面向 AI 就绪的 ARPES 社区数据库框架

Condensed Matter > Materials Science

arXiv:2610.15236 (cond-mat)

Authors:Zhi-Xun Shen, Jin Gene Wong, Karen Lei, Leon Garcia, Felix Baumberger, Sergey Borisenko, Aaron Bostwick, Ting Cao, Tanny Chavez, Yulin Chen, Monika Choudhary, Andrea Damascelli, Dan Dessau, Thomas P. Devereaux, Sandy A. Ekahana, Alexei Fedorov, Atsushi Fujimori, Yucheng Guo, M. Zahid Hasan, Makoto Hashimoto, Yu He, Philip Hofmann, Zahid Hussain, Harold Y. Hwang, Zhurun Ji, Chunjing Jia, Timur K. Kim, Phil D. C. King, Takeshi Kondo, Alessandra Lanzara, Chun Lin, Donghui Lu, Matteo Michiardi, Sung-Kwan Mo, Brian Moritz, Laurenz Rettig, Eli Rotenberg, Takafumi Sato, Kyle M. Shen, Jonathan A. Sobota, Inna Vishik, Shuolong Yang, Ming Yi, Timothy Zaklama

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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.

Comments:	Preprint under review
Subjects:	Materials Science (cond-mat.mtrl-sci); Strongly Correlated Electrons (cond-mat.str-el); Superconductivity (cond-mat.supr-con); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.15236 [cond-mat.mtrl-sci]
	(or arXiv:2610.15236v1 [cond-mat.mtrl-sci] for this version)
	https://doi.org/10.48550/arXiv.2610.15236 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jin Gene Wong [view email]
[v1] Sun, 11 Oct 2026 00:12:54 UTC (7,797 KB)

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