Periodic Reporting for period 2 - NOMAD CoE (Novel Materials Discovery)
Período documentado: 2022-10-01 hasta 2024-03-31
The Novel Materials Discovery (NOMAD) Centre of Excellence
Exascale computing will have a profound impact on everyday life in the coming decades. At 1018 operations per second, exascale supercomputers will be able to quickly analyze massive volumes of data and more realistically simulate complex processes. The goal of the NOMAD Center of Excellence is to bring computational materials science to the next level of supercomputing. The NOMAD CoE assesses and exploits the characteristics of extreme-scale data and exascale computing for computational materials science, to enable investigations of systems of higher complexity (space and time), consideration of metastable states and temperature, and all this at significantly higher accuracy and precision than what is possible today.
Systematic studies and predictions of novel materials to solve urgent energy, environmental, and societal challenges require such significant methodological advancements targeting the upcoming exascale computers. Key NOMAD examples are catalytic water splitting for hydrogen production and the transformation of waste heat into useful electricity.
Exascale computing will have a profound impact on everyday life in the coming decades. At 1018 operations per second, exascale supercomputers will be able to quickly analyze massive volumes of data and more realistically simulate complex processes. The goal of the NOMAD Center of Excellence is to bring computational materials science to the next level of supercomputing. The NOMAD CoE assesses and exploits the characteristics of extreme-scale data and exascale computing for computational materials science, to enable investigations of systems of higher complexity (space and time), consideration of metastable states and temperature, and all this at significantly higher accuracy and precision than what is possible today.
Systematic studies and predictions of novel materials to solve urgent energy, environmental, and societal challenges require such significant methodological advancements targeting the upcoming exascale computers. Key NOMAD examples are catalytic water splitting for hydrogen production and the transformation of waste heat into useful electricity.
The NOMAD CoE is organized in three topological pillars and overall thirteen work packages (WPs).
The goal of Pillar 1 is to advance ab initio computational materials science
for entire code families to enable tackling more complex problems
than what is possible today.
Efficient use of the exascale-ready libraries and codes developed in
Pillar 1 requires sophisticated workflows that are also capable of mana-
ging high-throughput computations and taking full advantage of
exascale resources. This is the focus of Pillar 2.
The overall aim of Pillar 3 is to utilize exascale technology to advance the
existing big data tools and bring them towards near-real-time performance
with a response time of seconds or less.
Horizontal activities support the three vertical research Pillars.
WP7 is dedicated to Data Infrastructure and makes the NOMAD data infrastructure exascale-ready.
The task of WP8 is the Co-Design across ab initio computational materials sciences (aiCMS) and HPC to deliver new inputs to HPC
architects. The aim is to ensure that the hard- and software developments go hand in hand.
We have Use-case Demonstrators (WP9) to test and demonstrate the NOMAD CoE developments in urgent energy and environ-
mental challenges. The NOMAD examples are catalytic water splitting for hydrogen production and waste heat recovery.
WPs10-13 add value to our research activities. The tasks include outreach to academia and industry, training, and administration.
More information on NOMAD work and WP objectives can be found here: https://www.nomad-coe.eu/nomad-coe/pillars-nomad-coe/overview-nomad-coe(se abrirá en una nueva ventana)
The goal of Pillar 1 is to advance ab initio computational materials science
for entire code families to enable tackling more complex problems
than what is possible today.
Efficient use of the exascale-ready libraries and codes developed in
Pillar 1 requires sophisticated workflows that are also capable of mana-
ging high-throughput computations and taking full advantage of
exascale resources. This is the focus of Pillar 2.
The overall aim of Pillar 3 is to utilize exascale technology to advance the
existing big data tools and bring them towards near-real-time performance
with a response time of seconds or less.
Horizontal activities support the three vertical research Pillars.
WP7 is dedicated to Data Infrastructure and makes the NOMAD data infrastructure exascale-ready.
The task of WP8 is the Co-Design across ab initio computational materials sciences (aiCMS) and HPC to deliver new inputs to HPC
architects. The aim is to ensure that the hard- and software developments go hand in hand.
We have Use-case Demonstrators (WP9) to test and demonstrate the NOMAD CoE developments in urgent energy and environ-
mental challenges. The NOMAD examples are catalytic water splitting for hydrogen production and waste heat recovery.
WPs10-13 add value to our research activities. The tasks include outreach to academia and industry, training, and administration.
More information on NOMAD work and WP objectives can be found here: https://www.nomad-coe.eu/nomad-coe/pillars-nomad-coe/overview-nomad-coe(se abrirá en una nueva ventana)
With the aim of advancing computational materials science to facilitate systematic studies and predictions of novel materials, the Novel Materials Discovery (NOMAD) Centre of Excellence (CoE) is working to address urgent energy, environmental and societal challenges. The list includes catalytic water splitting (hydrogen production) and the transformation of waste heat into useful electricity (search for efficient thermoelectric materials). The EU-funded NOMAD CoE project will develop a new level of materials modelling, enabled by upcoming exascale computing and extreme-scale data hardware. Results will be tested in two use cases. Among its many actions, NOMAD CoE will strive to train the next generation of students.
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.
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.