Michael Gastegger
Michael Gastegger
Microsoft Research AI4Science
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Cited by
Cited by
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142-10186, 2021
Machine learning molecular dynamics for the simulation of infrared spectra
M Gastegger, J Behler, P Marquetand
Chemical science 8 (10), 6924-6935, 2017
Combining machine learning and computational chemistry for predictive insights into chemical systems
JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ...
Chemical reviews 121 (16), 9816-9872, 2021
Equivariant message passing for the prediction of tensorial properties and molecular spectra
K Schütt, O Unke, M Gastegger
International Conference on Machine Learning, 9377-9388, 2021
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), 5024, 2019
SchNetPack: A deep learning toolbox for atomistic systems
KT Schütt, P Kessel, M Gastegger, KA Nicoli, A Tkatchenko, KR Müller
Journal of chemical theory and computation 15 (1), 448-455, 2018
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), 2018
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
N Gebauer, M Gastegger, K Schütt
Advances in neural information processing systems 32, 2019
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller
Nature communications 12 (1), 7273, 2021
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
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
Perspective on integrating machine learning into computational chemistry and materials science
J Westermayr, M Gastegger, KT Schütt, RJ Maurer
The Journal of Chemical Physics 154 (23), 2021
Inverse design of 3d molecular structures with conditional generative neural networks
NWA Gebauer, M Gastegger, SSP Hessmann, KR Müller, KT Schütt
Nature communications 13 (1), 973, 2022
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
Roadmap on machine learning in electronic structure
HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ...
Electronic Structure 4 (2), 023004, 2022
Machine learning of solvent effects on molecular spectra and reactions
M Gastegger, KT Schütt, KR Müller
Chemical science 12 (34), 11473-11483, 2021
SE (3)-equivariant prediction of molecular wavefunctions and electronic densities
O Unke, M Bogojeski, M Gastegger, M Geiger, T Smidt, KR Müller
Advances in Neural Information Processing Systems 34, 14434-14447, 2021
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), 2016
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), 2020
Generating equilibrium molecules with deep neural networks
NWA Gebauer, M Gastegger, KT Schütt
arXiv preprint arXiv:1810.11347, 2018
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