Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Cited by papers we hold 14

• A generative model for inorganic materials design · 2025
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• Scaling deep learning for materials discovery · 2023
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• An autonomous laboratory for the accelerated synthesis of inorganic materials · 2023
  Corrected
• Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements · 2022
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• DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science · 2022
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• Graph neural networks for materials science and chemistry · 2022
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• Gaussian Process Regression for Materials and Molecules · 2021
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• Efficient calculation of carrier scattering rates from first principles · 2021
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• A cost-effective and humidity-tolerant chloride solid electrolyte for lithium batteries · 2021
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• Materials Cloud, a platform for open computational science · 2020
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• Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach · 2018
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• Materials genomics methods for high-throughput construction of COFs and targeted synthesis · 2018
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• ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition · 2018
  No notice
• Color-stable highly luminescent sky-blue perovskite light-emitting diodes · 2018
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