NOMAD
NOMAD (Novel Materials Discovery Centre Of Excellence)
NOMAD will advance computational materials science to enable systematic studies and predictions of novel materials to address urgent energy, environmental, and societal challenges. Examples are catalytic water splitting (hydrogen production) and the transformation of waste heat into useful electricity (search for efficient thermoelectric materials). Such studies are infeasible with present concepts and computer codes but require significant methodological advancements targeting the upcoming exascale computers.
The NOMAD CoE is an important component of the NOMAD Laboratory which, again, is a component of the association FAIR-DI e.V. In general, FAIR-DI supports the establishment of a FAIR [1, 2] data infrastructure, to enable an efficient sharing of research data and to establish their Artificial Intelligence (AI) readiness.The NOMAD Repository & Archive, Encyclopedia and Artificial Intelligence (AI) Toolkit are NOMAD’s well-known flagship activities. They will be advanced in FAIR-DI, also considering more and more areas of computational materials science, experiments, and materials synthesis. The NOMAD CoE will continue to contribute to that, but now it concentrates on the assessment and exploitation of the characteristics of next generation high-perfomance computing for ab initio computational materials science. This will enable investigations of systems with higher complexity, consideration of metastable states and temperature, and all this at significantly higher accuracy and precision than what is possible today. For this, electronic-structure theory software will developed in terms of exascale libraries, as well as exascale workflows, exascale artificial intelligence tools and an advanced extreme-scale data infrastructure. More details can be found here.
- FAIR stands for findable, accessible, interoperable, and reusable; M.D. Wilkinson, et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
- C. Draxl and M. Scheffler, NOMAD: The FAIR Concept for Big-Data-Driven Materials Science. Invited Review for MRS Bulletin 43, 676-682 (2018). https://doi.org/10.1557/mrs.2018.208
