NOMAD Laboratory
NOMAD Centre of Excellence

Bringing computational materials science to exascale

Exascale Codes

  • Bringing DFT, Green-function methods, and coupled-cluster theory to exascale
  • Supporting entire code families, covering planewaves (PW), linearized augmented PWs, and atom-centred orbitals
  • Follow us on GitHub

Exascale Workflows

  • Enabling exascale computations by advanced workflows
  • Covering high-throughput computations and beyond-DFT workflows
  • Learn how to work with ASE/ASR and FireWorks in this tutorial

Extreme-scale data

  • Advance the NOMAD AI toolkit and bring it towards near-real-time performance
  • Like to visit the NOMAD Laboratory and its services for up- and downloading, and exploring materials data? 
  • Watch our video tutorials to learn how to work with the AI toolkit
Jan 3, 2022

NOMAD CoE study on the Design of Catalytic Oxid Materials by the AI Approach Subgroup Discovery accepted in Nature Communications

NOMAD CoE researchers Aliaksei Mazheika, Yanggang Wang, Luca Ghiringhelli and Matthias Scheffler together with a team of researchers from the University of Barcelona, Spain, and the Skolkovo Institute of Science and Technology, Moscow, Russia, have successfully applied the AI method subgroup discovery to identify "material genes" that can trigger, facilitate, or inhibit the activation of carbon dioxide (CO2) for chemical conversion. 

You can find more details here.