Scientific data are a significant raw material of the 21^st century. To exploit their value, a FAIR – Findable, Accessible, Interoperable, and Re-purposable – data infrastructure (DI) is a must. Making data Findable and AI Ready (an alternative interpretation of the acronym) will change the way in which science is done today. For the wider field of condensed-matter physics and the chemical physics of solids, FAIRmat sets out to make this happen. Integrating synthesis, experiment, theory, computations, and applications, it will substantially further the basic physical sciences, reaching out to chemistry, engineering, industry, and society.

Why are FAIRmat’s research data so important? Simply speaking, the prosperity and lifestyle of our society are very much governed by achievements of this field as new products from the energy, environment, health, mobility, IT sectors, etc. rely on improved or novel materials. Examples are solid-state lighting, touch screens, batteries, implants, and many more. Without an efficient DI, the data are neither easily accessible nor re-usable.

FAIRmat represents a broad community of numerous researchers from universities and leading institutions in Germany. It builds on extensive experience with the worldwide biggest data infrastructure in computational materials science, the Novel Materials Discovery (NOMAD) Laboratory and the association FAIR-DI e.V. FAIRmat aims at covering the full breadth of the Condensed Matter Section of the German Physical Society with its 12 divisions, and is further supported by the Chemistry, Physics, and Technology Section of the Max Planck Society, the Bunsen Society for Physical Chemistry, and more. It is fully embedded internationally, e.g., in the RDA, the EOSC, GO FAIR, etc. and has signed Memoranda of Understanding with leading institutions worldwide, for example NIST (USA), Shanghai University (China), and CSC (Finland). FAIRmat will continue to raise awareness and acceptance of a FAIR research-data infrastructure in Germany, Europe, and beyond.

Area A- Synthesis

aims at making sample synthesis reproducible, finding hidden synthesis parameters, accelerating the development of novel materials by devising synthesis recipes, and making characterization data of synthesized materials assessable.

Area B – Experiment

will implement FAIR data principles in key experimental condensed-matter physics diciplines and make their adaption very appealing, simple, and popular.

Area C – Theory

will integrate the NOMAD Laboratory, significantly enhance its infrastructure and services by adding recent ab initio advancements, classical simulations, multi-scale modelling, and excitations, and prepare the incorporation of other theoretical methods.

Area D – Data Infrastructure

will lay the foundation of a federated research data infrastructure for the wider condensed-matter and chemical-physics community.

Area E – Use-case Demonstrators

will demonstrate the functionality of the FAIRmat DI and possibly identify weaknesses to be improved. It will show how the developed DI tools will significantly support the research of the various sub-communities and highlight handshakes with other consortia.

Area F – User Support, Training, and Outreach

reaches out to the community, provides ample information and training, fosters awareness for FAIR-data issues, and returns suggestions to Areas A-E for improvement.

Area G – Administration and Coordination

is dedicated to coordination issues – both within FAIRmat and within the NFDI.