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Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence


Dec 24, 2022
Stefan Blücher, Klaus-Robert Müller, Stefan Chmiela

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* 18 pages, 12 figures, preprint 

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Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields


Aug 25, 2022
Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela

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* 16 pages, 4 figures 

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Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems


Jun 14, 2021
Thorben Frank, Stefan Chmiela

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BIGDML: Towards Exact Machine Learning Force Fields for Materials


Jun 08, 2021
Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko

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* 15 pages, 8 figures, development of methodology and applications 

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SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects


May 01, 2021
Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller

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Machine Learning Force Fields


Oct 14, 2020
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller

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Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach


May 04, 2020
Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noè, Cecilia Clementi

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* 14 pages, 6 figures 

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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions


Dec 19, 2017
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller

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* Advances in Neural Information Processing Systems 30 (2017), pp. 992-1002 

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