  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.
