Utilizing machine learning to accelerate computational catalyst search

Thomas Bliggard
Stanford University

Computational search for catalytic materials potentially offer a highly accelerated path towards addressing some of our time’s most pertinent technological and societal challenges. The systematic introduction of linear energy relations as a dimensionality reduction tool in catalyst searches lead to what is now referred to as the “descriptor-based search approach”. This approach has been successful in finding leads for novel heterogeneous catalysts and electro-catalysts. Many challenges still persist for the descriptor-based search approach to become a standard tool for “catalysts engineering”. I shall present how we utilize machine learning methods to establish uncertainty estimates on electronic structure simulations and catalyst functionality, and to reduce reaction network complexity, and to accelerate search for catalytic particles.

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