1 code implementation • 28 May 2022 • J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller
The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods.
no code implementations • 17 May 2022 • Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Klaus-Robert Müller
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes.
no code implementations • 30 Mar 2022 • Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
no code implementations • NeurIPS 2021 • Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus-Robert Müller
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations.
no code implementations • 1 May 2021 • Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields.
1 code implementation • 5 Feb 2021 • 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 dynamics studies.
no code implementations • 14 Oct 2020 • Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
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 traditional electronic-structure methods.
no code implementations • 21 Nov 2019 • Silvan Käser, Oliver T. Unke, Markus Meuwly
It is used to run finite-temperature molecular dynamics simulations, and to determine the infrared spectra and the hydrogen transfer rates for the three molecules.
6 code implementations • J. Chem. Theory Comput. 2019 • Oliver T. Unke, Markus Meuwly
Further, two new datasets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems.
Ranked #4 on Formation Energy on QM9
Drug Discovery Formation Energy Chemical Physics