Autonomous and combinatorial high-throughput exploration methods in solid-state chemistry

Juillet 2022 - Juin 2025

Partenaire IMN du projet : Romain GAUTIER   (équipe MIOPS)

Coordinateur :
Laboratoire Structures Propriétés et Modélisation des Solides
Partenaire :
'Institut de Chimie de la Matière Condensée (ICMCB Bordeaux)

Personnels IMN impliqués :
Thomas DEVIC (DR CNRS), Florian MASSUYEAU (IR CNRS)

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In recent years, innovative approaches were developed to accelerate the discovery of novel materials: the combinational approach and autonomous research approach The combinational approach allows the production of materials libraries that formally include several hundreds, even thousands, of compositions. Autonomous research implies the deployment of materials which include synthesis and characterisation modules for automated structural and functional properties, and using AI models to select the areas to be explored. Such a combination allows the exploration of complex multidimensional areas to optimise the composition of materials without human action. Europe is lagging behind the United States and China in this research area. Under this targeted project, our objective is both to use high-throughput combinatorial approaches and to develop autonomous configurations to explore material composition spaces for low-energy applications. In practical terms, we will apply the approaches proposed to accelerate the discovery of oxide materials for smart windows, smart sensors, low-consumption lighting and electronic systems. The parallel combinatorial approach will be implemented and completed with AI models. Interactions with other targeted projects will help extend this oxide strategy to other types of materials. Finally, in addition to automating the discovery of novel materials, this strategy should help researchers gain statistically sound knowledge and an understanding of the composition-(micro)structure-property relationship.