Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
(2010) Chemistry of Materials — Vol. 22, n° 12, p. 3762-3767 (2010)
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- Abstract
- Finding new compounds and their crystal structures is an essential step to new materials discoveries. We demonstrate how this search can be accelerated using a combination of machine learning techniques and high-throughput ab initio computations. Using a probabilistic model built on an experimental crystal structure database, novel compositions that are most likely to form a compound, and their most-probable crystal structures, are identified and tested for stability by ab initio computations. We performed such a large-scale search for new ternary oxides, discovering 209 new compounds with a limited computational budget. A list of these predicted compounds is provided, and we discuss the chemistries in which high discovery rates can be expected.
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Citations
Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G. (2010). Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory. Chemistry of Materials, 22(12), 3762-3767. https://doi.org/10.1021/cm100795d (Original work published 2010)