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

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.