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Oliver T. Unke
Oliver T. Unke
Senior Research Scientist at Google DeepMind
Geverifieerd e-mailadres voor google.com
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Machine Learning Force Fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142–10186, 2021
10612021
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
OT Unke, M Meuwly
Journal of Chemical Theory and Computation 15 (6), 3678-3693, 2019
9172019
Equivariant message passing for the prediction of tensorial properties and molecular spectra
KT Schütt, OT Unke, M Gastegger
Proceedings of the 38th International Conference on Machine Learning, 9377-9388, 2021
5652021
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, 7273, 2021
2602021
Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces
OT Unke, M Meuwly
Journal of Chemical Information and Modeling 57 (8), 1923-1931, 2017
1302017
Accurate global machine learning force fields for molecules with hundreds of atoms
S Chmiela, V Vassilev-Galindo, OT Unke, A Kabylda, HE Sauceda, ...
Science Advances 9 (2), eadf0873, 2023
1102023
A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
OT Unke, M Meuwly
The Journal of Chemical Physics 148 (24), 241708, 2018
992018
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
OT Unke, M Bogojeski, M Gastegger, M Geiger, T Smidt, KR Müller
Advances in Neural Information Processing Systems 35, 2021
982021
High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning
OT Unke, D Koner, S Patra, S Käser, M Meuwly
Machine Learning: Science and Technology 1 (1), 013001, 2020
722020
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
T Frank, O Unke, KR Müller
Advances in Neural Information Processing Systems 35, 29400-29413, 2022
58*2022
Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces
S Käser, OT Unke, M Meuwly
New Journal of Physics 22 (5), 055002, 2020
572020
Nonadiabatic effects in electronic and nuclear dynamics
MP Bircher, E Liberatore, NJ Browning, S Brickel, C Hofmann, A Patoz, ...
Structural Dynamics 4 (6), 061510, 2018
542018
Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation
D Koner, OT Unke, K Boe, RJ Bemish, M Meuwly
The Journal of Chemical Physics 150 (21), 211101, 2019
522019
Minimal distributed charges: Multipolar quality at the cost of point charge electrostatics
OT Unke, M Devereux, M Meuwly
The Journal of Chemical Physics 147 (16), 161712, 2017
472017
Accurate machine learned quantum-mechanical force fields for biomolecular simulations
OT Unke, M Stöhr, S Ganscha, T Unterthiner, H Maennel, S Kashubin, ...
arXiv preprint arXiv:2205.08306, 2022
312022
Isomerization and decomposition reactions of acetaldehyde relevant to atmospheric processes from dynamics simulations on neural network-based potential energy surfaces
S Käser, OT Unke, M Meuwly
The Journal of Chemical Physics 152 (21), 214304, 2020
312020
Reactive atomistic simulations of Diels-Alder reactions: The importance of molecular rotations
U Rivero, OT Unke, M Meuwly, S Willitsch
The Journal of Chemical Physics 151 (10), 104301, 2019
312019
Reactive molecular dynamics for the [Cl–CH3–Br]− reaction in the gas phase and in solution: a comparative study using empirical and neural network force fields
S Brickel, AK Das, OT Unke, HT Turan, M Meuwly
Electronic Structure 1 (2), 024002, 2019
282019
Collision-induced rotational excitation in N: Comparison of computations and experiment
OT Unke, JC Castro-Palacio, RJ Bemish, M Meuwly
The Journal of chemical physics 144 (22), 224307, 2016
262016
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
OT Unke, M Stöhr, S Ganscha, T Unterthiner, H Maennel, S Kashubin, ...
Science Advances 10 (14), eadn4397, 2024
212024
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