A general-purpose machine learning framework for predicting properties of inorganic materials
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A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.
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Assuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project and the UCI Machine Learning Repository to invent new, climate-friendly materials.
“Assuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project [380] and the UCI Machine Learning Repository [271] to invent new, climate-friendly materials [830].”
From Tackling climate change with machine learning (2023) · cites this paper as [830]
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- Authors
- Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Wolverton, Christopher
- Venue
- npj Computational Materials
- Published
- 2016-08-26
- Volume
- 2
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2464725281
- Cited by
- 1,831
- References
- 73
- Citations by year
- 2026: 352 · 2025: 275 · 2024: 240 · 2023: 206 · 2022: 199 · 2021: 183 · 2020: 179 · 2019: 103 · 2018: 69 · 2017: 22 · 2016: 3
- FWCI
- 29.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Computational Materials (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1343639, 1409601, 70NANB14H012)
- U.S. Department of Defense (FA9550, 70NANB14H012)
- U.S. Department of Energy (DESC0007456)
- U.S. Department of Commerce (70NANB14H012, FA9550-12-1-0458)
- National Institute of Standards and Technology (FA9550-12-1-0458, 70NANB14H012)
- Defense Advanced Research Projects Agency (N66001‐15‐C‐4036)
- National Defense Science and Engineering Graduate
- Center for Hierarchical Materials Design (70NANB14H012, FA9550-12-1-0458)
- Air Force Office of Scientific Research (FA9550-, #FA9550-12-1-0458, FA9550-12, FA9550‐12‐1)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- machine learning framework, inorganic materials, property prediction, materials data, data partitioning, crystalline materials, amorphous materials, band gap, glass-forming ability, prediction model
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