面向 AI 模型的 FAIR 原则及其在加速高能衍射显微术中的实际应用
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
AI总结：
本文提出面向 AI 模型的实用、可衡量 FAIR 原则，并构建整合光源、数据平台与 AI 超算资源的统一框架，以支持高能衍射显微术等领域的自主科学发现。
摘要：
一套简洁且可衡量的科学数据 FAIR（可发现、可访问、可互操作和可重用）原则，正在改变数据管理与管护的实践现状，为发现和创新提供支持并使其成为可能。借鉴这一举措，并认识到人工智能（AI）对科学与工程实践的影响，我们提出了一套实用、简洁且可衡量的 AI 模型 FAIR 原则。我们展示了如何在一个统一计算框架中创建和共享 FAIR 数据与 AI 模型，该框架结合了以下要素：阿贡国家实验室的先进光子源、Materials Data Facility、Data and Learning Hub for Science、funcX，以及阿贡领导计算设施（ALCF），尤其是 ThetaGPU 超级计算机和 ALCF AI Testbed 中的 SambaNova DataScale 系统。我们描述了如何利用这一与领域无关的计算框架来实现由 AI 驱动的自主发现。
英文摘要：
A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
