Materials Simulation, Friedrich Alexander University, Erlangen-Nürnberg, Germany
AI-Ready materials-science FAIR data: methods and infrastructure
To accelerate the identification and design of optimal materials for a desired property or process, strategies for a well-guided exploration of the materials space are highly needed. A desirable strategy would be to start from experimental or theoretical data, and by means of artificial-intelligence (AI), to identify yet unseen patterns in the data, and consequentially predictive data-driven models. This leads to the identification of materials' (properties) maps.
Here, I present recent updates on novel methods for the AI-aided identification of descriptors and materials maps, tailored to work (also) with "small-data", and applied to important materials-science challenges such as the prediction of mechanical properties of perovskite materials, of catalytic properties of experimentally characterized materials, and more.
Furthermore, I will introduce the FAIRmat consortium and the NOMAD infrastructure, for the FAIR storage and stewardship of materials-science data. I will focus on the AI-toolkit, an online platform for publishing and sharing curated Jupyter notebooks for the tutorial introduction of text-book and novel AI tools and for providing an interactive access to AI workflows as published in peer-reviewed journals. In this way, people can fully benefit of the community's advancements and reproducibility in science can meet its full potential.