Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

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.

发现新型化合物及其晶体结构是新材料发现的关键步骤。本文展示了如何通过结合机器学习技术和高通量从头计算方法来加速这一搜索过程。利用基于实验晶体结构数据库构建的概率模型，识别并测试了最有可能形成化合物的创新组成及其最可能的晶体结构。我们通过大规模搜索新三元氧化物，在有限的计算预算下发现了209种新型化合物。提供了一份预测化合物的清单，并讨论了哪些化学领域有望实现高发现率。