(663d) Data-Driven Models for Catalyst Synthesis and Stability

In the last two decades, millions of materials properties were curated from density functional theory and machine learning calculations, greatly accelerating catalyst design using computation. Despite these efforts in elucidating structure-property relationships, designing materials synthesis to obtain a structure of interest remains a challenge. This is particularly important in the case of catalysts, where the connection between atomistic models and catalytic performance is difficult to probe in experimental settings. In this talk, I will describe how combined advances in high-throughput simulation, machine learning, and literature extraction enable designing and accelerating catalyst synthesis. Using examples from two classes of heterogeneous catalysts – nanoporous materials and multi-metallic alloys – I will show how data-driven methods can guide the synthesis of materials with increasingly controlled active site distributions and stability. Finally, I will show how incorporating chemical theory in machine learning increases the robustness of models and enable increasingly data-efficient simulations for catalysts. These computational tools open numerous opportunities for accelerating catalyst discovery beyond screening.

Prepared by LLNL under Contract DE-AC52-07NA27344.