

     Sosso G. C.; Miceli G.; Caravati S.; Giberti F.; Behler J.; Bernasconi M. Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations. J. Phys. Chem. Lett. 2013, 4, 4241–4246..

     Not in our library10.1021/jz402268v (opens in a new tab)

296. 296.

     Gabardi S.; Baldi E.; Bosoni E.; Campi D.; Caravati S.; Sosso G. C.; Behler J.; Bernasconi M. Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires. J. Phys. Chem. C 2017, 121, 23827–23838..

     Not in our library10.1021/acs.jpcc.7b09862 (opens in a new tab)

297. 297.

     Sosso G. C.; Bernasconi M. Harnessing Machine Learning Potentials to Understand the Functional Properties of Phase-Change Materials. MRS Bull. 2019, 44, 705–709..

     Not in our library10.1557/mrs.2019.202 (opens in a new tab)

298. 298.

     Mocanu F. C.; Konstantinou K.; Elliott S. R. Quench-Rate and Size-Dependent Behaviour in Glassy Ge2Sb2Te5 Models Simulated with a Machine-Learned Gaussian Approximation Potential. J. Phys. D: Appl. Phys. 2020, 53, 244002.

     Not in our library10.1088/1361-6463/ab77de (opens in a new tab)

299. 299.

     Zhou Y.-X.; Zhang H.-Y.; Deringer V. L.; Zhang W. Structure and Dynamics of Supercooled Liquid Ge2Sb2Te5 from Machine-Learning-Driven Simulations. Phys. Status Solidi RRL 2021, 15, 2000403.

     Not in our library10.1002/pssr.202000403 (opens in a new tab)

300. 300.

     Schumacher M.; Weber H.; Jóvári P.; Tsuchiya Y.; Youngs T. G. A.; Kaban I.; Mazzarello R. Structural, Electronic and Kinetic Properties of the Phase-Change Material Ge2Sb2Te5 in the Liquid State. Sci. Rep. 2016, 6, 27434.

     Not in our library10.1038/srep27434 (opens in a new tab)

301. 301.

     Konstantinou K.; Mavračić J.; Mocanu F. C.; Elliott S. R. Simulation of Phase-Change-Memory and Thermoelectric Materials using Machine-Learned Interatomic Potentials: Sb2Te3. Phys. Status Solidi B2020,.

     Not in our library10.1002/pssb.202000416 (opens in a new tab)

302. 302.

     Dangić D.; Hellman O.; Fahy S.; Savić I. The Origin of the Lattice Thermal Conductivity Enhancement at the Ferroelectric Phase Transition in GeTe. npj Comput. Mater. 2021, 7, 57.

     Not in our library10.1038/s41524-021-00523-7 (opens in a new tab)

303. 303.

     Brommer K. D.; Needels M.; Larson B.; Joannopoulos J. D. Ab Initio Theory of the Si(111)-(7 × 7) Surface Reconstruction: A Challenge for Massively Parallel Computation. Phys. Rev. Lett. 1992, 68, 1355–1358..

     Not in our library10.1103/physrevlett.68.1355 (opens in a new tab)

304. 304.

     Laurila T.; Sainio S.; Caro M. A. Hybrid Carbon Based Nanomaterials for Electrochemical Detection of Biomolecules. Prog. Mater. Sci. 2017, 88, 499–594..

     Not in our library10.1016/j.pmatsci.2017.04.012 (opens in a new tab)

305. 305.

     Aarva A.; Deringer V. L.; Sainio S.; Laurila T.; Caro M. A. Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra. Chem. Mater. 2019, 31, 9243–9255..

     Not in our library10.1021/acs.chemmater.9b02049 (opens in a new tab)

306. 306.

     Aarva A.; Deringer V. L.; Sainio S.; Laurila T.; Caro M. A. Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra. Chem. Mater. 2019, 31, 9256–9267..

     Not in our library10.1021/acs.chemmater.9b02050 (opens in a new tab)

307. 307.

     Peltola E.; Aarva A.; Sainio S.; Heikkinen J. J.; Wester N.; Jokinen V.; Koskinen J.; Laurila T. Biofouling Affects the Redox Kinetics of Outer and Inner Sphere Probes on Carbon Surfaces Drastically Differently - Implications to Biosensing. Phys. Chem. Chem. Phys. 2020, 22, 16630–16640..

     Not in our library10.1039/d0cp02251a (opens in a new tab)

308. 308.

