Search Results for author: Michael Gastegger

Found 21 papers, 8 papers with code

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

2 code implementations11 Dec 2022 Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger

SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning.

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.

Property Prediction

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

2 code implementations5 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.

Drug Discovery

Machine learning of solvent effects on molecular spectra and reactions

1 code implementation28 Oct 2020 Michael Gastegger, Kristof T. Schütt, Klaus-Robert Müller

We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction.

BIG-bench Machine Learning

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

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

1 code implementation NeurIPS 2019 Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt

Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials.

Molecular Dynamics with Neural-Network Potentials

no code implementations18 Dec 2018 Michael Gastegger, Philipp Marquetand

Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time.

Active Learning BIG-bench Machine Learning

Machine learning enables long time scale molecular photodynamics simulations

no code implementations22 Nov 2018 Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger, Sebastian Mai, Leticia González, Philipp Marquetand

Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales.

BIG-bench Machine Learning Computational Efficiency

Generating equilibrium molecules with deep neural networks

no code implementations26 Oct 2018 Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt

Discovery of atomistic systems with desirable properties is a major challenge in chemistry and material science.

Analysis of Atomistic Representations Using Weighted Skip-Connections

no code implementations23 Oct 2018 Kim A. Nicoli, Pan Kessel, Michael Gastegger, Kristof T. Schütt

In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation.

BIG-bench Machine Learning Property Prediction

Quantum-chemical insights from interpretable atomistic neural networks

no code implementations27 Jun 2018 Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller

With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers.

WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials

no code implementations15 Dec 2017 Michael Gastegger, Ludwig Schwiedrzik, Marius Bittermann, Florian Berzsenyi, Philipp Marquetand

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning.

BIG-bench Machine Learning

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