The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Combinatorial screening for new materials in unconstrained composition space with machine learning
Abstract
Typically, computational screens for new materials sharply constrain the compositional search space, structural search space, or both, for the sake of tractability. To lift these constraints, we construct a machine learning model from a database of thousands of density functional theory (DFT) calculations. The resulting model can predict the thermodynamic stability of arbitrary compositions without any other input and with six orders of magnitude less computer time than DFT. We use this model to scan roughly 1.6 million candidate compositions for novel ternary compounds (${A}_{x}{B}_{y}{C}_{z}$), and predict 4500 new stable materials. Our method can be readily applied to other descriptors of interest to accelerate domain-specific materials discovery.
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Cites
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Cited by
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
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Self-citationMachine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
A general-purpose machine learning framework for predicting properties of inorganic materials
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Self-citationMaterials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
Metadata
- Authors
- Meredig, B., Agrawal, A., Kirklin, S., Saal, J. E., Doak, J. W., Thompson, A., Zhang, K., Choudhary, A., & Wolverton, C.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2014-03-14
- Volume
- 89
- Issue
- 9
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2313966941
- Cited by
- 730
- References
- 29
- FWCI
- 11.91
- Type
- journal article
- Language
- en
- Open access
- closed
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