Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity.

ABSTRACT: The complex interaction between molecules and catalyst surfaces leads to great difficulties in understanding and predicting the activity and selectivity in heterogeneous catalysis. Here we develop an end-to-end artificial intelligence framework for the activity prediction of heterogeneous catalytic systems (AI-Cat method), which takes simple inputs from names of molecules and metal catalysts and outputs the reaction energy profile from the input molecule to low energy pathway products. The AI-Cat method combines two neural network models, one for predicting reaction patterns and the other for providing the reaction barrier and energy, with a Monte Carlo tree search to resolve the low energy pathways in a reaction network. We then apply AI-Cat to resolve the reaction network of glycerol hyd

SUBMITTER: Kang PL 

PROVIDER: S-EPMC9278456 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature