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Design And Modeling of Oxide Catalysts by machine LEarning and atomistic Simulations

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Design And Modeling of Oxide Catalysts by machine LEarning and atomistic Simulations The DAMOCLES project aims to apply data-driven modeling (i.e., molecular simulations combined with machine learning) to study and tailor metal oxide catalysts for CO2 hydrogenation processes (reverse water-gas shift, CO2 methanation, and CO2 to methanol), with the final goal of screening oxides in search of new and better catalytic materials. The starting point of the project is the newly-released OC22 oxide data set (from Meta FAIR and Ulissi's group) comprising approximately 50k adsorption energies of relevant molecules on multi-component oxide surfaces spanning 52 elements of the periodic table. State-of-the-art machine learning models (e.g., Gaussian process regression, and graph neural networks), will be applied for the prediction of relevant adsorption energies not included in the sparsely labeled OC22 dataset. New density-functional theor

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recordType

award

status

SIGNED

region

EU

value

196829.28

unit

EUR