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DIADEM

Published on January 14, 2022
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DIscovery Acceleration for the Deployment of Emerging Materials

DIADEME

DIADEME


Accelerate the design and production of materials that are more efficient, sustainable and which originate from non-critical and non-toxic raw materials is the goal of DIADEM project. This project, in which SIMaP lab is strongly involved, is one of the 4 "Priority Research Programs and Equipments" selected as part of the first wave of the PIA4 call.

 
Materials are at the core of innovation and they strongly determine industrial competitiveness. In the Green Deal context, our industry must include its growth in an ethical model taking into account economic, environmental and societal trends. An ambitious project paving the way for the acceleration of the design and production of materials that are more efficient, sustainable and which originate from non-critical and non- toxic raw materials would play a central role in the strategic development of the French economy. Such a program must integrate modelling, numerical simulation, high throughput synthesis/screening and characterization technologies. The goal is to design novel materials for given specifications at a speed unattainable in the usual process of discovery, where breakthroughs are often unpredictable. The acceleration of novel material design requires the integration of teams/centers of expertise that combine combinatorial synthesis, shaping and high-throughput characterization platforms, coupled with multiscale numerical modelling, data mining and machine learning, with artificial intelligence (AI) at all steps. These platforms will first be dedicated to critical materials and must result in a 2- to 5-fold acceleration of materials discovery from about 20 years to between 4 to 10 years.

Such platforms should include:
  1. Combinatorial and/or high-throughput synthesis. The various additive manufacturing processes are key technologies for the fast processing of new material compositions (metals, ceramics, polymers possibly bio- based) but also of novel architectures that are eventually tunable with a stimulus, as in the case of 4D printing. Thin film engineering is often mandatory to reach targeted functionalities. A particular focus should also be directed on architectured materials, composites, hybrids and bio-based materials to maximize structural properties and functionalities: organic or hybrid organic-inorganic semi-conductors, hybrid functional materials, heterostructures, quantum dots, nano-objects, etc. The scale up from lab scale will pave the way for a new generation of industrial processes that are faster and have less environmental impact.
  2. High-throughput characterization: compositional, structural (large-scale facilities Soleil and ESRF), functional properties (optical, magnetic, electrical, mechanical, corrosion resistance), development of sensors for in situ or operando characterization which are essential for high throughput, and the generation of large databases, in particular in severe conditions.
  3. Data bases for gathering, processing and managing by using AI massive data fluxes. Experiments and modelling will need increased automation. A strategy will be established: connection of existing local data bases versus launching a national one. Specific AI tools will be developed to improve the management of experimental and simulation data.
  4. Numerical design and simulation: multiscale material modelling from ab initio to macroscale, including Artificial Intelligence, will be more user-friendly, interoperable and integrated in workflows to allow automatic and/or high-throughput calculations.

From these 4 platforms, especially from their synergy, an innovation renewal in materials science will emerge.

Scientific pilotes: CEA, CNRS
Partners: Université Paris-Saclay, Sorbonne Université, Institut Polytechnique de Paris, Université Grenoble-Alpes, Université de Lorraine, Université de Bordeaux, Université de Lyon

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Date of update January 14, 2022

Université Grenoble Alpes