Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914402475704320 |
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| author | Zhang, Yu Zhong, Yong Tran, Nhat Huy Li, Shuyi Lee, Kyuho Lee, Yonghun Wang, Tiffany C. Hwang, Harold Y. Shen, Zhi-Xun Jia, Chunjing |
| author_facet | Zhang, Yu Zhong, Yong Tran, Nhat Huy Li, Shuyi Lee, Kyuho Lee, Yonghun Wang, Tiffany C. Hwang, Harold Y. Shen, Zhi-Xun Jia, Chunjing |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_16725 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy Zhang, Yu Zhong, Yong Tran, Nhat Huy Li, Shuyi Lee, Kyuho Lee, Yonghun Wang, Tiffany C. Hwang, Harold Y. Shen, Zhi-Xun Jia, Chunjing Strongly Correlated Electrons 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. |
| title | Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy |
| topic | Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2603.16725 |