Stefan Chmiela
Stefan Chmiela
Technische Universität Berlin
Verified email at chmiela.com - Homepage
Title
Cited by
Cited by
Year
Quantum-chemical insights from deep tensor neural networks
KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko
Nature Communications 8, 13890, 2017
5562017
Machine learning of accurate energy-conserving molecular force fields
S Chmiela, A Tkatchenko, HE Sauceda, I Poltavsky, KT Schütt, KR Müller
Science Advances 3 (5), e1603015, 2017
3422017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
K Schütt, PJ Kindermans, HES Felix, S Chmiela, A Tkatchenko, KR Müller
Advances in Neural Information Processing Systems, 992-1002, 2017
1762017
Towards exact molecular dynamics simulations with machine-learned force fields
S Chmiela, HE Sauceda, KR Müller, A Tkatchenko
Nature Communications 9 (1), 3887, 2018
1322018
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
S Chmiela, HE Sauceda, I Poltavsky, KR Müller, A Tkatchenko
Computer Physics Communications, 38-45, 2019
332019
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
The Journal of Chemical Physics, 114102, 2019
222019
Towards exact molecular dynamics simulations with invariant machine-learned models
S Chmiela
52019
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
arXiv preprint arXiv:1909.08565, 2019
42019
Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
J Wang, S Chmiela, KR Müller, F Noé, C Clementi
The Journal of Chemical Physics 152 (19), 194106, 2020
32020
Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
S Chmiela, HE Sauceda, A Tkatchenko, KR Müller
arXiv preprint arXiv:1912.06401, 2019
22019
Machine Learning for Molecules and Materials
S Chmiela, JM Hernández-Lobato, KT Schütt, A Aspuru-Guzik, ...
2017
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Articles 1–11