FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
This work addresses the need for standardized and reusable AI models in scientific research, though it appears incremental as it extends existing FAIR principles from data to AI models.
The authors introduced a set of FAIR principles for AI models, adapting them from data management to enhance AI-driven scientific discovery, and demonstrated their application in a unified computational framework for high energy diffraction microscopy.
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.