     Timmermann J.; Kraushofer F.; Resch N.; Li P.; Wang Y.; Mao Z.; Riva M.; Lee Y.; Staacke C.; Schmid M.; Scheurer C.; Parkinson G. S.; Diebold U.; Reuter K. IrO2 Surface Complexions Identified through Machine Learning and Surface Investigations. Phys. Rev. Lett. 2020, 125, 206101.

     Not in our library10.1103/physrevlett.125.206101 (opens in a new tab)

309. 309.

     Reuter K.; Scheffler M. Composition, Structure, and Stability of RuO2(110) as a Function of Oxygen Pressure. Phys. Rev. B: Condens. Matter Mater. Phys. 2001, 65, 035406.

     Not in our library10.1103/physrevb.65.035406 (opens in a new tab)

310. 310.

     Hu L.; Huang B.; Liu F. Atomistic Mechanism Underlying the Si(111)-(7 × 7) Surface Reconstruction Revealed by Artificial Neural-Network Potential. Phys. Rev. Lett. 2021, 126, 176101.

     Not in our library10.1103/physrevlett.126.176101 (opens in a new tab)

311. 311.

     Elias J. S.; Artrith N.; Bugnet M.; Giordano L.; Botton G. A.; Kolpak A. M.; Shao-Horn Y. Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation. ACS Catal. 2016, 6, 1675–1679..

     Not in our library10.1021/acscatal.5b02666 (opens in a new tab)

312. 312.

     Wang Y.; Ding J.; Fan Z.; Tian L.; Li M.; Lu H.; Zhang Y.; Ma E.; Li J.; Shan Z. Tension-Compression Asymmetry in Amorphous Silicon. Nat. Mater.2021,.

     Not in our library10.1038/s41563-021-01017-z (opens in a new tab)

313. 313.

     Hegedüs J.; Elliott S. R. Microscopic Origin of the Fast Crystallization Ability of Ge-Sb-Te Phase-Change Memory Materials. Nat. Mater. 2008, 7, 399–405..

     Not in our library10.1038/nmat2157 (opens in a new tab)

314. 314.

     Sosso G. C.; Deringer V. L.; Elliott S. R.; Csányi G. Understanding the Thermal Properties of Amorphous Solids Using Machine-Learning-Based Interatomic Potentials. Mol. Simul. 2018, 44, 866–880..

     Not in our library10.1080/08927022.2018.1447107 (opens in a new tab)

315. 315.

     Qian X.; Yang R. Temperature Effect on the Phonon Dispersion Stability of Zirconium by Machine Learning Driven Atomistic Simulations. Phys. Rev. B: Condens. Matter Mater. Phys. 2018, 98, 224108.

     Not in our library10.1103/physrevb.98.224108 (opens in a new tab)

316. 316.

     Qian X.; Peng S.; Li X.; Wei Y.; Yang R. Thermal Conductivity Modeling Using Machine Learning Potentials: Application to Crystalline and Amorphous Silicon. Mater. Today Physics 2019, 10, 100140.

     Not in our library10.1016/j.mtphys.2019.100140 (opens in a new tab)

317. 317.

     Zhang C.; Sun Q. Gaussian Approximation Potential for Studying the Thermal Conductivity of Silicene. J. Appl. Phys. 2019, 126, 105103.

     Not in our library10.1063/1.5119281 (opens in a new tab)

318. 318.

     Byggmästar J.; Hamedani A.; Nordlund K.; Djurabekova F. Machine-Learning Interatomic Potential for Radiation Damage and Defects in Tungsten. Phys. Rev. B: Condens. Matter Mater. Phys. 2019, 100, 144105.

     Not in our library10.1103/physrevb.100.144105 (opens in a new tab)

319. 319.
     Insights into the primary radiation damage of silicon by a machine learning interatomic potential · 2020

     Not checked
320. 320.

     Byggmästar J.; Nordlund K.; Djurabekova F. Gaussian approximation potentials for body-centered-cubic transition metals. Phys. Rev. Materials 2020, 4, 093802.

     Not in our library10.1103/physrevmaterials.4.093802 (opens in a new tab)

321. 321.

     Tersoff J. New Empirical Approach for the Structure and Energy of Covalent Systems. Phys. Rev. B: Condens. Matter Mater. Phys. 1988, 37, 6991–7000..

     Not in our library10.1103/physrevb.37.6991 (opens in a new tab)

322. 322.

     Domínguez-Gutiérrez F. J.; Byggmästar J.; Nordlund K.; Djurabekova F.; von Toussaint U. Computational study of crystal defect formation in Mo by a machine learning molecular dynamics potential. Modell. Simul. Mater. Sci. Eng. 2021, 29, 055001.

     Not in our library10.1088/1361-651x/abf152 (opens in a new tab)

323. 323.

     Artrith N. Machine Learning for the Modeling of Interfaces in Energy Storage and Conversion Materials. J. Phys.: Energy 2019, 1, 032002.

     Not in our library10.1088/2515-7655/ab2060 (opens in a new tab)

324. 324.

     Deringer V. L. Modelling and Understanding Battery Materials with Machine-Learning-Driven Atomistic Simulations. J. Phys. Energy 2020, 2, 041003.

     Not in our library10.1088/2515-7655/abb011 (opens in a new tab)

325. 325.

     Shao Y.; Knijff L.; Dietrich F. M.; Hermansson K.; Zhang C. Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning. Batteries Supercaps 2021, 4, 585–595..

     Not in our library10.1002/batt.202000262 (opens in a new tab)

326. 326.

     Guo H.; Wang Q.; Stuke A.; Urban A.; Artrith N. Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning. Front. Energy Res. 2021, 9, 265.

     Not in our library10.3389/fenrg.2021.695902 (opens in a new tab)

327. 327.

     Forse A. C.; Merlet C.; Griffin J. M.; Grey C. P. New Perspectives on the Charging Mechanisms of Supercapacitors. J. Am. Chem. Soc. 2016, 138, 5731–5744..

     Not in our library10.1021/jacs.6b02115 (opens in a new tab)

328. 328.

     Stratford J. M.; Allan P. K.; Pecher O.; Chater P. A.; Grey C. P. Mechanistic Insights into Sodium Storage in Hard Carbon Anodes Using Local Structure Probes. Chem. Commun. 2016, 52, 12430–12433..

     Not in our library10.1039/c6cc06990h (opens in a new tab)

329. 329.

     Artrith N.; Urban A.; Ceder G. Constructing First-Principles Phase Diagrams of Amorphous LixSi using Machine-Learning-Assisted Sampling with an Evolutionary Algorithm. J. Chem. Phys. 2018, 148, 241711.

     Not in our library10.1063/1.5017661 (opens in a new tab)

330. 330.

     Onat B.; Cubuk E. D.; Malone B. D.; Kaxiras E. Implanted Neural Network Potentials: Application to Li-Si Alloys. Phys. Rev. B: Condens. Matter Mater. Phys. 2018, 97, 094106.

     Not in our library10.1103/physrevb.97.094106 (opens in a new tab)

331. 331.
     A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer · 2021

     No notice
332. 332.

     Ko T. W.; Finkler J. A.; Goedecker S.; Behler J. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer. Acc. Chem. Res. 2021, 54, 808–817..

     Not in our library10.1021/acs.accounts.0c00689 (opens in a new tab)

333. 333.

     Lahrar E. H.; Belhboub A.; Simon P.; Merlet C. Ionic Liquids under Confinement: From Systematic Variations of the Ion and Pore Sizes toward an Understanding of the Structure and Dynamics in Complex Porous Carbons. ACS Appl. Mater. Interfaces 2020, 12, 1789.

     Not in our library10.1021/acsami.9b16740 (opens in a new tab)

334. 334.

     Wang X.; Wang Z.; García de Arquer F. P.; Dinh C.-T.; Ozden A.; Li Y. C.; Nam D.-H.; Li J.; Liu Y.-S.; Wicks J.; et al. Efficient Electrically Powered CO2-to-Ethanol via Suppression of Deoxygenation. Nat. Energy 2020, 5, 478.

     Not in our library10.1038/s41560-020-0607-8 (opens in a new tab)

335. 335.

     Saeedimasine M.; Brandt E. G.; Lyubartsev A. P. Atomistic Perspective on Biomolecular Adsorption on Functionalized Carbon Nanomaterials under Ambient Conditions. J. Phys. Chem. B 2021, 125, 416–430..

     Not in our library10.1021/acs.jpcb.0c08622 (opens in a new tab)

336. 336.

     Xie T.; Grossman J. C. Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks. J. Chem. Phys. 2018, 149, 174111.

     Not in our library10.1063/1.5047803 (opens in a new tab)

337. 337.

     Cheng B.; Engel E. A.; Behler J.; Dellago C.; Ceriotti M. Ab Initio Thermodynamics of Liquid and Solid Water. Proc. Natl. Acad. Sci. U. S. A. 2019, 116, 1110–1115..

     Not in our library10.1073/pnas.1815117116 (opens in a new tab)

338. 338.

     Zhang L.; Han J.; Wang H.; Car R.; E W. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics. Phys. Rev. Lett. 2018, 120, 143001.

     Not in our library10.1103/physrevlett.120.143001 (opens in a new tab)

339. 339.

