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Machine learning seams of conical intersection: a characteristic polynomial approach

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1021/acs.jpclett.3c01649
AuthorSearch for: ; Search for: 1ORCID identifier: https://orcid.org/0000-0001-8134-1883; Search for: 1ORCID identifier: https://orcid.org/0000-0002-0922-9034
Affiliation
  1. National Research Council Canada. Security and Disruptive Technologies
FunderSearch for: Natural Sciences and Engineering Research Council of Canada
FormatText, Article
Subjectelements; energy; machine learning; mathematical methods; potential energy
Abstract
The machine learning of potential energy surfaces (PESs) has undergone rapid progress in recent years. The vast majority of this work, however, has been focused on the learning of ground state PESs. To reliably extend machine learning protocols to excited state PESs, the occurrence of seams of conical intersections between adiabatic electronic states must be correctly accounted for. This introduces a serious problem, for at such points, the adiabatic potentials are not differentiable to any order, complicating the application of standard machine learning methods. We show that this issue may be overcome by instead learning the coordinate-dependent coefficients of the characteristic polynomial of a simple decomposition of the potential matrix. We demonstrate that, through this approach, quantitatively accurate machine learning models of seams of conical intersection may be constructed.
Publication date
PublisherAmerican Chemical Society
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  • The Journal of Physical Chemistry Letters 14, no. 35 (24 August 2023): 7780–7786.
LanguageEnglish
Peer reviewedYes
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Record identifierb79fa35c-8c78-4d9f-855e-878c20a59f9f
Record created2024-01-25
Record modified2024-01-29
Date modified: