Courses

https://www.scientificupdate.com/training_courses/empowering-chemists-with-ai-and-ml-transformative-tools-and-techniques/

Empowering Chemists with AI and ML: Transformative Tools and Techniques - Scientific Update - UK

📅 Date: 15 June 2026 - 23 June 2026

📍 Online

A beginner-friendly course designed for chemists looking to explore AI and machine learning. Learn how to apply AI/ML in catalysis, materials science, and beyond with hands-on exercises in Python, MATLAB, and KNIME. No prior programming experience required!

https://www.scientificupdate.com/training_courses/ai-for-chemical-synthesis-graph-learning-and-practical-casp/

AI for Chemical Synthesis: Graph Learning and Practical CASP

📅 22 September 2026 - 30 September 2026

📍 Online

This advanced course introduces industrial chemists, process scientists, medicinal chemists, and computational R&D teams to how Graph Neural Networks and modern graph-based AI can support real-world chemical research, from molecular property prediction and reaction outcome modelling to yield estimation, retrosynthetic analysis, and route evaluation. Across four intensive sessions, participants will learn how molecules and reactions are represented as graphs, how these models extract chemically meaningful features, and how they are used within modern Computer-Aided Synthesis Planning workflows. The course also addresses the practical realities of industrial deployment, including data quality, proprietary datasets, process constraints, validation, and the limitations of current AI systems, with an elective final module focused either on 3D molecular and materials applications or AI-guided reaction optimisation and flow chemistry integration.

https://www.rsc.org/events/detail/82764/empowering-chemists-with-ai-and-ml-transformative-tools-and-techniques

Empowering Chemists with AI and ML: Transformative Tools and Techniques - Scientific Update - UK

📅 Date: 12, 13, 19 & 20 January 2026

📍 Online

A beginner-friendly course designed for chemists looking to explore AI and machine learning. Learn how to apply AI/ML in catalysis, materials science, and beyond with hands-on exercises in Python, MATLAB, and KNIME. No prior programming experience required!