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NOMAD CoE

Financiado

Cerrado

Novel Materials Discovery

Predicting novel materials with specific desirable properties is a major aim of ab initio computational materials science (aiCMS) and an urgent requirement of basic and applied materials science, engineering and industry. Such mat... ver más

31/03/2024

UBER

5M€

Presupuesto del proyecto: 5M€

Líder del proyecto

HUMBOLDTUNIVERSITAET ZU BERLIN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 13 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-03-31

INFRAEDI-05-2020: Centres of Excellence in exascale c...

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Información adicional privada

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13 Participantes

UBER

742.16K€ | Lider

ADAPTING

367.31K€ | Participante

108.62K€ | Participante

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Duración del proyecto: 44 meses Fecha Inicio: 2020-07-08
Fecha Fin: 2024-03-31

Líder del proyecto

HUMBOLDTUNIVERSITAET ZU BERLIN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 5M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Predicting novel materials with specific desirable properties is a major aim of ab initio computational materials science (aiCMS) and an urgent requirement of basic and applied materials science, engineering and industry. Such materials can have immense impact on the environment and on society, e.g. on energy, transport, IT, medical-device sectors and much more. Currently, however, precisely predicting complex materials is computationally infeasible. NOMAD CoE will develop a new level of materials modelling, enabled by upcoming HPC exascale computing and extreme-scale data hardware. In close contact with the R&D community, including industry, we will • develop exascale algorithms to create accurate predictive models of real-world, industrially-relevant, complex materials; • provide exascale software libraries for all code families (not just selected codes); enhancing today’s aiCMS to take advantage of tomorrow’s HPC computing platforms; • develop energy-to-solution as a fundamental part of new models. This will be achieved by developing novel artificial-intelligence (AI) guided work-flow engines that optimise the modelling calculations and provide significantly more information per calculation performed; • offer extreme-scale data services (data infrastructure, storage, retrieval and analytics/AI); • test and demonstrate our results in two exciting use cases, solving urgent challenges for the energy and environment that cannot be computed properly with today’s hard- and software (water splitting and novel thermoelectric materials); • train the next generation of students, also in countries where HPC studies are not yet well developed. NOMAD CoE is working closely together with POP, and it is synergistically complementary to and closely coordinated with the EoCoE, ECAM, BioExcel and MaX CoEs. NOMAD CoE will push the limits of aiCMS to unprecedented capabilities, speed and accuracy, serving basic science, industry and thus society.

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