发表机构
Carnegie Mellon University; Lawrence Berkeley National Laboratory（卡内基梅隆大学; 劳伦斯伯克利国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了AI/ML在ARPES全流程中的应用，分析了各阶段能力与局限，提出共享预训练模型并展望六级自动化框架下的自驱动实验室。
AI 中文摘要
人工智能（AI）正日益成为实验科学中一种有用的工具，包括角分辨光电子能谱（ARPES），该技术常规地产生电子结构的大规模、多维数据集。AI和机器学习（ML）的最新进展为整个ARPES工作流程开辟了新的机遇，从自动化样品制备和实时数据采集到实验后的数据分析以及与理论计算的比较。尽管取得了这些进展，但针对ML应用及其在不同ARPES工作流程阶段的能力和可靠性的全面综述仍然缺乏。在本综述中，我们首先介绍与凝聚态物理和材料科学领域的实验人员最相关的ML方法。然后，我们沿着ARPES工作流程，回顾每个步骤中现有的ML应用，并讨论其优势、局限性和未来发展的潜力。我们还考察了当前ARPES数据格局，其中存在几个开放数据库，但与诸如ImageNet等大型共享数据集相比，这些数据库仍然相对较小且分散。鉴于这些限制，我们建议社区专注于共享预训练模型，这些模型可以进一步训练、适应特定任务并重新分发，同时努力构建一个更大且标准化的开放ARPES数据集存储库。最后，我们使用实验室自动化的六级框架讨论了对AI在ARPES工作流程中未来的看法，强调了迈向全自动、自驱动ARPES实验室的机遇和挑战。
英文摘要
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.
