AI for Materials Science
I’m a materials scientist, and my research focuses on using AI to accelerate materials discovery and design—especially for complex metallic alloys, such as high-entropy alloys. I use AI not as a black box, but as a way to connect data, physics, and experiments into a coherent design loop for new materials.
Research Focus
Physics-Informed Modeling for Predictive Alloy Design — My main goal is practical: to predict and optimize key properties—like strength, ductility, or stacking fault energy—when experiments are slow, expensive, or noisy. For that, I build “physics-informed” machine learning workflows: instead of learning only from composition, I try to feed the models with physically meaningful descriptors and constraints, so the predictions are more robust and easier to interpret.
Data-Centric Materials AI — A big part of my work is also data-centric. I spend a lot of effort curating datasets, checking consistency, and managing uncertainty, because in materials science the data quality often limits the model more than the algorithm itself.
Decision-Making: Active Learning and Multi-Objective Optimization — Finally, I’m interested in decision-making tools: active learning to choose the next best experiments, and multi-objective optimization to balance performance with constraints like cost, sustainability, or compositional robustness.
Academic Positions and International Appointments
I am a professor of materials science at the Institute for Condensed Matter Chemistry of Bordeaux (ICMCB, CNRS) and Bordeaux INP in France, which I joined in 2001. Since September 2025, I have been based in Singapore as an Adjunct Senior Researcher at Nanyang Technological University (NTU), within CINTRA, working on AI-guided design of advanced materials. Since 2020, I have also held an Honor Chair Professorship at National Tsing Hua University (Taiwan).
I have built a sustained international research profile, with 120+ peer-reviewed journal publications and around 50 invited talks at conferences and universities. I have coordinated or led multiple EU-funded, national, and industry-partnered projects, and in 2018 I received the Constellium Prize of the French Academy of Sciences for contributions to metallurgy.
Recent Papers & Projects
🌱📊 Sustainability-Informed Alloy Design with AI
This line of work is developed in close collaboration with Prof. M.R. Barnett (Deakin University, Australia) and focuses on integrating economic, environmental and societal criteria into AI-guided high-entropy alloy design.
As part of my work at the interface of AI, alloy design, and sustainability, I develop quantitative indicators, open datasets, and decision frameworks to integrate economic, environmental and societal criteria into high-entropy alloy design. To facilitate the practical application of these metrics, I developed an open-source tool designed to integrate sustainability into alloy design. It calculates 9 economic, environmental, and societal footprints, providing a comparative visualization of new alloy formulas versus current state-of-the-art (HEAs/CCAs) and commercial standards. 
The publications below provide a foundation for this sustainability-aware exploration of HEA composition space.
S. Gorsse, T. Langlois, and M.R. Barnett,
Considering sustainability when searching for new high entropy alloys,
Sustainable Materials and Technologies 40 (2024) e00938.
https://doi.org/10.1016/j.susmat.2024.e00938S. Gorsse, T. Langlois, A.-C. Yeh and M.R. Barnett,
Sustainability indicators in high entropy alloy design: an economic, environmental, and societal database,
Scientific Data 12 (2025) 288.
https://doi.org/10.1038/s41597-025-04568-xM.R. Barnett and S. Gorsse,
Sustainability of High Entropy Alloys and Do They Have a Place in a Circular Economy?,
Metallurgical and Materials Transactions A 56 (2025) 4249.
https://doi.org/10.1007/s11661-025-07928-9
🚀⚙️ AI-Driven Design of High-Temperature Structural Alloys
In this line of work, developed in collaboration with Prof. A.-C. Yeh (National Tsing Hua University, Taiwan) and Dr. D.B. Miracle (Air Force Research Lab., USA), I combine physical metallurgy, CALPHAD-based thermodynamics and physics-informed, AI-driven optimisation to design and assess high-temperature structural alloys, including HEAs and CCAs for turbine blades and other hot-section components.
S. Gorsse et al. Advancing refractory high entropy alloy development with AI-predictive models for high temperature oxidation resistance,
Scripta Materialia 255 (2025) 116394.
https://doi.org/10.1016/j.scriptamat.2024.116394O.N. Senkov et al. Correlations to improve high-temperature strength and room temperature ductility of refractory complex concentrated alloys,
Materials & Design 239 (2024) 112762.
https://doi.org/10.1016/j.matdes.2024.112762