A Primary Unified Geometric Framework of Molecular Reaction Dynamics Based on the Variational Principle

TL;DR

This paper introduces a unified geometric framework for molecular reaction dynamics based on the variational principle, incorporating quantum mechanics, AI techniques, and spacetime curvature effects to better understand reaction processes.

Contribution

It develops a comprehensive geometric approach that unifies electronic structure and quantum dynamics, incorporating curvature and AI methods for potential energy surface construction.

Findings

Formulated a geometric description of molecular reaction dynamics.

Incorporated spacetime curvature and gauge fields into nuclear Hamiltonian.

Proposed variational approaches for electronic and quantum dynamics.

Abstract

This work describes a geometric framework on molecular reaction dynamics based on the variational principle, where the Schr{\"o}dinger equation must be solved to ``see'' how a reaction occurs. First, the mathematical preliminaries are given by discussing the principle of least action and the mountain pass theorem. Second, we discuss the physical preliminaries, including the principle of equivalence for deriving the kinetic energy operator (KEO) and artificial intelligence (AI) techniques to build the potential energy surface (PES) in general spacetime. Moreover, we simplified electromagnetic interactions in curved spacetime within the molecular system and consequently, we are able to construct the nuclear Hamiltonian in nonzero curvature spacetime. This indicates possibility to introduce gauge fields through the curvature, such as additional term in the nuclear KEO near a conical intersection. Third, the single-particle approximation provides a powful ansatz to solve the Schr{\"o}dinger equation by variational principle. Thus, one can formulate the variational approaches for either electronic structure or quantum dynamics. In this work, based on previous discussions ({\it Phys. Chem. Chem. Phys.} {\bf 27} (2025), 20397) we unified them by a geometric description, where the geometric phase is naturally introduced. Finally, due to optimization characteristic of the present theory, further discussions on the present theory from optimization insight are also given, including two postulates, generative AI techniques, role of perturbation, and Markov process in optimization.