     Grisafi A.; Ceriotti M. Incorporating Long-Range Physics in Atomic-Scale Machine Learning. J. Chem. Phys. 2019, 151, 204105.

     Not in our library10.1063/1.5128375 (opens in a new tab)

340. 340.

     Grisafi A.; Nigam J.; Ceriotti M. Multi-Scale Approach for the Prediction of Atomic Scale Properties. Chem. Sci. 2021, 12, 2078–2090..

     Not in our library10.1039/d0sc04934d (opens in a new tab)

341. 341.

     Adamo C.; Barone V. Toward Reliable Density Functional Methods without Adjustable Parameters: The PBE0Model. J. Chem. Phys. 1999, 110, 6158.

     Not in our library10.1063/1.478522 (opens in a new tab)

342. 342.

     Tkatchenko A.; Scheffler M. Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data. Phys. Rev. Lett. 2009, 102, 073005.

     Not in our library10.1103/physrevlett.102.073005 (opens in a new tab)

343. 343.

     Tkatchenko A.; Robert A DiStasio J.; Car R.; Scheffler M. Accurate and Efficient Method for Many-Body van der Waals Interactions. Phys. Rev. Lett. 2012, 108, 236403.

     Not in our library10.1103/physrevlett.108.236402 (opens in a new tab)

344. 344.

     Ceriotti M.; Manolopoulos D. E. Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei. Phys. Rev. Lett. 2012, 109, 100604.

     Not in our library10.1103/physrevlett.109.100604 (opens in a new tab)

345. 345.

     Gaus M.; Goez A.; Elstner M. Parametrization and Benchmark of DFTB3 for Organic Molecules. J. Chem. Theory Comput. 2013, 9, 338–354..

     Not in our library10.1021/ct300849w (opens in a new tab)

346. 346.
     Report on the sixth blind test of organic crystal structure prediction methods · 2016

     No notice
347. 347.
     Machine learning enables long time scale molecular photodynamics simulations · 2019

     No notice
348. 348.

     Westermayr J.; Marquetand P. Machine Learning for Electronically Excited States of Molecules. Chem. Rev.2020,.

     Not in our library10.1021/acs.chemrev.0c00749 (opens in a new tab)

349. 349.

     Dral P. O.; Barbatti M. Molecular Excited States through a Machine Learning Lens. Nat. Rev. Chem.2021, 5, 388.

     Not in our library10.1038/s41570-021-00278-1 (opens in a new tab)

350. 350.

     Glick Z. L.; Metcalf D. P.; Koutsoukas A.; Spronk S. A.; Cheney D. L.; Sherrill C. D. AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials. J. Chem. Phys. 2020, 153, 044112.

     Not in our library10.1063/5.0011521 (opens in a new tab)

351. 351.

     Cheng B.; Mazzola G.; Pickard C. J.; Ceriotti M. Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen. Nature 2020, 585, 217–220..

     Not in our library10.1038/s41586-020-2677-y (opens in a new tab)

352. 352.

     Montavon G.; Rupp M.; Gobre V.; Vazquez-Mayagoitia A.; Hansen K.; Tkatchenko A.; Müller K. R.; Anatole Von Lilienfeld O. Machine Learning of Molecular Electronic Properties in Chemical Compound Space. New J. Phys. 2013, 15, 095003.

     Not in our library10.1088/1367-2630/15/9/095003 (opens in a new tab)

353. 353.

     Ramakrishnan R.; Anatole von Lilienfeld O. Many Molecular Properties from One Kernel in Chemical Space. Chimia 2015, 69, 182–186..

     Not in our library10.2533/chimia.2015.182 (opens in a new tab)

354. 354.

     Faber F. A.; Hutchison L.; Huang B.; Gilmer J.; Schoenholz S. S.; Dahl G. E.; Vinyals O.; Kearnes S.; Riley P. F.; Anatole von Lilienfeld O. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. J. Chem. Theory Comput. 2017, 13, 5255–5264..

     Not in our library10.1021/acs.jctc.7b00577 (opens in a new tab)

355. 355.

     Rai B. K.; Bakken G. A. Fast and Accurate Generation of Ab Initio Quality Atomic Charges Using Nonparametric Statistical Regression. J. Comput. Chem. 2013, 34, 1661–1671..

     Not in our library10.1002/jcc.23308 (opens in a new tab)

356. 356.

     Artrith N.; Morawietz T.; Behler J. High-Dimensional Neural-Network Potentials for Multicomponent Systems: Applications to Zinc Oxide. Phys. Rev. B: Condens. Matter Mater. Phys. 2011, 83, 153101.

