Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy

TL;DR

This paper presents a deep learning framework using implicit neural representations to efficiently extract Hamiltonian parameters from high-dimensional photoemission spectroscopy data, improving accuracy and speed over traditional methods.

Contribution

The authors develop a novel deep learning approach that automates and accelerates the analysis of complex spectroscopic data for quantum materials.

Findings

Outperforms traditional analytical fitting methods

Achieves better agreement with experimental Fermi surfaces

Enables high-throughput analysis of electronic structures

Abstract

The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional dataset of energy and momentum. However, the quantitative extraction of Hamiltonian parameters from these high-dimensional spectra remains a significant challenge, currently relying on labor-intensive, expert-dependent analysis rather than standardized workflows. Here, we introduce a deep learning framework based on implicit neural representations to accelerate the retrieval of Hamiltonian parameters in two types of transition-metal oxides: perovskite nickelates and manganites. Our approach outperforms traditional analytical fitting procedures, yielding superior agreement with experimental Fermi surface topologies and energy-momentum dispersions. This work highlights the potential of deep learning tools to bridge the gap between theory and experiment, paving the way for high-throughput, autonomous discovery pipelines in quantum materials.