Tobias Morawietz
Cited by
Cited by
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
N Artrith, T Morawietz, J Behler
Physical Review B 83 (15), 153101, 2011
How van der Waals interactions determine the unique properties of water
T Morawietz, A Singraber, C Dellago, J Behler
Proceedings of the National Academy of Sciences 113 (30), 8368-8373, 2016
A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
T Morawietz, J Behler
The Journal of Physical Chemistry A 117 (32), 7356–7366, 2013
A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges
T Morawietz, V Sharma, J Behler
The Journal of chemical physics 136 (6), 064103, 2012
Parallel multistream training of high-dimensional neural network potentials
A Singraber, T Morawietz, J Behler, C Dellago
Journal of chemical theory and computation 15 (5), 3075-3092, 2019
The interplay of structure and dynamics in the Raman spectrum of liquid water over the full frequency and temperature range
T Morawietz, O Marsalek, SR Pattenaude, LM Streacker, D Ben-Amotz, ...
The journal of physical chemistry letters 9 (4), 851-857, 2018
Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials
SK Natarajan, T Morawietz, J Behler
Physical Chemistry Chemical Physics 17 (13), 8356-8371, 2015
Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
T Morawietz, N Artrith
Journal of Computer-Aided Molecular Design 35 (4), 557-586, 2021
A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer
T Morawietz, J Behler
Zeitschrift für Physikalische Chemie 227 (11), 1559, 2013
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations
AM Miksch, T Morawietz, J Kästner, A Urban, N Artrith
Machine Learning: Science and Technology 2 (3), 031001, 2021
Hiding in the crowd: spectral signatures of overcoordinated hydrogen-bond environments
T Morawietz, AS Urbina, PK Wise, X Wu, W Lu, D Ben-Amotz, ...
The Journal of Physical Chemistry Letters 10 (20), 6067-6073, 2019
Exploiting machine learning to efficiently predict multidimensional optical spectra in complex environments
MS Chen, TJ Zuehlsdorff, T Morawietz, CM Isborn, TE Markland
The Journal of Physical Chemistry Letters 11 (18), 7559-7568, 2020
Density anomaly of water at negative pressures from first principles
A Singraber, T Morawietz, J Behler, C Dellago
Journal of Physics: Condensed Matter 30 (25), 254005, 2018
AENET–LAMMPS and AENET–TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials
MS Chen, T Morawietz, H Mori, TE Markland, N Artrith
The Journal of Chemical Physics 155 (7), 074801, 2021
Don’t overweight weights: Evaluation of weighting strategies for multi-task bioactivity classification models
L Humbeck, T Morawietz, N Sturm, A Zalewski, S Harnqvist, W Heyndrickx, ...
Molecules 26 (22), 6959, 2021
Efficient simulations of water with ab initio accuracy
T Morawietz
Bochum, Ruhr-Universität Bochum, Diss., 2015, 2016
Entwicklung eines effizienten Potentials für das Wasser-Dimer basierend auf künstlichen neuronalen Netzen ()
T Morawietz
PhD Thesis, 2010
MELLODDY: cross pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information
W Heyndrickx, L Mervin, T Morawietz, N Sturm, L Friedrich, A Zalewski, ...
Optically induced anisotropy in time-resolved scattering: Imaging molecular scale structure and dynamics in disordered media with experiment and theory
A Montoya-Castillo, MS Chen, SL Raj, KA Jung, KS Kjaer, T Morawietz, ...
Physical Review Letters 129, 056001, 2022
Efficient simulations of water with accuracy
T Morawietz
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