The growing availability of FAIR materials data creates new opportunities for machine learning and AI, but using these data effectively requires suitable datasets, models, and reliable ways to evaluate their performance.

FAIRmat Area F bridges materials data and data-driven methods by making relevant data accessible for model development and supporting machine learning across computational and experimental materials science. Through data exploration and integration, ML-assisted analysis, and community benchmarking, the Area enables researchers to develop, evaluate, and apply data-driven methods to materials-science challenges.

Objectives

• Make high-quality materials data accessible and usable for training machine-learning and AI models.
• Enable ML-assisted analysis of experimental data and real-time feedback in laboratory workflows.
• Establish reliable ways to evaluate and compare ML models for physics and materials science.
• Accelerate the adoption of robust data-driven methods for materials research and discovery.