Towards Guided Predictions of Synthesis—Representations, Modeling and Disorder

Computational materials discovery campaigns are now at a scale where it is commonplace to produce many more candidates than could be experimentally verified, even with the advantage of automated laboratories. This motivates the development of tools which let us leverage computational predictions to improve the synthesis success rate by promoting or ruling out particular candidates. I will share motivating case studies from ongoing work that has occurred both internal to Toyota Research Institute and its consortium, including exploring the role of disorder, the study of phase transformations within solid-state synthesis, and finding synthesizability measures. Relevant methodologies will include DFT, the interface of DFT and experiment, and high-throughput processing of data.