Bidirectional Learning of Relationships between Atomic Environments and Electronic Band Dispersion in Semiconductor Heterostructures

TL;DR

This paper presents a bidirectional learning framework that connects atomic environments to electronic band structures in semiconductor heterostructures, enabling interpretation and prediction of spectroscopic data.

Contribution

It introduces a novel bidirectional model linking atomic environments and electronic bands, facilitating both prediction and inference from spectral data.

Findings

Successfully applied to silicon/germanium heterostructures

Reconstructed electronic bands from inferred atomic descriptors

Revealed spectral signatures of atomic environments

Abstract

Atomic-scale variations in semiconductor heterostructures, arising from strain, interfaces, and compositional modulation, strongly influence electronic band dispersion but remain difficult to probe and compare using first-principles methods alone. Here, we introduce a bidirectional learning approach that links local atomic environments to electronic band dispersion using atomically resolved spectral functions as information-dense representations. This formulation enables a forward model that predicts how atomic environments shape electronic bands, and a reverse model that infers atomic-environment descriptors directly from band dispersion images, including angle-resolved photoemission spectra. Applied to silicon/germanium superlattices and heterostructures, the approach reveals how inner and interfacial atomic environments give rise to distinct spectral signatures. The coupled forward-reverse framework enables self-consistent validation by reconstructing electronic band structures from inferred descriptors. By treating electronic bands as decomposable, learnable objects, this work provides a physics-informed route for interpreting spectroscopic data and for data-driven exploration of electronic properties in complex semiconductor heterostructures.