Crystallisation Screening DataFactory

Funder	Engineering and Physical Sciences Research Council
Recipient Organization	University of Strathclyde
Country	United Kingdom
Start Date	Dec 01, 2024
End Date	Nov 30, 2028
Duration	1,460 days
Number of Grantees	2
Roles	Student; Supervisor
Data Source	UKRI Gateway to Research
Grant ID	`2934074`

Grant Description

Summary:

max. 3500 characters, including spaces, please ensure at least a minimum of 1000 characters) * Solvent selection and process conditions for scalable purification and particle engineering objectives in presence of impurities from S1. Particular focus proposed for this project will be to build solvent dependent morphology prediction tool within the CCS framework.

This will explore current methods for morphology prediction (BFDH; Habit; Crystalgrower and Addict, "persistent needles ex McCardle") alongside data driven ML approaches that exploit data generation from the DataFactory platform. In addition, the influence of impurities on resultant morphology will be investigated. Hence, comparison of mechanistic, data driven and hybrid approaches will be enabled.

Premise to explore multiple APIs (molecular descriptors), solvents (attributes/descriptors), structural descriptors and interactions (CCDC, cif, solution, PIXEL, COSMO-RS; MD; surface interaction e.g. Material Studio); crystallisation conditions and outcomes in the presence or absence of target impurities and identify mechanisms; kinetics and impact on particle shape e.g. engineerability.