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Machine Learning Force Fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda +5
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditiona…
Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela +2
Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level methods, but at a much lower computational cos…
Fairwashing Explanations with Off-Manifold Detergent
Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski +2
Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making…
Dynamical Strengthening of Covalent and Non-Covalent Molecular Interactions by Nuclear Quantum Effects at Finite Temperature
Huziel E. Sauceda, Valentin Vassilev-Galindo, Stefan Chmiela +2
Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the inclusion of the zero point energy and its coupling with the anharmonicities in interatomic…
Automatic Identification of Types of Alterations in Historical Manuscripts
David Lassner, Anne Baillot, Sergej Dogadov +2
Alterations in historical manuscripts such as letters represent a promising field of research. On the one hand, they help understand the construction of text. On the other hand, to…
Autonomous robotic nanofabrication with reinforcement learning
Philipp Leinen, Malte Esders, Kristof T. Schütt +3
The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembl…