Potential energy surface for AlF-AlF collisions

ORAL

Abstract

This work presents a new approach to generating diatom-diatom potential energy surfaces (PES) through machine learning techniques. In particular, after using some energies at given geometries calculated at CCSD(T) (coupled cluster with single and double and perturbative triple excitations) level of theory, we employ a Gaussian process regression method based on a particular set of molecular features valid for all range of distances, i.e., a single model works for the long-range and short-range region of the PES. Finally, as an example, we calculate the PES for AlF-AlF and the density of states and lifetime of intermediate complexes via the developed machine learning approach.

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Presenters

  • Weiqi Wang

    • Fritz-Haber Institute

Authors

  • Weiqi Wang

    • Fritz-Haber Institute

  • Xiangyue Liu

    • Fritz-Haber Institute

  • Jesus Perez Rios

    • Department of Physics, Stony Brook University, Stony Brook, New York 11794, USA
    • Department of Physics and Astronomy, Stony Brook University; Fritz-Haber Institute
    • Fritz Haber Institute of the Max Planck Society
    • Department of Physics and Astronomy, Stony Brook University
    • Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794, USA
    • Fritz-Haber-Institut der Max-Planck-Gesellschaft; IMM, Radboud University; Department of Physics and Astronomy, Stony Brook University
    • Department of Physics and Astronomy, Stony Brook University; Fritz-Haber-Institute
    • Department of Physics and Astromy, Stony Brook University, Stony Brook, NY 11794, USA