Success Stories


When all details matter: Heat Transport in Energy Materials

Researchers at the NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society have shed light of the microscopic mechanisms that determine thermal conduction in heat insulators. Powered by the advances made in the NOMAD CoE, their computational research has shown that even short-lived and microscopically localized defect structures have a substantial impact on macroscopic transport processes. This discovery could contribute to more energy-efficient technologies by allowing for the tailoring of nanoscale thermal insulators through defect engineering.

published 27.06.2023
High-Throughput DFT Calculations will Guide the Development of Nanoengineered Brain Sensors

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.

published 04.07.2022
High-throughput workflows with ASR and MyQueue

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. 

published 28.03.2022
Massively Parallel Coupled Cluster Theory Calculations for Materials Science

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.

published 23.03.2022
New Nature Perspectives publication

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.

published 17.01.2022
Study published in Nature Communications

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).

published 03.01.2022
New Website for the NOMAD AI-Toolkit

The web service of the NOMAD Artificial-Intelligence (AI) Toolkit has been upgraded in its functionality and its look-and-feel. 

published 22.11.2021
Work by NOMAD CoE researchers published in MRS Bulletin

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.

published 11.01.2021