The 7th edition of the FAIRmat newsletter is now available for download! Discover the latest project developments, explore new NOMAD tools, read an interview with our domain expert Esma Birsen Boydas, and find more exciting articles from the FAIRmat community. Download it now from our website!
LUMI Grand Challenge – Professor of Theoretical Physics, Kristian Sommer Thygesen and his development team at the Technical University of Denmark (DTU) went all-in to utilise the full capacity of the LUMI supercomputer.
Scientists from the NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society have recently proposed a workflow that can dramatically accelerate the search for novel materials with improved properties. They demonstrated the power of the approach by identifying more than 50 strongly thermally insulating materials. These can help alleviate the ongoing energy crisis by allowing for more efficient thermoelectric elements, i.e., devices that can convert otherwise wasted heat into useful electrical voltage.
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.
The paper Similarity of materials and data‑quality assessment by fingerprinting by Martin Kuban, Šimon Gabaj, Wahib Aggoune, Cecilia Vona, Santiago Rigamonti and Claudia Draxl appeared in the October 2022 MRS Bulletin.
Abstract
Identifying similar materials (i.e., those sharing a certain property or feature) requires interoperable data of high quality. It also requires means to measure similarity. We demonstrate how a spectral fingerprint as a descriptor, combined with a similarity metric, can be used for establishing quantitative relationships between materials data, thereby serving multiple purposes. This concerns, for instance, the identification of materials exhibiting electronic properties similar to a chosen one. The same approach can be used for assessing uncertainty in data that potentially come from different sources. Selected examples show how to quantify differences between measured optical spectra or the impact of methodology and computational parameters on calculated properties, like the density of states or excitonic spectra. Moreover, combining the same fingerprint with a clustering approach allows us to explore materials spaces in view of finding (un)expected trends or patterns. In all cases, we provide physical reasoning behind the findings of the automatized assessment of data.
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).
