MatheGram focused on thermomechanical behaviour of granular materials including sintering and particle engineering; CoPerMix addressed mixing and blending processes.
SAINT GOBAIN RECHERCHE
Saint-Gobain's central R&D lab specializing in granular materials, powder technology, and AI-driven process control for industrial manufacturing.
Their core work
Saint-Gobain Recherche is the central R&D hub of the Saint-Gobain Group, one of the world's largest building materials and high-performance materials manufacturers. Their H2020 involvement focuses on advanced materials science — particularly granular and powder materials, thermomechanical behavior, mixing processes, and calcium-silicate-hydrate systems used in construction. They bring deep industrial expertise in translating fundamental materials research into scalable manufacturing processes, serving as a bridge between academic research networks and large-scale industrial application.
What they specialise in
CoPerMix applied reinforcement learning and statistical mechanisms to control and predict mixing, stirring, and diffusion processes.
ERICA project engineered calcium-silicate-hydrates for industrial applications, directly relevant to Saint-Gobain's core construction materials business.
Participated as third party in OxiGEN, contributing materials expertise to next-generation SOFC stack development.
MatheGram included additive manufacturing and discrete element modelling as key research themes, suggesting growing interest in this area.
How they've shifted over time
Early H2020 involvement (2017-2018) centered on established materials domains — engineered construction materials (ERICA) and energy materials for fuel cells (OxiGEN), reflecting Saint-Gobain's traditional product lines. From 2019 onward, the focus shifted decisively toward computational and data-driven materials science: thermomechanical modelling, discrete element methods, and notably reinforcement learning applied to mixing processes (CoPerMix). This signals a clear move from purely experimental materials R&D toward AI-augmented process control and simulation.
Saint-Gobain Recherche is integrating machine learning and computational modelling into its traditional materials science expertise, positioning itself at the intersection of industrial manufacturing and AI-driven process optimization.
How they like to work
Saint-Gobain Recherche consistently participates as a partner or third party — never as coordinator — reflecting their role as an industrial contributor bringing real-world manufacturing context to academic-led consortia. With 41 unique partners across 9 countries from just 4 projects, they engage in large, diverse consortia rather than tight bilateral collaborations. This pattern is typical of a large corporate R&D lab that selectively joins projects where its materials expertise and industrial testing infrastructure add direct value.
Despite only 4 projects, Saint-Gobain Recherche has built a broad European network of 41 partners across 9 countries, reflecting their participation in large MSCA training networks that naturally span many institutions. Their network skews heavily toward universities and research institutes in the materials science community.
What sets them apart
As the R&D arm of a €40B+ multinational, Saint-Gobain Recherche offers something few academic partners can: direct industrial validation pathways and manufacturing scale-up capability for materials innovations. Their recent pivot toward AI-driven process control (reinforcement learning for mixing) combined with deep physical materials knowledge makes them a rare partner who understands both the computational and the factory-floor sides. For consortium builders, they represent a credible route from lab-scale materials research to commercial production.
Highlights from their portfolio
- CoPerMixCombines reinforcement learning with industrial mixing processes — represents Saint-Gobain's strategic shift toward AI-augmented manufacturing and is their only project with recorded EC funding (EUR 274,802).
- MatheGramMultiscale analysis spanning sintering, additive manufacturing, and discrete element modelling — showcases the breadth of their computational materials science capabilities across multiple industrial applications.