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The Journal of Physical Chemistry. A|April 6, 2013
A density-functional theory-based neural network potential for water clusters including van der Waals correctionsTobias Morawietz, Jörg BehlerJournal of Computer-Aided Molecular Design|October 9, 2020
Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applicationsTobias Morawietz, Nongnuch ArtrithThe Journal of Chemical Physics|February 25, 2012
A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and chargesTobias Morawietz, Vikas Sharma, Jörg BehlerPhysical Chemistry Chemical Physics : PCCP|December 2, 2014
Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentialsSuresh Kondati Natarajan, Tobias Morawietz, Jörg BehlerProceedings of the National Academy of Sciences of the United States of America|July 13, 2016
How van der Waals interactions determine the unique properties of waterTobias Morawietz, Andreas Singraber, Christoph Dellago, et al.Journal of Physics. Condensed Matter : an Institute of Physics Journal|May 16, 2018
Density anomaly of water at negative pressures from first principlesAndreas Singraber, Tobias Morawietz, Jörg Behler, et al.Journal of Chemical Theory and Computation|April 18, 2019
Parallel Multistream Training of High-Dimensional Neural Network PotentialsAndreas Singraber, Tobias Morawietz, Jörg Behler, et al.The Journal of Chemical Physics|August 15, 2023
Developing machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulationsAustin O Atsango, Tobias Morawietz, Ondrej Marsalek, et al.ACS Applied Materials & Interfaces|September 24, 2016
Quantitative in Situ Analysis of Ionomer Structure in Fuel Cell Catalytic LayersTobias Morawietz, Michael Handl, Claudio Oldani, et al.The Journal of Chemical Physics|August 22, 2021
AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentialsMichael S Chen, Tobias Morawietz, Hideki Mori, et al.Pageof 3