Conveners
Machine Learning in Materials and Chemicals Design: Machine Learning in Materials and Chemicals Design
- Tom Whitehead (Intellegens)
Description
Designing new materials and chemicals is one of the great challenges of modern industry: the energy transition, drive for sustainability, and the rise of advanced manufacturing techniques all require new building blocks for our engineered world. However the design spaces involved, spanning compositions, processing routes, and performance targets, are now far too complex for human intuition alone, so to make progress we increasingly rely on computational tools. Machine learning is a branch of Artificial Intelligence focused on turning historic data into actionable insights and suggestions, and in the materials and chemicals space is now routinely used to guide experimental R&D campaigns, suggesting where to explore next and accelerating discovery.
In this seminar we will explore the use of machine learning in adaptive experimental design for materials and chemicals, starting with an overview of the field and then exmaining details of the component parts of this approach. We will identify the key mathematical ideas behind different methodologies and how they can guide us on different toolsets to use for different applications, finishing with a brief case study of adaptive experimental design in action.
