no code implementations • 4 Sep 2024 • Hartmut Maennel, Oliver T. Unke, Klaus-Robert Müller

When modeling physical properties of molecules with machine learning, it is desirable to incorporate $SO(3)$-covariance.

1 code implementation • 15 Jan 2024 • Oliver T. Unke, Hartmut Maennel

This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group $\mathrm{E}(3)$, consisting of translations, rotations, and reflections of three-dimensional space.

1 code implementation • 21 Sep 2023 • J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller, Stefan Chmiela

Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations.

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.

2 code implementations • 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.

Ranked #6 on Drug Discovery on QM9

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.

Chemical Physics

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 #6 on Formation Energy on QM9

Drug Discovery Formation Energy Chemical Physics

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