Project title: Different flavours of machine learning potentials for modelling processes in complex solvated environments
PI: Fernanda Duarte
Solvents are integral components of chemical processes. Most commonly used solvents are toxic to humans and sources of pollution and ideally need replacing with environmentally friendly alternatives. Computational modelling offers insights into chemical reactions at the molecular level and may guide the design of novel synthetic paths. However, modelling chemical reactions in sustainable solvents remains challenging and computationally demanding. This research aims to develop machine learning-based potentials, offering a fast and accurate approach to modelling chemical reactions in complex solutions.
