SchNet – A deep learning architecture for molecules and materials

Volume: 148, Issue: 24
Published: Mar 29, 2018
Abstract
Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space....
Paper Details
Title
SchNet – A deep learning architecture for molecules and materials
Published Date
Mar 29, 2018
Volume
148
Issue
24
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