Michael Gastegger
Michael Gastegger
Verified email at tu-berlin.de
Cited by
Cited by
Machine learning molecular dynamics for the simulation of infrared spectra
M Gastegger, J Behler, P Marquetand
Chemical science 8 (10), 6924-6935, 2017
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
KT Schütt, M Gastegger, A Tkatchenko, KR Müller, RJ Maurer
Nature communications 10 (1), 1-10, 2019
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
M Gastegger, L Schwiedrzik, M Bittermann, F Berzsenyi, P Marquetand
The Journal of chemical physics 148 (24), 241709, 2018
SchNetPack: A deep learning toolbox for atomistic systems
KT Schutt, P Kessel, M Gastegger, KA Nicoli, A Tkatchenko, KR Müller
Journal of chemical theory and computation 15 (1), 448-455, 2018
High-dimensional neural network potentials for organic reactions and an improved training algorithm
M Gastegger, P Marquetand
Journal of chemical theory and computation 11 (5), 2187-2198, 2015
Machine learning enables long time scale molecular photodynamics simulations
J Westermayr, M Gastegger, MFSJ Menger, S Mai, L González, ...
Chemical science 10 (35), 8100-8107, 2019
Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes
M Gastegger, C Kauffmann, J Behler, P Marquetand
The Journal of chemical physics 144 (19), 194110, 2016
Loganin and secologanin derived tryptamine–iridoid alkaloids from Palicourea crocea and Palicourea padifolia (Rubiaceae)
A Berger, MK Kostyan, SI Klose, M Gastegger, E Lorbeer, L Brecker, ...
Phytochemistry 116, 162-169, 2015
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
J Westermayr, M Gastegger, P Marquetand
The journal of physical chemistry letters 11 (10), 3828-3834, 2020
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
NWA Gebauer, M Gastegger, KT Schütt
arXiv preprint arXiv:1906.00957, 2019
Quantum-chemical insights from interpretable atomistic neural networks
KT Schütt, M Gastegger, A Tkatchenko, KR Müller
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 311-330, 2019
Generating equilibrium molecules with deep neural networks
NWA Gebauer, M Gastegger, KT Schütt
arXiv preprint arXiv:1810.11347, 2018
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
arXiv preprint arXiv:2010.07067, 2020
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
M Gastegger, A McSloy, M Luya, KT Schütt, RJ Maurer
The Journal of Chemical Physics 153 (4), 044123, 2020
Molecular dynamics with neural network potentials
M Gastegger, P Marquetand
Machine Learning Meets Quantum Physics, 233-252, 2020
Exploring density functional subspaces with genetic algorithms
M Gastegger, L González, P Marquetand
Monatshefte für Chemie-Chemical Monthly 150 (2), 173-182, 2019
Molecular dynamics with neural-network potentials
M Gastegger, P Marquetand
arXiv preprint arXiv:1812.07676, 2018
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
HE Sauceda, M Gastegger, S Chmiela, KR Müller, A Tkatchenko
The Journal of Chemical Physics 153 (12), 124109, 2020
Modeling molecular spectra with interpretable atomistic neural networks
M Gastegger, K Schütt, H Sauceda, KR Müller, A Tkatchenko
APS March Meeting Abstracts 2019, E32. 007, 2019
Artificial Intelligence in theoretical chemistry
M Gastegger
Ph. D. thesis, University of Vienna, 2017
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