Andreas Singraber

University of Vienna — Austria

Materials Science · Physical Sciences

8h-index1.1kcitations20works3.52yr avg
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Research Topics

Machine Learning in Materials Science(9), Protein Structure and Dynamics(4), Computational Drug Discovery Methods(4), Spectroscopy and Quantum Chemical Studies(3), Quantum, superfluid, helium dynamics(3)

Publications20 total

Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
The Journal of Chemical Physics·2025· 7 cited
Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
arXiv (Cornell University)·2025Open Access
Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
Physical Review Letters·2023· 47 cited
Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
arXiv (Cornell University)·2022· 2 citedOpen Access
Improved description of atomic environments using low-cost polynomial functions with compact support
Machine Learning Science and Technology·2021· 1 citedOpen Access
CompPhysVienna/n2p2: Version 2.1.1
Zenodo (CERN European Organization for Nuclear Research)·2021· 2 citedOpen Access
Improved Description of Atomic Environments using Low-cost Polynomial\n Functions with Compact Support
arXiv (Cornell University)·2020Open Access
Parallel Multistream Training of High-Dimensional Neural Network Potentials
Journal of Chemical Theory and Computation·2019· 221 cited
HDNNP training data set for Cu2S
Zenodo (CERN European Organization for Nuclear Research)·2019Open Access
HDNNP training data set for Cu2S
Zenodo (CERN European Organization for Nuclear Research)·2019Open Access
Library-Based <i>LAMMPS</i> Implementation of High-Dimensional Neural Network Potentials
Journal of Chemical Theory and Computation·2019· 282 cited
CompPhysVienna/n2p2: Neural network potential package 1.0.0
Zenodo (CERN European Organization for Nuclear Research)·2018· 5 citedOpen Access
Density anomaly of water at negative pressures from first principles
Journal of Physics Condensed Matter·2018· 21 citedOpen Access
Designing and training neural network potentials for molecular dynamics simulations
2018
How van der Waals interactions determine the unique properties of water
Proceedings of the National Academy of Sciences·2016· 479 citedOpen Access
S-shooting: a Bennett–Chandler-like method for the computation of rate constants from committor trajectories
Faraday Discussions·2016· 13 citedOpen Access
How van der Waals interactions determine the unique properties of water
Figshare·2015Open Access
Optimising reaction coordinates for crystallisation by tuning the crystallinity definition
Molecular Physics·2013· 38 cited
Trimaximal lepton mixing with a trivial Dirac phase
Physics Letters B·2010· 20 citedOpen Access
Lepton mixing modelling and numerical methods
University of Vienna·2010Open Access

Frequent Co-authors

Christoph Dellago(11), Jörg Behler(7), Tobias Morawietz(3), Peitao Liu(2), Jian-Tao Wang(2), Noah Avargues(2), Carla Verdi(2), Ferenc Karsai(2), Xing‐Qiu Chen(2), Georg Kresse(2), Emir Kocer(2), Jonas A. Finkler(2), Philipp Misof(2), Tsz Wai Ko(2), Martin P. Bircher(2), Tobias Morawietz(1), Swetlana Jungblut(1), Walter Grimus(1), L. Lavoura(1), Georg Menzl(1)