FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

TL;DR

This paper proposes practical FAIR principles tailored for AI models, demonstrating their application in high energy diffraction microscopy to facilitate data sharing, interoperability, and AI-driven scientific discovery.

Contribution

It introduces measurable FAIR principles specifically for AI models and illustrates their implementation within a unified computational framework for scientific research.

Findings

Successful creation and sharing of FAIR AI models and data

Integration of multiple computational platforms for AI-driven discovery

Framework enables autonomous scientific research processes

Abstract

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.