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Stefan Chmiela
Stefan Chmiela
Technische Universität Berlin
Bestätigte E-Mail-Adresse bei chmiela.com - Startseite
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Zitiert von
Zitiert von
Jahr
Quantum-chemical insights from deep tensor neural networks
KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko
Nature Communications 8, 13890, 2017
10212017
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
7622017
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
5712017
Towards exact molecular dynamics simulations with machine-learned force fields
S Chmiela, HE Sauceda, KR Müller, A Tkatchenko
Nature Communications 9 (1), 3887, 2018
4122018
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
arXiv preprint arXiv:2010.07067, 2020
2302020
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ...
arXiv preprint arXiv:2102.06321, 2021
1152021
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
1152019
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
752019
Machine Learning Meets Quantum Physics
KT Schütt, S Chmiela, OA von Lilienfeld, A Tkatchenko, K Tsuda, ...
Springer International Publishing, 2020
672020
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller
arXiv preprint arXiv:2105.00304, 2021
502021
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
352020
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
242021
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
192020
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
132020
Towards exact molecular dynamics simulations with invariant machine-learned models
S Chmiela
PQDT-Global, 2019
92019
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
72020
BIGDML: Towards Exact Machine Learning Force Fields for Materials
HE Sauceda, LE Gálvez-González, S Chmiela, LO Paz-Borbón, KR Müller, ...
arXiv preprint arXiv:2106.04229, 2021
32021
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
2022
Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields
NF Schmitz, KR Müller, S Chmiela
arXiv preprint arXiv:2208.12104, 2022
2022
Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems
T Frank, S Chmiela
arXiv preprint arXiv:2106.02549, 2021
2021
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