Speaker
Description
Modern chemical technology makes extensive use of formulated products, from flavours and fragrances to surfactants and resins. These products are traditionally created and optimised using trial and error, an inefficient and costly process. This situation arises in part due to the complexity of the task. Understanding and accounting for the huge number of (inter)molecular interactions in a design task is currently a huge challenge. The Big Chemistry Consortium aims to transform formulation from an art to a science-based technology by establishing the RobotLab, an autonomous, self-driving laboratory combining AI, chemical data and high-throughput experimentation. Our consortium is spread across several institutions in The Netherlands (Radboud University Nijmegen, TU Eindhoven, AMOLF, Rijksuniversiteit Groningen and Fontys Hogeschool), each contributing to a pool of experimental methods, data and expertise. In this talk, I will present a selection of our initial investigations in high-throughput data collection, how we are approaching AI methods such as chemical language models for property prediction, and how we are working towards establishing efficient and secure data sharing methods within the consortium.