Nicola Marzari holds the Chair of Theory and Simulation of Materials at the École Polytechnique Fédérale de Lausanne, where he is also the director of the MARVEL National Centre for Computational Design and Discovery of Novel Materials. He also currently holds an Excellence Chair at the University of Bremen. Previous appointments include the Toyota Chair for Materials Processing at the Massachusetts Institute of Technology, and the Statutory Chair of Materials Modelling at the University of Oxford, where he directed the Materials Modelling Laboratory.

Materials simulations have become powerful and widespread tools for scientific discovery and technological innovation, with billions of dollars spent worldwide every year in supporting the researchers deploying these simulations. Applications range from nanotechnology to planetary science, from additive manufacturing to fine chemicals, from semiconducting qubits to Li-ion batteries. Against this backdrop, it is remarkable how comparatively little we plan and invest as a scientific society in developing, supporting, validating and disseminating such a successful research paradigm.

The needs and resulting benefits are many, and go from verifying and validating the quantum engines in widespread use, to optimizing their performance on complex architectures, lowering the adoption threshold by enhancing usability and reliability, and integrating data and simulation services. I’ll contextualize this with the ongoing worldwide efforts and our own, dedicated to developing and supporting core quantum engines, the AiiDA and AiiDAlab environments needed to provide user-friendly automates simulations [1,2], and the Materials Cloud dissemination platform [3] for curated and raw FAIR data.

[1] Huber, S.P., Zoupanos, S., Uhrin, M. et al. AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance. Sci Data 7, 300 (2020). https://doi.org/10.1038/s41597-020-00638-4

[2] Yakutovich, A.V., Eimre, K., Schütt, M. et al. AiiDAlab – an ecosystem for developing, executing, and sharing scientific workflows, Computational Materials Science, 188, 110165 (2021). https://doi.org/10.1016/j.commatsci.2020.110165

[3] Talirz, L., Kumbhar, S., Passaro, E. et al. Materials Cloud, a platform for open computational science. Sci Data 7, 299 (2020). https://doi.org/10.1038/s41597-020-00637-5