With a worldwide production of nearly 380 million tons per year, polymer materials play a central role in our modern society. They are used in the manufacture of innumerable daily-life products, or as more sophisticated compounds in medicine, diagnostics, and fine chemistry. However, economical and new societal constraints require a more rational design and alternative synthesis, formulation and processing methods for polymer manufacturing to meet the need for greater sustainability, more virtuous end-of-life management, while maintaining optimal performances in application. Polymer-based materials of the future will be one of the pillars of the circular economy. Thus, the discovery of new polymers will lead to a paradigm shift and new methodologies for the design, processing and analysis of polymer-based materials. The recent development of high-throughput (HTP) and artificial intelligence (AI) methods has opened up enormous opportunities to tackle these challenges. While such methods are emerging in chemistry, they have not yet been implemented in France in Polymer Science. Thus, the AMETHIST demonstrator project proposes, as proof of concept, to merge all these fields by combining the use of HTP and AI methods to address three different case studies. The issues addressed have been chosen for their relevance to meet pressing scientific and societal challenges:
- Case study 1 – Design of polymer materials with programmable degradability;
- Case study 2 – Polymer-based organic-inorganic materials, nanocomposites and composites;
- Case study 3 – Biobased polymer materials.
In each case study, each type of material, HTP synthesis and characterization method will be implemented at molecular, macromolecular and materials scales. Data from the HTP analysis will be used to feed machine-learning approaches for determining the best combination with targeted multiple properties. Optimized materials designed by AI will be manufactured and evaluated.
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