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Stefan Chmiela

Showing results (1-10 of 20) with videos related to

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Journal of Chemical Theory and Computation|May 8, 2023
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear ConvergenceStefan Blücher, Klaus-Robert Müller, Stefan Chmiela
The Journal of Physical Chemistry Letters|October 24, 2022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force FieldsNiklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Nature Communications|September 26, 2018
Towards exact molecular dynamics simulations with machine-learned force fieldsStefan Chmiela, Huziel E Sauceda, Klaus-Robert Müller, et al.
Nature Communications|August 6, 2024
A Euclidean transformer for fast and stable machine learned force fieldsJ Thorben Frank, Oliver T Unke, Klaus-Robert Müller, et al.
Nature Communications|July 11, 2023
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
The Journal of Chemical Physics|March 9, 2021
Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approachJiang Wang, Stefan Chmiela, Klaus-Robert Müller, et al.
Nature Communications|June 15, 2023
Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
The Journal of Chemical Physics|October 2, 2020
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fieldsHuziel E Sauceda, Michael Gastegger, Stefan Chmiela, et al.
The Journal of Chemical Physics|March 24, 2019
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forcesHuziel E Sauceda, Stefan Chmiela, Igor Poltavsky, et al.
Nature Communications|January 10, 2017
Quantum-chemical insights from deep tensor neural networksKristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
Journal of Chemical Theory and Computation|May 8, 2023
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear ConvergenceStefan Blücher, Klaus-Robert Müller, Stefan Chmiela
The Journal of Physical Chemistry Letters|October 24, 2022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force FieldsNiklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Nature Communications|September 26, 2018
Towards exact molecular dynamics simulations with machine-learned force fieldsStefan Chmiela, Huziel E Sauceda, Klaus-Robert Müller, et al.
Nature Communications|August 6, 2024
A Euclidean transformer for fast and stable machine learned force fieldsJ Thorben Frank, Oliver T Unke, Klaus-Robert Müller, et al.
Nature Communications|July 11, 2023
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
The Journal of Chemical Physics|March 9, 2021
Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approachJiang Wang, Stefan Chmiela, Klaus-Robert Müller, et al.
Nature Communications|June 15, 2023
Efficient interatomic descriptors for accurate machine learning force fields of extended moleculesAdil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, et al.
The Journal of Chemical Physics|October 2, 2020
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fieldsHuziel E Sauceda, Michael Gastegger, Stefan Chmiela, et al.
The Journal of Chemical Physics|March 24, 2019
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forcesHuziel E Sauceda, Stefan Chmiela, Igor Poltavsky, et al.
Nature Communications|January 10, 2017
Quantum-chemical insights from deep tensor neural networksKristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, et al.
Pageof 2