1.6k citations · 5.7k across the 27 of their papers we have counts for
8 papers · 1 filter
Inverse design of 3d molecular structures with conditional generative neural networks
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann +2
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel m…
Roaming leads to amino acid photodamage: A deep learning study of tyrosine
Julia Westermayr, Michael Gastegger, Dora Vörös +4
Although the amino acid tyrosine is among the main building blocks of life, its photochemistry is not fully understood. Traditional theoretical simulations are neither accurate eno…
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…
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke, Stefan Chmiela, Michael Gastegger +3
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs typically ignore electroni…
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…
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…
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…
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…