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”. One challenge is to accurately predict the structure and the activity of a catalytic particle. We shall present an accelerated approach to solving this problem, involving the integration of machine learning, genetic algorithms, and kMC.

Elucidation of structure and activity using machine learning-accelerated genetic algorithms and kMC

253rd National Meeting (2017)

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