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
Aug 5, 2018

Scheffler Plenary at CCP2018

NOMAD Coordinator Matthias Scheffler joined international experts in computational physics at CCP2018, the XXX IUPAP Conference on Computational Physics, to give a plenary talk on 'Data-Driven Materials-Science – Rethinking the Pursuit of Understanding'. 

CCP2018 was the 30th in the CCP conference series, continuing a long-standing tradition of bringing together the broad international computational physics community to present contemporary research results and consider the vision of where the field is going in future.

In his plenary, Matthias spoke about the discovery of:

  • improved or even novel - not just new - materials, or
  • hitherto-unknown properties of known materials

to meet a specific scientific or industrial requirement, one of the most exciting and economically important applications of high-performance computing to date. He discussed methods and applications to extract knowledge from the immense volume of data now available, in particular how to spot yet unseen patterns or structures in the data, by identifying the key atomic and collective physical actuators by compressed sensing and machine learning. 

The talk covered methodologies for describing and predicting 2D topological insulators, metal/insulator classification, catalytic CO2 activation, and more, many related to NOMAD's Big-Data Analytics activities.