Our Mission
Why Join AI4Cat?
Artificial intelligence, from data science and machine learning to large language models, holds enormous potential to transform how we understand and develop catalytic systems.
Yet for this potential to be realized, researchers must be trained not just in AI tools, but in how to apply them thoughtfully to real catalytic data. Catalysis is inherently complex, and the data available is often limited. This makes domain expertise essential when applying AI methods, a generic approach simply isn't enough.
AI4Cat bridges this gap by combining rigorous AI training with a deep focus on catalysis-specific challenges.
Complex Catalytic Systems
Catalysis involves high-dimensional, often scarce datasets that demand domain-aware AI approaches.
The Knowledge Gap
Generic AI training isn't enough. Researchers need to integrate chemical understanding with machine learning.
Hands.On is Essencial
Theoretical knowledge only goes so far. Real competency is built through working with real data and tools.
Objective 1
Teach AI Fundamentals
Provide participants with a solid grounding in data science and machine learning — including exploratory data analysis, key data characteristics, ML modelling with real datasets, and an introduction to large language models.
Objective 2
Apply AI to Catalysis
Go beyond the basics and tackle catalysis-specific challenges: modelling complex catalytic datasets, overcoming data scarcity through augmentation and transfer learning, and combining physics-based knowledge with AI.
Objective 3
Hands-On Learning
Every concept is reinforced through practical sessions, participants work directly with real data and tools, leaving with skills they can immediately apply in their own research.
Objective 4
Build a Community
Foster connections between early-career researchers across institutions, creating a growing network of AI-literate catalysis scientists that will collectively drive the field forward.