The Novel Materials Discovery Laboratory (NoMaD)


Essentially every new commercial product – be it a smart phone, solar cell, battery, transport technology, artificial hip, etc. – depends on improved or even novel materials. Computational material science is increasingly influential as a method to identify such critical materials for both research and development. Enormous amounts of data, precious but heterogeneous and difficult to access or utilise, are already stored in repositories scattered across Europe. The NoMaD Centre of Excellence (CoE) opens new HPC opportunities by enabling access to this data and delivering powerful new tools to search, retrieve and manage it. NoMaD fosters the sharing of all relevant data, building on the unique CECAM, Psi-k and ETSF communities and thus putting Europe ahead of materials science in other continents. For this, NoMaD integrates the leading codes and makes their results comparable by converting (and compressing) existing inputs and outputs into a common format, thus making these valuable data accessible to academia and industry.

Additonally, NoMaD develops “big-data analytics” for materials science. This requires novel algorithms, e.g., for statistical learning based on the created materials encyclopedia, offering complex searches and novel visualisations. These challenges exploit the essential resources of our HPC partners. Without the infrastructure and services provided by the NoMaD CoE, much of the information created with the above mentioned petascale (towards exascale) computations would be wasted.

Against this background LRZ will be engaging in the field of interactive remote visualisation and advanced interaction with multi-dimensional data sets and therefore leverage its expertise and V2C facilities.

Events and Conferences

Big Data of Material Science, CECAM Confernce, Nov 30 – Dec 4 2015, Lausanne, Switzerland

Fast Facts

Project Duration

01/11/2015 - 31/10/2018

Contact person

Dr. Anton Frank

Funding agency

European Commission (H2020 Funding Scheme)


Partner Institutions

  • Coordinator: Fritz Haber Institute of Max Planck Society
  • Aalto University
  • Barcelona Supercomputing Centre
  • CSC – Centre for Scientific Computing
  • Humboldt University Berlin
  • King’s College London
  • Leibniz Supercomputing Centre
  • Max Planck Computing and Data Facility
  • Max Planck Institute for the Structure and Dynamics of Matter