FAIR-DI e.V.
FAIRmat
NOMAD Laboratory
NOMAD CoE
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
Apr 15, 2018

Emerging Technologies in Scientific Data Visualisation


Pasquale Pavone, HU Berlin, gave an invited talk 'Accessing Big Data: The NOMAD Encyclopedia' at the Emerging Technologies in Scientific Data Visualisation workshop in Pisa, Italy, April 4–6, 2018.

The workshop, co-sponsored by CECAM and Psi-k, focused on visualization of materials Big-Data, bringing together data producers, data analysts and visualization technologists to answer the following open questions:

  • Data Producers: What are the emerging visualization needs for Big Data; how are they different than scaled-up versions of existing tools?

  • Data Analysts: How to enhance current analysis tools or create new ones with visualization? What visual analytics techniques, representations and mapping methods can we borrow from other fields now that the molecular simulations can produce a variety of data other than molecular representations?

  • Technologists: How can we better use the developing technologies such as Virtual Reality, haptic feedback mechanisms, graphical artificial neural networks, and computer vision to reveal patterns and relationships that were previously not exposed to visualization at all? How can computational material science benefit from technologies that target interaction with data?