Data‐Driven Materials Science: Status, Challenges, and Perspectives

Lauri Himanen, Amber Geurts, Adam Stuart Foster and 1 more

Advanced Science · 2019 · Review · CC BY

doi:10.1002/advs.201900808

Abstract

Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 314

1.
G. Ceder, Science 1998, 280, 1099.
Not in our library
2.
T. W. Eagar, Technol. Rev. 1995, 98, 42.
Not in our library
3.
M. Boren, V. Chan, C. Musso, In McKinsey on Chemicals, Vol. 4 McKinsey & Company Industry Publications, 2012.
Not in our library
4.
T. Hey, S. Tansley, K. Tolle, The Fourth Paradigm: Data‐Intensive Scientific Discovery, Microsoft Research, Redmond, WA, USA 2009.
Not in our library
5.
A. Agrawal, A. Choudhary, APL Mater. 2016, 4, 053208.
Not in our library
6.
K. Schwab, The Fourth Industrial Revolution, https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution (accessed: August 2019).
Not in our library
7.
E. Brynjolfsson, A. McAfee, Second Machine Age. Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company, New York City, NY, USA 2014.
Not in our library
8.
K. Rajan, Mater. Today 2005, 8, 38.
Not in our library
9.
K. Rajan, Annu. Rev. Mater. Res. 2015, 45, 153.
Not in our library
10.
Y. Liu, T. Zhao, W. Ju, S. Shi, J. Materiomics 2017, 3, 159.
Not in our library
11.
L. Zdeborová, Nat. Phys. 2017, 13, 420.
Not in our library
12.
M. Rupp, Int. J. Quantum Chem. 2015, 115, 1058.
Not in our library
13.
R. Ramprasad, R. Batra, G. Pilania, A. Mannodi‐Kanakkithodi, C. Kim, npj Comput. Mater. 2017, 3, 54.
Not in our library
14.
L. Ward, C. Wolverton, Curr. Opin. Solid State Mater. Sci. 2017, 21, 167.
Not in our library
15.
T. Mueller, A. G. Kusne, R. Ramprasad, Machine Learning in Materials Science. John Wiley & Sons, Hoboken, NJ, USA 2016, Ch. 4, pp. 186–273.
Not in our library
16.
A. Zunger, Nat. Rev. Chem. 2018, 2, 0121.
Not in our library
17.
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature 2018, 559, 547.
Not in our library10.1038/s41586-018-0337-2
18.
J. E. Gubernatis, T. Lookman, Phys. Rev. Mater. 2018, 2, 120301.
Not in our library
19.
B. R. Goldsmith, J. Esterhuizen, J. X. Liu, C. J. Bartel, C. Sutton, AIChE J. 2018, 64, 2311.
Not in our library
20.
K. Takahashi, Y. Tanaka, Dalton Trans. 2016, 45, 10497.
Not in our library10.1039/c6dt01501h
21.
The Minerals Metals & Materials Society (TMS), Building a Materials Data Infrastructure: Opening New Pathways to Discovery and Innovation in Science and Engineering. TMS, Pittsburgh, PA 2017.
Not in our library
22.
A. P. Bartók, S. De, C. Poelking, N. Bernstein, J. R. Kermode, G. Csányi, M. Ceriotti, Sci. Adv. 2017, 3, 12.
Not in our library10.1126/sciadv.1701816
23.
Gu G. H., J. Noh, I. Kim, Y. Jung, J. Mater. Chem. A 2019, 7, 17096.
Not in our library
24.
Walsh K., editor, Open Innovation, Open Science, Open to the World—A Vision for Europe, European Commission, Luxembourg City, Luxembourg 2016.
Not in our library
25.
J. J. Regazzi, Scholarly Communications: A History from Content as King to Content as Kingmaker, Rowman & Littlefield, Lanham 2015.
Not in our library
26.
D. Fanelli, Proc. Natl. Acad. Sci. USA 2018, 115, 2628.
Not in our library10.1073/pnas.1708272114
27.
The academic, economic and societal impacts of Open Access: an evidence-based review · 2016
No notice
28.
J. M. Esanu, P. F. Uhlir, in The Role of Scientific and Technical Data and Information in The Public Domain: Proc. of A Symp. The National Academies Press, Washington, D.C. 2003.
Not in our library
29.
Open Science, https://openscience.com (accessed: August 2019).
Not in our library
30.
FOSTER, https://www.fosteropenscience.eu (accessed: August 2019).
Not in our library
31.
H. W. Chesborough, Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business Press, Brighton, MA, USA 2006.
Not in our library
32.
How open science helps researchers succeed · 2016
No notice
33.
When will everything be Open Access?, https://blog.impactstory.org/oa-by-when/ (accessed: August 2019).
Not in our library
34.
The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles · 2018
No notice
35.
M. Schiltz, PLOS Biol. 2018, 16, 1.
Not in our library10.1371/journal.pbio.3000031
36.
ICSU World Data System, https://www.icsu-wds.org (accessed: August 2019).
Not in our library
37.
OECD, OECD Principles and Guidelines for Access to Research Data from Public Funding, OECD Publishing, Paris, France 2007.
Not in our library
38.
re3data.org, https://www.re3data.org (accessed: August 2019).
Not in our library
39.
PLOS, https://www.plos.org (accessed: August 2019).
Not in our library
40.
Scientific Data, https://www.nature.com/sdata/ (accessed: August 2019).
Not in our library

All 314 references

Cited by papers we hold 1

Gaussian Process Regression for Materials and Molecules · 2021
No notice

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.