ArticleThe journal of physical chemistry letters2023
Machine Learning Seams of Conical Intersection: A Characteristic Polynomial Approach.
Article in The journal of physical chemistry letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Minimum Energy Conical Intersection Optimization Using DFT/MRCI(2).Journal of chemical theory and computation · 2025Article
- Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning.npj computational materials · 2025Article
- Fast and accurate excited states predictions: machine learning and diabatization.Physical chemistry chemical physics : PCCP · 2024Article
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3 authors.
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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.
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