     Not in our library10.1103/physrevb.83.153101 (opens in a new tab)

357. 357.

     Popelier P. L. A. QCTFF: On the construction of a novel protein force field. Int. J. Quantum Chem. 2015, 115, 1005–1011..

     Not in our library10.1002/qua.24900 (opens in a new tab)

358. 358.

     Bereau T.; DiStasio R. A.; Tkatchenko A.; von Lilienfeld O. A. Non-Covalent Interactions across Organic and Biological Subsets of Chemical Space: Physics-Based Potentials Parametrized from Machine Learning. J. Chem. Phys. 2018, 148, 241706.

     Not in our library10.1063/1.5009502 (opens in a new tab)

359. 359.

     Musil F.; De S.; Yang J.; Campbell J. E.; Day G. M.; Ceriotti M. Machine Learning for the Structure-Energy-Property Landscapes of Molecular Crystals. Chem. Sci. 2018, 9, 1289–1300..

     Not in our library10.1039/c7sc04665k (opens in a new tab)

360. 360.

     Stecher T.; Bernstein N.; Csányi G. Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression. J. Chem. Theory Comput. 2014, 10, 4079–4097..

     Not in our library10.1021/ct500438v (opens in a new tab)

361. 361.

     John S. T.; Csányi G. Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. J. Phys. Chem. B 2017, 121, 10934–10949..

     Not in our library10.1021/acs.jpcb.7b09636 (opens in a new tab)

362. 362.

     McDonagh J. L.; Shkurti A.; Bray D. J.; Anderson R. L.; Pyzer-Knapp E. O. Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields. J. Chem. Inf. Model. 2019, 59, 4278–4288..

     Not in our library10.1021/acs.jcim.9b00646 (opens in a new tab)

363. 363.

     Wang J.; Olsson S.; Wehmeyer C.; Pérez A.; Charron N. E.; de Fabritiis G.; Noé F.; Clementi C. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Cent. Sci. 2019, 5, 755–767..

     Not in our library10.1021/acscentsci.8b00913 (opens in a new tab)

364. 364.

     Scherer C.; Scheid R.; Andrienko D.; Bereau T. Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids. J. Chem. Theory Comput. 2020, 16, 3194–3204..

     Not in our library10.1021/acs.jctc.9b01256 (opens in a new tab)

365. 365.

     Wang J.; Charron N.; Husic B.; Olsson S.; Noé F.; Clementi C. Multi-body effects in a coarse-grained protein force field. J. Chem. Phys. 2021, 154, 164113.

     Not in our library10.1063/5.0041022 (opens in a new tab)

366. 366.

     Shahriari B.; Swersky K.; Wang Z.; Adams R. P.; de Freitas N. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 2016, 104, 148–175..

     Not in our library10.1109/jproc.2015.2494218 (opens in a new tab)

367. 367.

     Denzel A.; Kästner J. Gaussian Process Regression for Geometry Optimization. J. Chem. Phys. 2018, 148, 094114.

     Not in our library10.1063/1.5017103 (opens in a new tab)

368. 368.

     Bisbo M. K.; Hammer B. Efficient Global Structure Optimization with a Machine-Learned Surrogate Model. Phys. Rev. Lett. 2020, 124, 086102.

     Not in our library10.1103/physrevlett.124.086102 (opens in a new tab)

369. 369.

     Peterson A. A. Acceleration of Saddle-Point Searches with Machine Learning. J. Chem. Phys. 2016, 145, 074106.

     Not in our library10.1063/1.4960708 (opens in a new tab)

370. 370.

     Koistinen O.-P.; Dagbjartsdóttir F. B.; Ásgeirsson V.; Vehtari A.; Jónsson H. Nudged Elastic Band Calculations Accelerated with Gaussian Process Regression. J. Chem. Phys. 2017, 147, 152720.

     Not in our library10.1063/1.4986787 (opens in a new tab)

371. 371.

     Koistinen O.-P.; Ásgeirsson V.; Vehtari A.; Jónsson H. Nudged Elastic Band Calculations Accelerated with Gaussian Process Regression Based on Inverse Interatomic Distances. J. Chem. Theory Comput. 2019, 15, 6738–6751..

     Not in our library10.1021/acs.jctc.9b00692 (opens in a new tab)

372. 372.

     Meyer R.; Schmuck K. S.; Hauser A. W. Machine Learning in Computational Chemistry: An Evaluation of Method Performance for Nudged Elastic Band Calculations. J. Chem. Theory Comput. 2019, 15, 6513–6523..

     Not in our library10.1021/acs.jctc.9b00708 (opens in a new tab)

373. 373.

     Yamashita T.; Sato N.; Kino H.; Miyake T.; Tsuda K.; Oguchi T. Crystal Structure Prediction Accelerated by Bayesian Optimization. Phys. Rev. Mater. 2018, 2, 013803.

     Not in our library10.1103/physrevmaterials.2.013803 (opens in a new tab)

374. 374.

     Xia K.; Gao H.; Liu C.; Yuan J.; Sun J.; Wang H.-T.; Xing D. A Novel Superhard Tungsten Nitride Predicted by Machine-Learning Accelerated Crystal Structure Search. Sci. Bull. 2018, 63, 817–824..

     Not in our library10.1016/j.scib.2018.05.027 (opens in a new tab)

375. 375.

     Kanamori K.; Toyoura K.; Honda J.; Hattori K.; Seko A.; Karasuyama M.; Shitara K.; Shiga M.; Kuwabara A.; Takeuchi I. Exploring a Potential Energy Surface by Machine Learning for Characterizing Atomic Transport. Phys. Rev. B: Condens. Matter Mater. Phys. 2018, 97, 125124.

     Not in our library10.1103/physrevb.97.125124 (opens in a new tab)

376. 376.

     Garrido Torres J. A.; Jennings P. C.; Hansen M. H.; Boes J. R.; Bligaard T. Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model. Phys. Rev. Lett. 2019, 122, 156001.

     Not in our library10.1103/physrevlett.122.156001 (opens in a new tab)

377. 377.

     Jørgensen M. S.; Mortensen H. L.; Meldgaard S. A.; Kolsbjerg E. L.; Jacobsen T. L.; Sørensen K. H.; Hammer B. Atomistic Structure Learning. J. Chem. Phys. 2019, 151, 054111.

     Not in our library10.1063/1.5108871 (opens in a new tab)

378. 378.

     Mortensen H. L.; Meldgaard S. A.; Bisbo M. K.; Christiansen M.-P. V.; Hammer B. Atomistic Structure Learning Algorithm with Surrogate Energy Model Relaxation. Phys. Rev. B: Condens. Matter Mater. Phys. 2020, 102, 075427.

     Not in our library10.1103/physrevb.102.075427 (opens in a new tab)

379. 379.

     Rupp M.; Ramakrishnan R.; Anatole von Lilienfeld O. Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. J. Phys. Chem. Lett. 2015, 6, 3309–3313..

     Not in our library10.1021/acs.jpclett.5b01456 (opens in a new tab)

380. 380.

     Anker L. S.; Jurs P. C. Prediction of Carbon-13 Nuclear Magnetic Resonance Chemical Shifts by Artificial Neural Networks. Anal. Chem. 1992, 64, 1157–1164..

     Not in our library10.1021/ac00034a015 (opens in a new tab)

381. 381.

     Meiler J. PROSHIFT: Protein Chemical Shift Prediction Using Artificial Neural Networks. J. Biomol. NMR 2003, 26, 25–37..

     Not in our library10.1023/a:1023060720156 (opens in a new tab)

382. 382.

     Han B.; Liu Y.; Ginzinger S. W.; Wishart D. S. SHIFTX2: Significantly Improved Protein Chemical Shift Prediction. J. Biomol. NMR 2011, 50, 43–57..

     Not in our library10.1007/s10858-011-9478-4 (opens in a new tab)

383. 383.

     Pickard C. J.; Mauri F. All-Electron Magnetic Response with Pseudopotentials: NMR Chemical Shifts. Phys. Rev. B: Condens. Matter Mater. Phys. 2001, 63, 245101.

     Not in our library10.1103/physrevb.63.245101 (opens in a new tab)

384. 384.

     Thonhauser T.; Ceresoli D.; Mostofi A. A.; Marzari N.; Resta R.; Vanderbilt D. A Converse Approach to the Calculation of NMR Shielding Tensors. J. Chem. Phys. 2009, 131, 101101.

     Not in our library10.1063/1.3216028 (opens in a new tab)

385. 385.

     Cuny J.; Xie Y.; Pickard C. J.; Hassanali A. A. Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation. J. Chem. Theory Comput. 2016, 12, 765–773..

     Not in our library10.1021/acs.jctc.5b01006 (opens in a new tab)

386. 386.
     Chemical shifts in molecular solids by machine learning · 2018

     No notice
387. 387.
     Chemical shifts in molecular solids by machine learning · 2018

     No notice
388. 388.

     Engel E. A.; Anelli A.; Hofstetter A.; Paruzzo F.; Emsley L.; Ceriotti M. A Bayesian Approach to NMR Crystal Structure Determination. Phys. Chem. Chem. Phys. 2019, 21, 23385–23400..

