Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics

Abstract

In this work, we report a simple, efficient, and scalable machinelearning (ML) approach for mapping non-self-consistent Kohn−Sham Hamiltonians constructed with one kind of density functional to the nearly selfconsistent Hamiltonians constructed with another kind of density functional. This approach is designed as a fast surrogate Hamiltonian calculator for use in long nonadiabatic dynamics simulations of large atomistic systems. In this approach, the input and output features are Hamiltonian matrices computed from different levels of theory. We demonstrate that the developed ML-based Hamiltonian mapping method (1) speeds up the calculations by several orders of magnitude, ( 2) is conceptually simpler than alternative ML approaches, (3) is applicable to different systems and sizes and can be used for mapping Hamiltonians constructed with arbitrary density functionals, (4) requires a modest training data, learns fast, and generates molecular orbitals and their energies with the accuracy nearly matching that of conventional calculations, and (5) when applied to nonadiabatic dynamics simulation of excitation energy relaxation in large systems yields the corresponding time scales within the margin of error of the conventional calculations. Using this approach, we explore the excitation energy relaxation in C 60 fullerene and Si 75 H 64 quantum dot structures and derive qualitative and quantitative insights into dynamics in these systems.

Nonadiabatic molecular dynamics with subsystem density functional theory: application to crystalline pentacene

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Detailed Complementary Consistency: Wave Function Tells Particle How to Hop, Particle Tells Wave Function How to Collapse

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