Artificial intelligence based stable materials discovery process

Abstract

An existing materials database (EMDB) is a compilation of inorganic materials with composition and crystal structures known from prior experimental synthesis and characterization reports, or from ab initio or other computational studies, and includes a composition, structure, and stability value for each material. A hypothetical materials database (HMDB) is an extremely large compilation of materials of unknown stability and synthesizability, with no explicitly available or accessible prior experimental or computational report of their structure-composition combinations. An automated process for efficiently expanding the size of an EMDB includes a cyclical sub-process in which a rapid algorithm provides preliminary stability estimates for hypothetical materials selected from an HMDB, and those materials with a favorable stability prediction undergo a full ab initio analysis to obtain quantitative stability values and are then added to the EMDB. During each iteration of the cyclical sub-process, the rapid algorithm is trained on the EMDB, so that it becomes more effective at providing preliminary stability estimates with each iteration, as the EMDB expands.