Fast and accurate excited states predictions: machine learning and diabatization.

ABSTRACT: The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends t

SUBMITTER: Srsen S 

PROVIDER: S-EPMC10829538 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature