FAIR-DI European Conference on Data Intelligence 2024

The development of new materials is a critical issue for industrialized societies, with relevance for many different application areas, including but not limited to energy, environment, information technologies, as well as life sciences. The search for new materials is a challenging task, considering that most conceivable materials have not yet been experimentally realized. In fact, the number of possible materials is so large that humanity will never be able to synthesize (and experimentally characterize) all of them.

Therefore, the development of new strategies to accelerate the discovery of new materials is very important. We, the FAIR-DI association and the consortium FAIRmat, see an opportunity to accelerate this process by applying digitalization strategies to materials research, using advanced computational approaches together with sophisticated experimental efforts, including unsupervised experimental procedures. This is a highly interdisciplinary challenge, requiring input from specialists with expertise in experimental and theoretical materials science, as well as contributions from computer science, synthetic chemistry, engineering, and the application of advanced characterization methods. In recent decades, materials science has made impressive progress in the development of both experimental and theoretical methods, along with the ever-increasing computational power. Taken together, these new developments will enable a paradigm shift in the development of new materials systems. The clear goal is to replace traditional trial-and-error research with more sophisticated efforts in which computational simulations are performed in parallel (and even simultaneously) with novel experimental approaches, including autonomous and robot-aided experimentation. 

Prerequisite for making this happen is the availability of well-characterized, high-quality data. Therefore, easy-to-use tools for data handling, i.e., processing, storage, analysis, accessibility, visualization, and error quantification play a key role. These aspects of data infrastructure – the core topic of FAIR-DI – will be addressed.

In this context, it is important to note that the launch of the National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur e.V., NFDI) initiative serves as a strong catalyst for the digitalization of both pure and applied sciences. Indeed, several NFDI consortia are at least partly focused on exploring new approaches in materials science. The FAIR-DI 2024 conference is chiefly organized by members from the non-profit association FAIR-DI and the NFDI consortium FAIRmat. However, it will also hold significance for other consortia, including NFDI4Ing, NFDI-MatWerk, DAPHNE4NFDI, and NFDI4Chem.