论文标题
AI模型的公平原理，具有加速高能衍射显微镜的实际应用
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
论文作者
论文摘要
科学数据的一套简洁且可衡量的公平（可访问，可互操作和可重复使用的）原则正在转变用于数据管理和管理的实践，以支持和支持发现和创新。从这项倡议中学习，并承认人工智能（AI）在科学和工程实践中的影响，我们引入了一套实用，简洁和可衡量的AI模型的公平原则。我们展示了如何在统一的计算框架内创建和共享公平的数据和AI模型，结合了以下元素：Argonne国家实验室的高级光子源，材料数据设施，科学数据和FunCX的数据和学习中心以及Argonne领导力计算设施（ALCF），尤其是Thetagpupupupupuputer superComputer and Sambanova datcff datcff。我们描述了如何利用这种域 - 不足的计算框架来实现自主AI驱动的发现。
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.
