Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning

TL;DR

This paper introduces N$^2$AMD, a machine learning framework employing an E(3)-equivariant neural Hamiltonian to significantly improve the accuracy and efficiency of non-adiabatic molecular dynamics simulations in solids, enabling large-scale and precise excited-state studies.

Contribution

The paper presents a novel neural Hamiltonian-based framework that preserves Euclidean symmetry, achieving superior accuracy and efficiency in NAMD simulations compared to traditional methods.

Findings

Achieves state-of-the-art accuracy in simulating excited-state dynamics in solids.

Demonstrates large-scale simulations of carrier recombination with improved lifetime predictions.

Ensures high generalizability and seamless integration with existing NAMD techniques.

Abstract

Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N$^2$AMD which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. The preservation of Euclidean symmetry of Hamiltonian enables N$^2$AMD to achieve state-of-the-art performance. Distinct from conventional machine learning methods that predict key quantities in NAMD, N$^2$AMD computes these quantities directly with a deep neural Hamiltonian, ensuring supreme accuracy, efficiency, and consistency. Furthermore, N$^2$AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework not only boosts the efficiency and precision of NAMD simulations but also opens new avenues to advance materials research.