     Not in our library10.1039/c9cp04489b (opens in a new tab)

389. 389.

     Hofstetter A.; Balodis M.; Paruzzo F. M.; Widdifield C. M.; Stevanato G.; Pinon A. C.; Bygrave P. J.; Day G. M.; Emsley L. Rapid Structure Determination of Molecular Solids Using Chemical Shifts Directed by Unambiguous Prior Constraints. J. Am. Chem. Soc. 2019, 141, 16624–16634..

     Not in our library10.1021/jacs.9b03908 (opens in a new tab)

390. 390.

     Huang X.; Braams B. J.; Bowman J. M. Ab Initio Potential Energy and Dipole Moment Surfaces for H5O2+. J. Chem. Phys. 2005, 122, 44308.

     Not in our library10.1063/1.1834500 (opens in a new tab)

391. 391.

     Yu Q.; Bowman J. M. Classical, Thermostated Ring Polymer, and Quantum VSCF/VCI Calculations of IR Spectra of H 7 O 3 + and H 9 O 4 + (Eigen) and Comparison with Experiment. J. Phys. Chem. A 2019, 123, 1399–1409..

     Not in our library10.1021/acs.jpca.8b11603 (opens in a new tab)

392. 392.

     Veit M.; Wilkins D. M.; Yang Y.; DiStasio R. A.; Ceriotti M. Predicting Molecular Dipole Moments by Combining Atomic Partial Charges and Atomic Dipoles. J. Chem. Phys. 2020, 153, 024113.

     Not in our library10.1063/5.0009106 (opens in a new tab)

393. 393.

     Wilkins D. M.; Grisafi A.; Yang Y.; Lao K. U.; A. DiStasio R. Jr.; Ceriotti M. AlphaML Website. 2018; http://alphaml.org.

     Not in our library

394. 394.

     Zhang L.; Chen M.; Wu X.; Wang H.; E W.; Car R. Deep Neural Network for the Dielectric Response of Insulators. Phys. Rev. B: Condens. Matter Mater. Phys. 2020, 102, 041121.

     Not in our library10.1103/physrevb.102.041121 (opens in a new tab)

395. 395.

     McQuarrie D. A. Statistical Mechanics; Harper & Row: New York, 1975.

     Not in our library

396. 396.

     Roke S.; Gonella G. Nonlinear Light Scattering and Spectroscopy of Particles and Droplets in Liquids. Annu. Rev. Phys. Chem. 2012, 63, 353–378..

     Not in our library10.1146/annurev-physchem-032511-143748 (opens in a new tab)

397. 397.

     Kapil V.; Wilkins D. M.; Lan J.; Ceriotti M. Inexpensive Modeling of Quantum Dynamics Using Path Integral Generalized Langevin Equation Thermostats. J. Chem. Phys. 2020, 152, 124104.

     Not in our library10.1063/1.5141950 (opens in a new tab)

398. 398.

     Fabrizio A.; Grisafi A.; Meyer B.; Ceriotti M.; Corminboeuf C. Electron Density Learning of Non-Covalent Systems. Chem. Sci. 2019, 10, 9424.

     Not in our library10.1039/c9sc02696g (opens in a new tab)

399. 399.
     Bypassing the Kohn-Sham equations with machine learning · 2017

     No notice
400. 400.

     Alred J. M.; Bets K. V.; Xie Y.; Yakobson B. I. Machine Learning Electron Density in Sulfur Crosslinked Carbon Nanotubes. Compos. Sci. Technol. 2018, 166, 3–9..

     Not in our library10.1016/j.compscitech.2018.03.035 (opens in a new tab)

401. 401.

     Chandrasekaran A.; Kamal D.; Batra R.; Kim C.; Chen L.; Ramprasad R. Solving the Electronic Structure Problem with Machine Learning. npj Comput. Mater. 2019, 5, 22.

     Not in our library10.1038/s41524-019-0162-7 (opens in a new tab)

402. 402.

     Fowler A. T.; Pickard C. J.; Elliott J. A. Managing Uncertainty in Data-Derived Densities to Accelerate Density Functional Theory. J. Phys. Mater. 2019, 2, 034001.

     Not in our library10.1088/2515-7639/ab0b4a (opens in a new tab)

403. 403.

     Zepeda-Núñez L.; Chen Y.; Zhang J.; Jia W.; Zhang L.; Lin L. Deep Density: Circumventing the Kohn-Sham Equations via Symmetry Preserving Neural Networks. J. Comput. Phys.2021, 110523

     Not in our library10.1016/j.jcp.2021.110523 (opens in a new tab)

404. 404.

