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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 developing adaptive sampling (active learning) workflows to construct accurate and efficient ML-based interatomic potentials and electronic friction models (for predicting density and electronic friction tensors).

List of publications

  • M. Sachs, W. G. Stark, R. J. Maurer, and C. Ortner, Equivariant Representation of Configuration-Dependent Friction Tensors in Langevin Heatbaths, arXiv:2407.13935 (2024) [arXiv]

  • 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, Mach. Learn.: Sci. Technol., 5, 3, 030501 (2024) [arXiv] [journal]

  • 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!

Other contributions

  • Adaptive sampling (active learning) for gas-surface dynamics - Tutorial: ml-gas-surface

  • Program for optimizing chemical processes, based on design of experiments - Do-Exp (MSc project): do-exp.com