I’m developing ML-based models for gas-surface dynamics, for both adiabatic (Born-Oppenheimer molecular dynamics) and non-adiabatic (molecular dynamics with electronic friction) approaches. This includes building ML-based interatomic potentials and electronic friction models for local density friction approximation (LDFA), where we model the metal surface density and orbital dependent friction (ODF), in which we model entire electronic friction tensor.

List of publications

  • W. G. Stark, C. van der Oord, I. Batatia, Y. Zhang, B. Jiang, G. Csányi, and R. J. Maurer, Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces, arXiv:2403.15334 [physics.chem-ph] (2024) [arXiv]

  • W. G. Stark, J. Westermayr, O. A. Douglas-Gallardo, J. Gardner, S. Habershon, R. J. Maurer, Machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces based on iterative refinement of reaction probabilities, J. Phys. Chem. C, 127, 50, 24168–24182 (2023) [arXiv] [journal]

  • C. L. Box, W. G. Stark, R. J. Maurer, Ab initio calculation of electron-phonon linewidths and molecular dynamics with electronic friction at metal surfaces with numeric atom-centered orbitals, Electron. Struct. 5, 035005 (2023) [arXiv] [journal]

  • J. Gardner, O. A. Douglas-Gallardo, W. G. Stark, J. Westermayr, S. M. Janke, S. Habershon, R. J. Maurer, NQCDynamics.jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase, J. Chem. Phys. 156, 174801 (2022) [arXiv] [journal]

Find the full publication list on google scholar!