     Grisafi A.; Fabrizio A.; Meyer B.; Wilkins D. M.; Corminboeuf C.; Ceriotti M. Transferable Machine-Learning Model of the Electron Density. ACS Cent. Sci. 2019, 5, 57–64..

     Not in our library10.1021/acscentsci.8b00551 (opens in a new tab)

405. 405.

     Fabrizio A.; Briling K.; Grisafi A.; Corminboeuf C. Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity. Chimia 2020, 74, 232.

     Not in our library10.2533/chimia.2020.232 (opens in a new tab)

406. 406.

     Whitten J. L. Coulombic Potential Energy Integrals and Approximations. J. Chem. Phys. 1973, 58, 4496–4501..

     Not in our library10.1063/1.1679012 (opens in a new tab)

407. 407.

     Fabrizio A.; Ksenia R. B.; Girardier D. D.; Corminboeuf C. Learning On-Top: Regressing the On-Top Pair Density for Real-Space Visualization of Electron Correlation. J. Chem. Phys.2020, 153, 204111

     Not in our library10.1063/5.0033326 (opens in a new tab)

408. 408.

     Dronskowski R. Computational Chemistry of Solid State Materials: A Guide for Materials Scientists, Chemists, Physicists and Others; Wiley-VCH: Weinheim, 2005.

     Not in our library

409. 409.

     Rubner Y.; Tomasi C.; Guibas L. J. A Metric for Distributions with Applications to Image Databases. Proceedings of the Sixth International Conference on Computer Vision; Washington, DC, USA, 1998; pp 59–66.

     Not in our library

410. 410.

     Umeno Y.; Kubo A. Prediction of electronic structure in atomistic model using artificial neural network. Comput. Mater. Sci. 2019, 168, 164.

     Not in our library10.1016/j.commatsci.2019.06.005 (opens in a new tab)

411. 411.

     del Rio B. G.; Kuenneth C.; Tran H. D.; Ramprasad R. An Efficient Deep Learning Scheme To Predict the Electronic Structure of Materials and Molecules: The Example of Graphene-Derived Allotropes. J. Phys. Chem. A 2020, 124, 9496.

     Not in our library10.1021/acs.jpca.0c07458 (opens in a new tab)

412. 412.

     Fung V.; Hu G.; Ganesh P.; Sumpter B. G. Machine Learned Features from Density of States for Accurate Adsorption Energy Prediction. Nat. Commun. 2021, 12, 88.

     Not in our library10.1038/s41467-020-20342-6 (opens in a new tab)

413. 413.

     Ellis J. A.; Cangi A.; Modine N. A.; Stephens J. A.; Thompson A. P.; Rajamanickam S. Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. 2020; arxiv.org/abs/2010.04905.

     Not in our library

414. 414.

     Yeo B. C.; Kim D.; Kim C.; Han S. Pattern Learning Electronic Density of States. Sci. Rep. 2019, 9, 5879.

     Not in our library10.1038/s41598-019-42277-9 (opens in a new tab)

415. 415.

     Alavi A.; Kohanoff J.; Parrinello M.; Frenkel D. Ab Initio Molecular Dynamics with Excited Electrons. Phys. Rev. Lett. 1994, 73, 2599–2602..

     Not in our library10.1103/physrevlett.73.2599 (opens in a new tab)

416. 416.

     Lopanitsyna N.; Ben Mahmoud C.; Ceriotti M. Finite-Temperature Materials Modeling from the Quantum Nuclei to the Hot Electron Regime. Phys. Rev. Materials 2021, 5, 043802.

     Not in our library10.1103/physrevmaterials.5.043802 (opens in a new tab)

417. 417.
     Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon · 2021

     Not checked
418. 418.

     Stukowski A. Visualization and Analysis of Atomistic Simulation Data with OVITO-the Open Visualization Tool. Modell. Simul. Mater. Sci. Eng. 2010, 18, 015012.

     Not in our library10.1088/0965-0393/18/1/015012 (opens in a new tab)

419. 419.

     Ramakrishnan R.; Dral P. O.; Rupp M.; von Lilienfeld O. A. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. J. Chem. Theory Comput. 2015, 11 (5), 2087–2096..

     Not in our library10.1021/acs.jctc.5b00099 (opens in a new tab)

420. 420.

     Young T. A.; Johnston-Wood T.; Deringer V. L.; Duarte F. A transferable active-learning strategy for reactive. Chemical Science 2021,.

     Not in our library10.1039/d1sc01825f (opens in a new tab)

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