Framework for an AI-Ready ARPES Community Database
Abstract
Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization impedes this endeavor. As a premier measure of electronic structure underlying material properties, Angle-Resolved Photoemission Spectroscopy (ARPES) has accumulated over four decades of spectra, yet the community's publication-centered workflow preserves only a highly selected fraction of its experimental record. A shared data ecosystem could transform these fragmented measurements and their contexts into a persistent, collective scientific memory, creating immediate value for reproducibility and reuse while laying the foundation for AI-enabled discovery. In this Perspective, we report outcomes of a recent community workshop and propose a practical path toward such an ecosystem. Its benefits unfold along three timescales. In the near term, standardized, searchable, and citable datasets strengthen analysis pipelines independently of AI. In the intermediate future, community-validated tools operating across laboratories could enable broader reuse and increasingly closed-loop automated experiments. In the long term, a diverse corpus of measured electronic spectra could support foundation models for quantum matter. Looking forward, a diverse corpus of measured electronic spectra could support foundation models for quantum matter and new discoveries. We discuss lessons from existing scientific databases, the current state of AI in ARPES, as well as areas of community convergence on data organization, access, credit, governance, and a staged pilot program.
Related
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- Nano-ARPES observation of a flat band at the \bar{M} point in 2.4° twisted bilayer graphene
- Phonon-Dressed Quasiparticles in Epitaxial Scandium Nitride Resolved by Soft X-ray ARPES
- Probing the electronic structure of UTe2 with ARPES and high-energy spectroscopy
- Experimental Signatures of a Memory-Dressed Cooper-Pair Field in Antinodal ARPES
BibTeX
@misc{shen2026framework,
title = {{Framework for an AI-Ready ARPES Community Database}},
author = {Zhi-Xun Shen and Jin Gene Wong and Karen Lei and Leon Garcia and Felix Baumberger and Sergey Borisenko and Aaron Bostwick and Ting Cao and Tanny Chavez and Yulin Chen and Monika Choudhary and Andrea Damascelli and Dan Dessau and Thomas P. Devereaux and Sandy A. Ekahana and Alexei Fedorov and Atsushi Fujimori and Yucheng Guo and M. Zahid Hasan and Makoto Hashimoto and Yu He and Philip Hofmann and Zahid Hussain and Harold Y. Hwang and Zhurun Ji and Chunjing Jia and Timur K. Kim and Phil D. C. King and Takeshi Kondo and Alessandra Lanzara and Chun Lin and Donghui Lu and Matteo Michiardi and Sung-Kwan Mo and Brian Moritz and Laurenz Rettig and Eli Rotenberg and Takafumi Sato and Kyle M. Shen and Jonathan A. Sobota and Inna Vishik and Shuolong Yang and Ming Yi and Timothy Zaklama},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.15236},
eprint = {2610.15236},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.15236},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Framework for an AI-Ready ARPES Community Database
AU - Zhi-Xun Shen
AU - Jin Gene Wong
AU - Karen Lei
AU - Leon Garcia
AU - Felix Baumberger
AU - Sergey Borisenko
AU - Aaron Bostwick
AU - Ting Cao
AU - Tanny Chavez
AU - Yulin Chen
AU - Monika Choudhary
AU - Andrea Damascelli
AU - Dan Dessau
AU - Thomas P. Devereaux
AU - Sandy A. Ekahana
AU - Alexei Fedorov
AU - Atsushi Fujimori
AU - Yucheng Guo
AU - M. Zahid Hasan
AU - Makoto Hashimoto
AU - Yu He
AU - Philip Hofmann
AU - Zahid Hussain
AU - Harold Y. Hwang
AU - Zhurun Ji
AU - Chunjing Jia
AU - Timur K. Kim
AU - Phil D. C. King
AU - Takeshi Kondo
AU - Alessandra Lanzara
AU - Chun Lin
AU - Donghui Lu
AU - Matteo Michiardi
AU - Sung-Kwan Mo
AU - Brian Moritz
AU - Laurenz Rettig
AU - Eli Rotenberg
AU - Takafumi Sato
AU - Kyle M. Shen
AU - Jonathan A. Sobota
AU - Inna Vishik
AU - Shuolong Yang
AU - Ming Yi
AU - Timothy Zaklama
PY - 2026
DA - 2026/10/11
PB - arXiv
DO - 10.48550/arxiv.2610.15236
UR - https://arxiv.org/abs/2610.15236
AB - Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization impedes this endeavor. As a premier measure of electronic structure underlying material properties, Angle-Resolved Photoemission Spectroscopy (ARPES) has accumulated over four decades of spectra, yet the community's publication-centered workflow preserves only a highly selected fraction of its experimental record. A shared data ecosystem could transform these fragmented measurements and their contexts into a persistent, collective scientific memory, creating immediate value for reproducibility and reuse while laying the foundation for AI-enabled discovery. In this Perspective, we report outcomes of a recent community workshop and propose a practical path toward such an ecosystem. Its benefits unfold along three timescales. In the near term, standardized, searchable, and citable datasets strengthen analysis pipelines independently of AI. In the intermediate future, community-validated tools operating across laboratories could enable broader reuse and increasingly closed-loop automated experiments. In the long term, a diverse corpus of measured electronic spectra could support foundation models for quantum matter. Looking forward, a diverse corpus of measured electronic spectra could support foundation models for quantum matter and new discoveries. We discuss lessons from existing scientific databases, the current state of AI in ARPES, as well as areas of community convergence on data organization, access, credit, governance, and a staged pilot program.
ER -