Journal
CHEMISTRY OF MATERIALS
Volume 22, Issue 12, Pages 3762-3767Publisher
AMER CHEMICAL SOC
DOI: 10.1021/cm100795d
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Funding
- NSF [DMR-0606276]
- Belgian American Education Foundation (BAEE) and Total
- U.S. Department of Energy [DE-FG02-97ER25308]
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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 Mill 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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