M. Gastegger

发表

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm.

M. Gastegger, P. Marquetand, 2015, Journal of chemical theory and computation.

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Kristof T. Schütt, K. Müller, A. Tkatchenko, 2019, Nature Communications.

Machine learning of solvent effects on molecular spectra and reactions

Kristof T. Schütt, M. Gastegger, Klaus-Robert Müller, 2020, Chemical science.

Perspective on integrating machine learning into computational chemistry and materials science.

Kristof T. Schütt, M. Gastegger, R. Maurer, 2021, The Journal of chemical physics.

Deep integration of machine learning into computational chemistry and materials science

Kristof T. Schütt, M. Gastegger, R. Maurer, 2021 .

Analysis of Atomistic Representations Using Weighted Skip-Connections

Kristof T. Schutt, K. T. Schutt, M. Gastegger, 2018, 1810.09751.

Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes.

J. Behler, M. Gastegger, P. Marquetand, 2016, The Journal of chemical physics.

Machine learning molecular dynamics for the simulation of infrared spectra

J. Behler, M. Gastegger, P. Marquetand, 2017, Chemical science.

Modeling Molecular Spectra with Interpretable Atomistic Neural Networks

Klaus-Robert Müller, Michael Gastegger, Alexandre Tkatchenko, 2019 .

Machine Learning Force Fields

Klaus-Robert Müller, Michael Gastegger, Alexandre Tkatchenko, 2020, Chemical reviews.

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Oliver T. Unke, Thomas Unterthiner, A. Tkatchenko, 2022, ArXiv.

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

Klaus-Robert Müller, Michael Gastegger, Oliver T. Unke, 2021, Nature Communications.

Deep learning study of tyrosine reveals that roaming can lead to photodamage

M. Gastegger, P. Marquetand, J. Westermayr, 2022, Nature Chemistry.

Roadmap on Machine learning in electronic structure

Kristof T. Schütt, T. Carrington, S. Manzhos, 2022, Electronic Structure.

Deep learning study of tyrosine reveals that roaming can lead to photodamage

M. Gastegger, P. Marquetand, J. Westermayr, 2022, Nature Chemistry.

SchNetPack 2.0: A neural network toolbox for atomistic machine learning.

Kristof T. Schütt, K. T. Schutt, Stefaan S. P. Hessmann, 2022, The Journal of chemical physics.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

A. Tkatchenko, M. Gastegger, Stefan Chmiela, 2021, Chemical reviews.

Machine Learning Force Fields

Michael Gastegger, Klaus-Robert Muller, Alexandre Tkatchenko, 2020, Chemical reviews.

Loganin and secologanin derived tryptamine-iridoid alkaloids from Palicourea crocea and Palicourea padifolia (Rubiaceae).

M. Gastegger, L. Brecker, A. Berger, 2015, Phytochemistry.

SchNetPack: A Deep Learning Toolbox For Atomistic Systems.

K-R Müller, Kristof T. Schütt, M Gastegger, 2018, Journal of chemical theory and computation.

Deep learning study of tyrosine reveals that roaming can lead to photodamage

M. Gastegger, P. Marquetand, J. Westermayr, 2021, Nature Chemistry.

Inverse design of 3d molecular structures with conditional generative neural networks pdf

Michael Gastegger, Kristof T. Schütt, Niklas W. A. Gebauer, 2021, Nature Communications.

Generating equilibrium molecules with deep neural networks pdf

Michael Gastegger, Kristof T. Schütt, Niklas W. A. Gebauer, 2018, ArXiv.

wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials.

M Gastegger, L Schwiedrzik, M Bittermann, 2017, The Journal of chemical physics.

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Julia Westermayr, Michael Gastegger, Philipp Marquetand, 2020, The journal of physical chemistry letters.

Exploring density functional subspaces with genetic algorithms

Michael Gastegger, Philipp Marquetand, Leticia González, 2018, Monatshefte für Chemie - Chemical Monthly.

Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields.

K. Müller, A. Tkatchenko, M. Gastegger, 2020, The Journal of chemical physics.

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

Michael Gastegger, Oliver T. Unke, Kristof T. Schütt, 2021, ICML.

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

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

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

Michael Gastegger, Klaus-Robert Muller, Mario Geiger, 2021, NeurIPS.

Machine learning molecular dynamics for the simulation of infrared spectra† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02267k

Michael Gastegger, Philipp Marquetand, Jörg Behler, 2017, Chemical science.

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation.

Kristof T. Schütt, M Gastegger, A McSloy, 2020, The Journal of chemical physics.

Machine learning enables long time scale molecular photodynamics simulations

Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger, 2018, Chemical science.

Machine learning enables long time scale molecular photodynamics simu- lations

M. Gastegger, J. Westermayr, M. Menger, 2021 .

Machine learning of solvent effects on molecular spectra and reactions

Michael Gastegger, Kristof T. Schütt, Kristof T Schütt, 2020, Chemical Science.

Automatic identification of chemical moieties

Oliver T. Unke, Kristof T. Schütt, Michael C. Kampffmeyer, 2022, Physical chemistry chemical physics : PCCP.

Molecular Dynamics with Neural Network Potentials

Michael Gastegger, Philipp Marquetand, M. Gastegger, 2018, Machine Learning Meets Quantum Physics.

Quantum-chemical insights from interpretable atomistic neural networks

Klaus-Robert Müller, Michael Gastegger, Alexandre Tkatchenko, 2018, Explainable AI.

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Michael Gastegger, Klaus-Robert Müller, Alexandre Tkatchenko, 2019, Nature Communications.

Perspective on integrating machine learning into computational chemistry and materials science.

Kristof T. Schütt, M. Gastegger, R. Maurer, 2021, The Journal of chemical physics.

Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning

M. Gastegger, Tatyana Krivobokova, P. A. Sánchez‐Murcia, 2023, J. Chem. Inf. Model..