arXiv：cs.LG· Sandy Adhitia Ekahana, Aalok Tiwari, Pratik Saud, Aaron Bostwick, Chris Jozwiak, Eli Rotenberg, Jyoti Katoch·· 4 小时前AI 评分22
AI 在 ARPES 工作流中的进展与展望
Progress and Prospect of AI in ARPES Workflow
AI 导读
这篇综述系统梳理了机器学习在 ARPES 全工作流中的应用，覆盖自动化样品制备、实时数据采集、实验后数据分析及与理论计算对比等环节，并逐步骤讨论其优势与局限。作者指出当前 ARPES 开放数据库规模小且碎片化，远不及 ImageNet 等共享数据集，建议社区共享可再训练、可适配并再分发的预训练模型，同时推进更大规模、标准化的开放 ARPES 数据仓库。
正文
Abstract:Artificial intelligence (AI) is becoming an increasingly useful tool across the experimental sciences, including angle-resolved photoemission spectroscopy (ARPES), which routinely produces large, multidimensional datasets of electronic structure. Recent advances in AI and machine learning (ML) have opened new opportunities across the entire ARPES workflow, from automated sample preparation and real-time data acquisition to post-experiment data analysis and comparison with theoretical calculations. Despite this progress, a comprehensive review of ML applications, their capabilities, and reliability across the different stages of ARPES workflow is still lacking. In this review, we first introduce ML methods that are most relevant to experimentalists working in condensed matter physics and materials science. We then follow the ARPES workflow, reviewing existing ML applications at each step and discussing their advantages, limitations and potential for future development. We also examine the current ARPES data landscape, where several open databases are available but remain relatively small and fragmented compared with large, shared datasets such as ImageNet. Given these limitations, we suggest that the community focus on sharing pretrained models that can be further trained, adapted to specific tasks, and redistributed, while working toward a larger and standardized open ARPES dataset repository. Finally, we discuss our perspectives on the future of AI within the ARPES workflow using a six-level framework of laboratory automation, highlighting the opportunities and challenges in moving toward a fully autonomous, self-driving ARPES laboratory.
| Subjects: | Other Condensed Matter (cond-mat.other); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an) |
| Cite as: | arXiv:2610.10140 [cond-mat.other] |
| (or arXiv:2610.10140v1 [cond-mat.other] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10140 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sandy Adhitia Ekahana [view email]
[v1]
Wed, 7 Oct 2026 14:18:50 UTC (4,464 KB)
来源：arXiv：cs.LG · arxiv.org