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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
11132017
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
8182017
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ...
arXiv preprint arXiv:1706.08566, 2017
6732017
Towards exact molecular dynamics simulations with machine-learned force fields
S Chmiela, HE Sauceda, KR Müller, A Tkatchenko
Nature Communications 9 (1), 3887, 2018
4642018
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142-10186, 2021
3392021
Combining machine learning and computational chemistry for predictive insights into chemical systems
JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ...
Chemical reviews 121 (16), 9816-9872, 2021
1692021
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
1322019
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
832019
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller
Nature communications 12 (1), 1-14, 2021
732021
Machine Learning Meets Quantum Physics
KT Schütt, S Chmiela, OA von Lilienfeld, A Tkatchenko, K Tsuda, ...
Springer International Publishing, 2020
692020
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
382020
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
HE Sauceda, V Vassilev-Galindo, S Chmiela, KR Müller, A Tkatchenko
Nature Communications 12 (1), 1-10, 2021
252021
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
HE Sauceda, M Gastegger, S Chmiela, KR Müller, A Tkatchenko
The Journal of Chemical Physics 153 (12), 124109, 2020
242020
BIGDML—Towards accurate quantum machine learning force fields for materials
HE Sauceda, LE Gálvez-González, S Chmiela, LO Paz-Borbón, KR Müller, ...
Nature communications 13 (1), 1-16, 2022
142022
Construction of machine learned force fields with quantum chemical accuracy: Applications and chemical insights
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
Machine Learning Meets Quantum Physics, 277-307, 2020
122020
Towards exact molecular dynamics simulations with invariant machine-learned models
S Chmiela
PQDT-Global, 2019
112019
Accurate molecular dynamics enabled by efficient physically constrained machine learning approaches
S Chmiela, HE Sauceda, A Tkatchenko, KR Müller
Machine Learning Meets Quantum Physics, 129-154, 2020
92020
Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
A Kabylda, V Vassilev-Galindo, S Chmiela, I Poltavsky, A Tkatchenko
arXiv preprint arXiv:2209.03985, 2022
32022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
NF Schmitz, KR Müller, S Chmiela
The Journal of Physical Chemistry Letters 13, 10183-10189, 2022
32022
Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems
T Frank, S Chmiela
arXiv preprint arXiv:2106.02549, 2021
22021
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