activity
20182022
most citedComparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes

66 citations · 176 across the 6 of their papers we have counts for

collaborators

18 papers

physics.chem-ph202217 cited

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Oliver T. Unke, Martin Stöhr, Stefan Ganscha +8

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical ca…

physics.chem-ph202125 cited

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

Oliver T. Unke, Mihail Bogojeski, Michael Gastegger +3

Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Inst…

physics.chem-ph202166 cited

Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes

Michael Gastegger, Clemens Kauffmann, Jörg Behler +1

Many approaches, which have been developed to express the potential energy of large systems, exploit the locality of the atomic interactions. A prominent example are fragmentation…

physics.chem-ph2021

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng +4

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from…

physics.chem-ph2021

Perspective on integrating machine learning into computational chemistry and materials science

Julia Westermayr, Michael Gastegger, Kristof T. Schütt +1

Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly establish…

cs.LG2021

Equivariant message passing for the prediction of tensorial properties and molecular spectra

Kristof T. Schütt, Oliver T. Unke, Michael Gastegger

Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamic…