Search Results for author: Oliver T. Unke

Found 9 papers, 3 papers with code

So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems

1 code implementation28 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.

Automatic Identification of Chemical Moieties

no code implementations30 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.

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

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.

Transfer Learning

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects

no code implementations1 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.

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

1 code implementation5 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.

Machine Learning Force Fields

no code implementations14 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.

BIG-bench Machine Learning

Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces

no code implementations21 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

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges

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.

Drug Discovery Formation Energy Chemical Physics

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