LUMI at full capacity helps research into new materials for solar cells, catalysts and quantum technology

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.

published 18.12.2023
Using Artificial Intelligence to guide the high-throughput search for new materials

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.

published 01.11.2023
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
New publication

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.

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.

published 19.09.2022
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
8 Million Euro Grant from the Novo Nordisk Foundation

Kristian Thygesen (WPs 4, 5 & 9), is part of a team at the Technical University of Denmark (DTU) that has just received an 8 million EURO (60 million DKK) grant from the Novo Nordisk Foundation. The BIO-MAG project focuses on developing new materials for highly sensitive magnetic field sensors for neuroimaging. Kristian leads the theoretical activities of the project, which is funded over six years starting January 2022.

Kristian is part of a team led by Nini Pryds (Department of Energy Conversion and Storage at DTU) that will develop 2D materials for two novel sensing technologies that can capture images of the brain at room temperature with tremendous sensitivity and spatial resolution. This will allow nervous system disorders to be detected and treated much earlier. The goal is to allow general practitioners to use the technology and perform imaging directly in their practices. The technology under development could become an alternative to expensive magnetic resonance imaging (MRI), which is usually only available in hospitals.

The goal of the Novo Nordisk Foundation Challenge Program is to support excellent researchers in addressing major societal challenges in the fields of health, sustainability or biotechnology.

published 15.12.2021
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
Plenary talk of Matthias Schefffler at the 9th International Symposium on Surface Science (ISSS9)

Toward Sustainable Development

Matthias Scheffler (NOMAD CoE coordinator) gave a plenary talk titled "Artificial Intelligence for Surface Science and Heterogeneous Catalysis: Learning Rules and Creating Maps of Materials Properties" at the ISSS9 virtual conference (Nov. 28 - Dec. 01, 2021). The meeting was organized by the Japan Society of Vacuum and Surface Science (JVSS) and highlights recent achievements in surface science and its related fields. ISSS9 was originally planned as a face-to-face conference but had to be changed to an online format due to the pandemic.

published 08.11.2021
NOMAD PI appointed as Villum Investigator

NOMAD PI Kristian Sommer Thygesen appointed as Villum Investigator

The Danish VILLUM Foundation has appointed NOMAD PI Kristian Sommer Thygesen as Villum Investigator with a grant of 30 Mio DDK (4 Mio Euro) for the project “Data-Driven Discovery of Functionalized 2D Materials”.

published 19.04.2021
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