Novel neural network framework advances large-scale simulations of zeolites

Researchers from Charles University have developed a novel machine-learning framework for large-scale simulations of zeolites. Using convolutional neural networks, this approach significantly accelerates atomistic simulations, enabling detailed investigations of zeolite materials under operating conditions. The framework also uncovers previously unknown chemical processes and species in zeolites, aiding in their application in petrochemical processes and sustainable chemistry. This advancement represents a major step forward in the rational design and understanding of catalytic materials.

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