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SchNet – A deep learning architecture for molecules and materials

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons192349

Sauceda,  Huziel E.
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons205908

Müller,  Klaus-Robert
Machine Learning Group, Technische Universität Berlin;
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;
Department of Brain and Cognitive Engineering, Korea University;

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Citation

Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018). SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148(24): 241722. doi:10.1063/1.5019779.


Cite as: http://hdl.handle.net/21.11116/0000-0001-3BD6-C
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. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.