Article ; Online: Machine Learning Seams of Conical Intersection: A Characteristic Polynomial Approach.
The journal of physical chemistry letters
2023 Volume 14, Issue 35, Page(s) 7780–7786
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 ... ...
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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Language | English |
Publishing date | 2023-08-24 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 1948-7185 |
ISSN (online) | 1948-7185 |
DOI | 10.1021/acs.jpclett.3c01649 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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