Air Liquide creates AI for chemistry research chair with ENS-PSL

Air Liquide, the École normale supérieure (ENS-PSL) and the ENS Foundation have created the "Artificial intelligence for chemistry and through chemistry" research chair, the first launched under normalesup.ai. The chair will develop AI models that combine machine learning with fundamental chemical knowledge, focusing on reducing data requirements, physico-chemical law-based models, and discovering new molecules and materials. It will also train students, doctoral candidates and early-career researchers.

Key facts

Market context

Industrial gases such as oxygen, nitrogen, hydrogen and argon are produced in large volumes and sold to steelmaking, chemicals, electronics, healthcare and energy customers, so new ways to find and develop products or cut production costs matter across these sectors. Machine learning is increasingly used in chemistry to predict molecular properties, screen candidate materials and speed up experiments, but models trained on limited data can be unreliable. Approaches that anchor models to established physical and chemical laws aim to reduce that data requirement. For suppliers, faster discovery of gases, catalysts and materials can shorten development cycles, while better process modelling can help plant operations and customer service. Training researchers in these methods also builds the talent pool the chemical industry needs. Announcements of this type are generally research-stage and their effects on supply, pricing or competitors usually take years to show.

Source: official release on airliquide.com. Summary and context written by Market Krystal.