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
May 29, 2018

Prof. M. Scheffler speaks to BASF about the new NOMAD way of understanding data in materials discovery

NOMAD is driving a new way of understanding data in materials science, as NOMAD Coordinator Matthias Scheffler told key representatives at BASF, the largest chemical company in the world with a global presence in more than 80 countries worldwide. 

Matthias was invited by Ansgar Schäfer of our Industry Advisory Committee to talk about how NOMAD is rethinking the pursuit of understanding through data-driven materials science. In new and exciting ways, high-performance computing is being applied to the discovery of:

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

Such discoveries are immensely exciting and hugely important economically for industry, but are challenging due to the immense amount of data available. In a presentation highlighting the work of NOMAD, Matthias described how compressed sensing and machine learning can be used to spot yet unseen patterns or structures in massive collections of data by identifying the key atomic and collective physical actuators. The results of this Big-Data analysis are maps of materials, where different regions correspond to materials with different properties.

Concrete, industrially relevant examples were presented, including methods to describe and predict 2D topological insulators, classify metals/insulators classification, model catalytic CO2 activation, and more.

Check out the NOMAD Success Stories on Uncovering Structure-Property Relationships of Materials by Subgroup DiscoveryCO2 Conversion to Fuels and Other Useful Chemicals and many more to learn more about the work of NOMAD in this exciting domain.


NOMAD - modelling catalytic CO2 activation