Machine Learning for Electron-phonon Interactions From Finite Difference

TL;DR

This paper introduces a machine learning pipeline that significantly accelerates the modeling of electron-phonon interactions from finite difference calculations, enabling efficient analysis of complex materials without sacrificing accuracy.

Contribution

The authors develop a novel ML-based approach to predict force constants and Hamiltonians, reducing computational costs of finite difference methods for EPIs while maintaining high accuracy.

Findings

Validated on bilayer graphene, capturing temperature-dependent electronic properties.

Achieved orders of magnitude speedup over traditional finite difference calculations.

Demonstrated applicability to multilayer materials with complex interactions.

Abstract

First-principles investigations of electron-phonon interactions (EPIs) play a crucial role in understanding a wide range of phenomena in physics and materials science. Among various approaches, the finite difference method offers a direct route to capture higher-order EPIs and is compatible with diverse electronic structure solvers. However, its considerable computational cost limits its broader application. To overcome this bottleneck, we present a machine learning electron-phonon interaction (MLEPI) pipeline that predicts force constants and electronic Hamiltonians for modeling EPIs from finite difference calculations, improving efficiency by orders of magnitude without compromising accuracy. The performance of MLEPI is validated by studying the temperature dependence of the electronic band properties in bilayer graphene, where both first- and second order EPIs are treated on an equal footing. Using a heterogeneous edge network, the pipeline integrates both interlayer and intralayer interactions, making it particularly suitable for studying multilayer materials. With its inherent adaptability and ease of transfer to other applications, our methodology provides a robust tool with a very favorable accuracy/efficiency balance for investigating EPIs in large-scale material systems.