Simulation of surface processes using machine-learned potentials

PN 3-4 (II)

Project description

Machine-learned models for atomistic simulations enable evermore applications in physics, chemistry, and material science, owing to the simulations' unparalleled accuracy/cost ratio. Our project aims to merge our recent developments in machine-learned potentials, surface science, and astrochemistry to tackle the question of the origin of organic chemistry in interstellar environments. Data science techniques will allow us to simulate the onset of organic chemistry on top of interstellar ices in star-forming regions in atomistic resolution. By the end of the project, we will be closer to determining the fate of organic molecules synthesized in space. Moreover, we will have the scientific tools to investigate surface processes in different fields of materials science.

Project information

Project title Simulation of surface processes using machine-learned potentials
Project leaders

Johannes Kästner (Germán Molpeceres, Kristyna Pluhackova)
Project staff

Juan-Carlos Valle Morales, doctoral researcher
Project duration January 2023 - December 2025
Project number PN 3-4 (II)

Publications PN 3-4 and PN 3-4 (II)

  1. 2023

    1. Molpeceres, G., Zaverkin, V., Furuya, K., Aikawa, Y., and Kästner, J., “Reaction dynamics on amorphous solid water surfaces using interatomic machine-learned potentials - Microscopic energy partition revealed from the P + H → PH reaction,” Astronomy & Astrophysics, vol. 673, p. A51, 2023, doi: 10.1051/0004-6361/202346073.
    2. V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner, “Transfer learning for chemically accurate interatomic neural network potentials,” Physical Chemistry Chemical Physics, vol. 25, no. 7, Art. no. 7, 2023, doi: 10.1039/D2CP05793J.
    3. K. Gubaev, V. Zaverkin, P. Srinivasan, A. I. Duff, J. Kästner, and B. Grabowski, “Performance of two complementary machine-learned potentials in modelling chemically complex systems,” NPJ Computational Materials, vol. 9, p. 129, 2023, doi: 10.1038/s41524-023-01073-w.

  2. 2022

    1. C. Kessler et al., “Influence of layer slipping on adsorption of light gases in covalent organic frameworks: A combined experimental and computational study,” Microporous and Mesoporous Materials, vol. 336, p. 111796, May 2022, doi: 10.1016/j.micromeso.2022.111796.
    2. V. Zaverkin, D. Holzmüller, R. Schuldt, and J. Kästner, “Predicting properties of periodic systems from cluster data: A case study of liquid water,” The Journal of Chemical Physics, vol. 156, no. 11, Art. no. 11, 2022, doi: 10.1063/5.0078983.
    3. V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,” Journal of Chemical Theory and Computation, vol. 18, pp. 1–12, 2022, doi: 10.1021/acs.jctc.1c00853.

  3. 2021

    1. D. Born and J. Kästner, “Geometry Optimization in Internal Coordinates Based on Gaussian Process Regression: Comparison of Two Approaches,” Journal of Chemical Theory and Computation, vol. 17, no. 9, Art. no. 9, 2021, doi: 10.1021/acs.jctc.1c00517.
    2. G. Molpeceres, V. Zaverkin, N. Watanabe, and J. Kästner, “Binding energies and sticking coefficients of H₂ on crystalline and amorphous CO ice,” Astronomy & Astrophysics, vol. 648, p. A84, 2021, doi: 10.1051/0004-6361/202040023.

  4. 2020

    1. G. Molpeceres, V. Zaverkin, and J. Kästner, “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc., vol. 499, pp. 1373–1384, 2020, doi: 10.1093/mnras/staa2891.
    2. V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” Journal of Chemical Theory and Computation, vol. 16, pp. 5410–5421, 2020, doi: 10.1021/acs.jctc.0c00347.
    3. A. Denzel and J. Kästner, “Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression,” Journal of Chemical Theory and Computation, vol. 16, no. 8, Art. no. 8, Jul. 2020, doi: 10.1021/acs.jctc.0c00348.

  5. 2019

    1. A. Denzel, B. Haasdonk, and J. Kästner, “Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search,” The Journal of Physical Chemistry A, vol. 123, no. 44, Art. no. 44, 2019, doi: 10.1021/acs.jpca.9b08239.

  6. 2018

    1. A. Denzel and J. Kästner, “Gaussian Process Regression for Transition State Search,” Journal of Chemical Theory and Computation, vol. 14, no. 11, Art. no. 11, 2018, doi: 10.1021/acs.jctc.8b00708.
    2. A. Denzel and J. Kästner, “Gaussian process regression for geometry optimization,” Journal of Chemical Physics, vol. 148, no. 9, Art. no. 9, 2018, doi: 10.1063/1.5017103.
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