Khalid Ferji is Full Professor of Polymer Chemistry at Université de Lorraine (EEIGM, ENSIC, LRGP), where he develops research at the interface of polymer chemistry, organic chemistry, and artificial intelligence.
His scientific expertise spans controlled radical polymerization, polymer self-assembly, polysaccharide-based nanomaterials, and photo-RAFT polymerization-induced self-assembly (PISA). He obtained his PhD in 2013 and his Habilitation (HDR) in 2022.
Over the past decade, his research has contributed to the development of advanced polysaccharide-based nanocarriers, light-mediated polymerization strategies, and innovative self-assembled polymer systems. More recently, he has established a new research direction centered on AI-driven polymer science, integrating machine learning, automation, and digital workflows into polymer synthesis and characterization.
His group develops computational and experimental frameworks for polymer informatics, AI-assisted polymer synthesis, automated characterization, and open-source scientific tools such as DetectNano, with the long-term vision of accelerating polymer discovery through sustainable and data-driven approaches.
Khalid has authored more than 40 peer-reviewed publications and is the pedagogical coordinator of the Master’s program CHIPS at Université de Lorraine, where he initiated the AIPoly track at the interface of polymer science and artificial intelligence. He also serves as President of the Eastern Section of the French Polymer Group (GFP), contributing to the structuring and visibility of the French polymer community.
His current ambition is to establish a leading interdisciplinary research hub in AI for polymer science, bridging chemistry, data science, and materials engineering to shape the next generation of sustainable polymer innovation.
Research Themes
- Polymer synthesis and self-assembly – Design of functional polymers via controlled radical polymerization, photo-RAFT, and polymerization-induced self-assembly (PISA).
- Physico-chemistry of polymers – Understanding polymer morphology, colloid science, and advanced characterization methods.
- AI-driven polymer informatics – Machine learning for predicting polymerization outcomes, morphologies, and guiding experimental design.
- Continuous-flow polymerization and automation – Coupling flow chemistry with AI and automated characterization for reproducibility and sustainability.