About the Research
Research Theme
Prediction of chemically relevant properties using machine learning, for accelerating AFIR calculations
Keyword
Research History
Please see CV.
Representative Research Achievements
Efficient recursive least squares solver for rank-deficient matrices
R. Staub, S. N. Steinmann, Appl. Math. Comput., 2021, 399, 125996
DOI: 10.1016/j.amc.2021.125996
DockOnSurf: A Python Code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces
C. Martí, S. Blanck, R. Staub, S. Loehlé, C. Michel, S. N. Steinmann, J. Chem. Inf. Model., 2021, 61, 3386-3396
DOI: 10.1021/acs.jcim.1c00256
Parameter-free coordination numbers for solutions and interfaces
R. Staub, S. N. Steinmann, J. Chem. Phys., 2020, 152, 024124
DOI: 10.1063/1.5135696
Water adlayers on noble metal surfaces: Insights from energy decomposition analysis
P. Clabaut, R. Staub, J. Galiana, E. Antonetti, S. N. Steinmann, J. Chem. Phys., 2020, 153, 054703
DOI: 10.1063/5.0013040
Energy Decomposition Analysis for Metal Surface–Adsorbate Interactions by Block Localized Wave Functions
R. Staub, M. Iannuzzi, R. Z. Khaliullin, S. N. Steinmann, J. Chem. Theory Comput., 2019, 15, 265-275
DOI: 10.1021/acs.jctc.8b00957
Related Research
Publications
2023
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Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson's Catalyst Case
, P. Gantzer, Y. Harabuchi, S. Maeda, A. Varnek, Molecules, 2023, 28 (11), 4477
DOI: 10.3390/molecules28114477