Papers › SchNet - a deep learning architecture for molecules and materials

SchNet - a deep learning architecture for molecules and materials

17 Dec 2017arXiv:1712.06113links table onlyarchive 2025-07-28

Kristof T. Schütt, Huziel E. Sauceda, Pieter-Jan Kindermans, Alexandre Tkatchenko, Klaus-Robert Müller

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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 is ideally suited for representing quantum-mechanical interactions, enabling 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 \emph{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 of the quantum-mechanical properties of C₂₀-fullerene that would have been infeasible with regular ab initio molecular dynamics.

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atomistic-machine-learning/schnetpack officialpytorchNOASSERTION report
Tony-Y/cgnn mentioned on GitHubpytorch report
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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Formation Energy Materials Project SchNet MAE 35 #7 of 9 Archive leaderboard report

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