Matthias Rupp

University of Konstanz — Germany

Materials Science · Physical Sciences

31h-index10.5kcitations109works5.32yr avg
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Research Topics

Machine Learning in Materials Science(68), Computational Drug Discovery Methods(53), X-ray Diffraction in Crystallography(12), Protein Structure and Dynamics(11), Advanced Chemical Physics Studies(10)

Publications109 total

Hydrogen liquid-liquid transition from first principles and machine learning
Physical review. B./Physical review. B·2025· 2 citedOpen Access
Hydrogen liquid-liquid transition from first principles and machine learning
arXiv (Cornell University)·2025Open Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
arXiv (Cornell University)·2024Open Access
Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO<sub>2</sub> on CuPt/TiO<sub>2</sub>
The Journal of Physical Chemistry C·2024· 16 cited
Guest editorial: Special Topic on software for atomistic machine learning
The Journal of Chemical Physics·2024· 3 citedOpen Access
Guest Editorial: Special Topic on Software for Atomistic Machine Learning
arXiv (Cornell University)·2024Open Access
Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO$_2$ by CuPt/TiO$_2$
arXiv (Cornell University)·2024· 3 citedOpen Access
Heat flux for semilocal machine-learning potentials
Physical review. B./Physical review. B·2023· 21 citedOpen Access
Ultra-fast interpretable machine-learning potentials
npj Computational Materials·2023· 67 citedOpen Access
Research data for "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials"
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Research data for "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials"
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Code Generation for Machine Learning using Model-Driven Engineering and SysML
arXiv (Cornell University)·2023· 3 citedOpen Access
Heat flux for semi-local machine-learning potentials
arXiv (Cornell University)·2023· 1 citedOpen Access
Data and software: Heat flux for semi-local machine-learning potentials
2023· 1 citedOpen Access
Data and software: Heat flux for semi-local machine-learning potentials
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
How Much Chemistry and Physics Can We Push AI to Do?
ChemViews·2023
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Frequent Co-authors

Klaus‐Robert Müller(12), O. Anatole von Lilienfeld(11), Gisbert Schneider(11), Katja Hansen(10), Alexandre Tkatchenko(7), Ewgenij Proschak(6), Matthias Scheffler(5), Raghunathan Ramakrishnan(4), Robert Körner(4), Igor V. Tetko(4), Kieron Burke(4), Gábor Cśanyi(4), John Snyder(3), Grégoire Montavon(3), Heiko Zettl(3), Haoyan Huo(3), Mario Boley(3), Luca M. Ghiringhelli(3), Jilles Vreeken(3), Timon Schroeter(3)