Articles with public access mandates - Stefan ChmielaLearn more
Available somewhere: 20
Quantum-chemical insights from deep tensor neural networks
KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko
Nature Communications 8, 13890, 2017
Mandates: US National Science Foundation, German Research Foundation, Federal Ministry …
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
K Schütt, PJ Kindermans, HE Sauceda Felix, S Chmiela, A Tkatchenko, ...
Advances in neural information processing systems 30, 2017
Mandates: German Research Foundation, European Commission, Federal Ministry of …
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
Mandates: US National Science Foundation, German Research Foundation, European …
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142-10186, 2021
Mandates: Swiss National Science Foundation, German Research Foundation, European …
Towards exact molecular dynamics simulations with machine-learned force fields
S Chmiela, HE Sauceda, KR Müller, A Tkatchenko
Nature Communications 9 (1), 3887, 2018
Mandates: US National Science Foundation, German Research Foundation, European Commission
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
Mandates: US National Science Foundation, Swiss National Science Foundation, German …
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
Mandates: Swiss National Science Foundation, German Research Foundation, Federal …
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
Mandates: US National Science Foundation, German Research Foundation, European …
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
Mandates: US National Science Foundation, German Research Foundation, European …
Accurate global machine learning force fields for molecules with hundreds of atoms
S Chmiela, V Vassilev-Galindo, OT Unke, A Kabylda, HE Sauceda, ...
Science Advances 9 (2), eadf0873, 2023
Mandates: European Commission, Luxembourg National Research Fund, Federal Ministry of …
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
Mandates: European Commission, Luxembourg National Research Fund, Federal Ministry of …
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
Mandates: German Research Foundation, European Commission, Federal Ministry of …
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
Mandates: US National Science Foundation, German Research Foundation, European …
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
Mandates: German Research Foundation, European Commission, Luxembourg National …
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
A Kabylda, V Vassilev-Galindo, S Chmiela, I Poltavsky, A Tkatchenko
Nature Communications 14 (1), 3562, 2023
Mandates: European Commission, Luxembourg National Research Fund, Federal Ministry of …
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
Mandates: US National Science Foundation, German Research Foundation, European …
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
Mandates: Federal Ministry of Education and Research, Germany
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
A Kabylda, JT Frank, SS Dou, A Khabibrakhmanov, LM Sandonas, ...
Mandates: European Commission, Luxembourg National Research Fund
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
I Poltavsky, A Charkin-Gorbulin, M Puleva, GC Fonseca, I Batatia, ...
Mandates: UK Engineering and Physical Sciences Research Council, European Commission …
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence
S Blücher, KR Müller, S Chmiela
Journal of Chemical Theory and Computation, 2023
Mandates: Federal Ministry of Education and Research, Germany
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