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

<!-- START_ABSTRACT --> <p> 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. </p> <!-- END_ABSTRACT --> <!-- START_TEMPLATE --> <ul> <li> Source: </li> <li> Tags: </li> </ul> <!-- END_TEMPLATE -->