The number of serious brain disorders and deaths worldwide caused by diseases of the nervous system has risen sharply in recent decades. Despite huge advances in neuroscience over the past century, our understanding of the brain is still far from complete. To understand the causes and to aid the growing number of affected people, we need to be able to study the brain more closely. New tailored sensors measuring small electromagnetic fluctuations produced by active neurons could contribute to rapidly developing treatments for brain disorders.
The Atomic Simulation Recipes (ASR) is an open source Python framework for working with atomistic materials simulations in an efficient and sustainable way that is ideally suited for high-throughput computations. ASR contains a library of recipes, or high-level functions, that define specific atomistic simulations tasks using the Atomic Simulation Environment (ASE). The recipes can be combined into workflows that perform complex simulation tasks while keeping track of relevant metadata to ensure documentation and reproducibility of the data. The ASR also contains functionality for collecting the resulting data into databases and presenting them in a browser.
NOMAD CoE researchers from TU Wien and the Fritz Haber Institute have developed novel computer codes to enable massively parallel and highly accurate coupled cluster theory simulations of materials.
In a newly accepted Nature Perspective article Matthias Scheffler and colleagues describe the challenges of establishing a FAIR (Findable, Accessible, Interoperable, and Re- usable) data infrastructure.
Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides
A. Mazheika, Y. Wang, R. Valero, F. Vines, F. Illas, L. Ghiringhelli, S. Levchenko, and M. Scheffler of the NOMAD Laboratory of the Fritz Haber Institute developed and advanced artificial intelligence methods that enable the identification of basic materials parameters that correlate with materials properties and functions of interest (here the activation of CO2).
The web service of the NOMAD Artificial-Intelligence (AI) Toolkit has been upgraded in its functionality and its look-and-feel.
A tailored AI Approach for Heterogeneous Catalysis
NOMAD CoE researchers Lucas Foppa, Luca M. Ghiringhelli, Matthias Scheffler and other colleagues have developed a customized artificial intelligence approach for modeling of heterogeneous catalysis. This method takes into account the key physicochemical parameters that are correlated with catalytic performance to accelerate the discovery of improved or novel materials. The study was published in the prestigious high-ranking journal MRS Bulletin in November 2021.