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Home   /   Post Doctorat   /   Combinatorial optimization of base materials for the design of new materials

Combinatorial optimization of base materials for the design of new materials

Artificial intelligence & Data intelligence Technological challenges


The design of new materials is a field of growing interest, especially with the emergence of additive manufacturing processes, thin film deposition, etc. In order to create new materials to target properties of interest for an application area, it is often necessary to mix several raw materials.

A physicochemical modeling of the reactions that occur during this mixing is often very difficult to obtain, especially when the number of raw materials increases. We want to free ourselves as much as possible from this modeling. From experimental data and business knowledge, the goal of this project is to create a symbolic AI capable of groping for the optimal mixture to achieve one or more given properties. The idea is to adapt existing methods of operations research, such as combinatorial optimization, in a context of imprecise knowledge.

We will focus on different use cases such as electric batteries, solvents for photovoltaic cells and anti-corrosion materials.

Within the project, you will:
• Study the state of the art,
• Propose one or several algorithms to prototype, and their evaluation,
• Disseminate the resulting innovations to the consortium and the scientific community, through presentations, contributions to technical reports and / or scientific publications.

Maximum duration: 18-24 months (regarding your experience).


Département Métrologie Instrumentation et Information (LIST)
Service Intelligence des Données
Laboratoire Intelligence Artificielle et Apprentissage Automatique
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