Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Open accessAbstract
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10^{4} data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
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Low-cost, topological, three-dimensional, one-hot chemical encoding, and geometric descriptors are often employed for this purpose in transition-metal chemistry.
“Low-cost, topological (5, 7, 32, 92-94), three-dimensional (95-100), one-hot chemical encoding (92, 101), and geometric (e.g., for MOF pores) (102-104) descriptors are often employed for this purpose in transition-metal chemistry.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [93]
The paper’s main text (indexed under Abstract; page unavailable) describes a crystal graph built from atom connections, with feature vectors encoding atomic properties. That supports the topological-representation part of the citation, but the copy does not identify its encoding as one-hot or establish that these descriptor types are often used specifically in transition-metal chemistry. The sentence’s three-dimensional and MOF-pore examples are attached to other citations, not this paper. (LLM, conf=0.82)
Experimental — accuracy not yet measuredML can be a valuable tool for improving AM processes.
“ML can be a valuable tool for improving AM processes [859].”
From Tackling climate change with machine learning (2023) · cites this paper as [859]
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- Authors
- Xie, T., & Grossman, J. C.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2018-04-06
- Volume
- 120
- Issue
- 14
- Pages
- 145301-145301
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2766856748
- Cited by
- 2,828
- References
- 36
- Citations by year
- 2026: 576 · 2025: 557 · 2024: 479 · 2023: 360 · 2022: 308 · 2021: 282 · 2020: 173 · 2019: 74 · 2018: 19
- FWCI
- 51.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Toyota Research Institute
- National Energy Research Scientific Computing Center (DE-AC02-05CH11231)
- Extreme Science and Engineering Discovery Environment (ACI-1053575)
- 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
- crystal graph convolutional neural network, crystal structure representation, materials property prediction, model interpretability, perovskite, materials design, crystal structure types
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