The Novel Materials Discovery Laboratory (NoMaD) (H2020-EINFRA-5-2015, Centers of Excellence for Computing applications)

European Union

Status: ongoing project
Contract Number:
676580

Essentially every new commercial product, be they smart phones, solar cells, batteries, transport technology, artificial hips,etc., depends on improved or even novel materials. Computational materials science is increasingly influential as a method to identify such critical materials for both R&D. Enormous amounts of data, precious but heterogeneous and difficult to access or utilise, are already stored in repositories scattered across Europe. The NoMaD CoE will open new HPC opportunities by enabling access to this data and delivering powerful new tools to search, retrieve and manage it.NoMaD will foster sharing of all relevant data, building on the unique CECAM, Psi-k and ETSF communities, putting Europe ahead of materials science in other continents. Unprecedented, already initialised networking with researchers, with industry,with students and with other stakeholders will guarantee relevance and end-user value. NoMaD will become a crucial tool
for atomistic simulations and multi-scale modelling in the physical, materials, and quantum-chemical sciences. This field is characterised by a healthy but heterogeneous eco-system of many different codes that are used at all HPC centers worldwide, with millions of CPU hours spent every day, some of them at petascale performance. NoMaD will integrate the leading codes and make 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:NoMaD will develop “big-data analytics” for materials science. This will require 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.

The Max Planck Society's press group decided to put NOMAD as "hot topic" on the MPG home page:http://www.mpg.de/de

First newsletter: https://nomad-coe.eu/uploads/outreach/Newsletter/NOMAD_2017_Newsletter_A...

The NOMAD (Novel Materials Discovery) Laboratory, a European Center of Excellence, https://nomad-coe.eu, recently released a 3-minute video describing its goals in simple terms: https://youtu.be/yawM2ThVlGw .
We are also running a hands-on workshop on the so far developed tools and services: http://meetings.nomad-coe.eu/nomad-summer-2017/

As part of the outreaching activities, NOMAD aims to attract interest of the Industry sector. On this way, we provided an Industrial Training Workshop with TECNALIA(http://www.tecnalia.com/en/) make visible the NOMAD project and prepare future networks. (http://nano-bio.ehu.es/system/files/private/jjornetsomoza_nomad-tecnalia...)

Many more novel concepts and methods need to be developed. It is an exciting time where the field is starting a totally new research direction. Outstanding students and postdocs are encouraged to send their application material to Hanna Krauter. They are also welcome to contact the PI or Work Package leader (see TEAM) of the area they like to join. Applicants should have knowledge in one or more of these fields: computational informatics, statistical learning, compressed sensing, electronic-structure theory, statistical mechanics.

Objectives

The specific objectives for the CoE are as follows:
1. To convert, compress and integrate the scattered,unconnected major collections of materials­‐science data into a single code-­‐independent database.To assign error bars to the data.
2. To enable powerful new search and information retrieval tools (e.g.advanced graphics and statistical learning) on the code-­‐independent big data of materials-­‐science,taken full (and
needed)advantage of advanced HPC infrastructure and technologies.
3. To research,develop and deliver new tools and technologies for big data handling and analytics
to enable discovery of novel materials with targeted,unique properties.
4. To develop a suite of services based on these capabilities,available to engineers and materials
scientists in industry and academia,thereby improving access to computing applications,data and expertise,enhancing productivity and enabling scientific excellence.
5. To deliver direct benefit to industry (including SMEs), through novel materials discovery,
engineering and modelling, and by making CoE expertise available to address industry research
challenges.
6. To offer “computations on demand” for materials properties not yet accessible by the database.
7. To build a long-­‐term multi-­‐disciplinary highly skilled community of materials scientists,
engineers and HPC experts, with a shared understanding of their respective aims,priorities and
constraints,and a commitment to collaboration,interlinked with existing networks and communities.
8. To create a computational environment that enables and stimulates people from industry and academia to create their own software and applications (“Apps”) that discover,model and utilise
special properties and functions of materials that are missing so far.
9. To create a pool of computational materials scientists, with strong HPC experience,available
for academic or industrial careers.To feed into under-­‐graduate and post-­‐graduate training across
the consortium and beyond (this includes most of the leading materials-­‐science research groups
in Europe).
10. To establish a sustainable,long-­‐term centre of excellence,with a supportive governance model
which combines excellent science with industrial relevance.

Participants

Max Planck Gesellschaft (MPG)-Germany-Prof.Matthias Scheffler-Stefan Heinzel-Prof.Angel Rubio
King’s College London (KCL)-UK-Prof.Alessandro De Vita
Humboldt University Berlin (HUB)-Germany-Prof.Claudia Draxl
University of Cambridge (CAM)-UK-Prof.Daan Frenkel
University of Barcelona (UB)-Spain-Prof.Francesc Illas
Aalto University,Helsinki (AALTO)-Finland-Prof.Risto Nieminen
Technical University of Denmark,Lyngby (DTU)-Denmark-Prof.Kristian Sommer Thygesen
Leibniz-­‐Rechenzentrum,Garching (BADW-­‐LRZ)-Germany-Prof.Arndt Bode
Center for Scientific Computing,Espoo (CSC)-Finland-Dr.Kimmo Koski
Barcelona Supercomputing Center (BSC)-Spain-Prof.Jose Maria Cela
Pintail Ltd (PT)-Ireland- Ciaran Clissmann

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