| EPSRC Reference: |
EP/X021033/1 |
| Title: |
Adopting Green Solvents through Predicting Reaction Outcomes with AI/Machine Learning |
| Principal Investigator: |
Nguyen, Dr BN |
| Other Investigators: |
|
| Researcher Co-Investigators: |
|
| Project Partners: |
|
| Department: |
Sch of Chemistry |
| Organisation: |
University of Leeds |
| Scheme: |
Standard Research |
| Starts: |
01 January 2024 |
Ends: |
31 December 2027 |
Value (£): |
1,589,770
|
| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Catalysis & Applied Catalysis |
| Gas & Solution Phase Reactions |
|
|
| EPSRC Industrial Sector Classifications: |
| Manufacturing |
Chemicals |
| Pharmaceuticals and Biotechnology |
|
|
| Related Grants: |
|
| Panel History: |
|
|
Summary on Grant Application Form |
The switch from traditional organic solvents, many of which are hazardous, volatile or non-sustainable, to modern green solvents is one of the key sustainability objectives in High Value Chemical Manufacture. Currently, the use of green solvents is often explored at process development stage, instead of discovery stage. This necessitates re-optimisation of processes, due to changes in yield, selectivity, impurity profile and purification. These lead to longer development time, cost, and additional uncertainty. On the other hand, selecting the right solvent early may enhance chemoselectivity, avoid additional reaction steps, and simplify purification of the products.
Predicting these changes is an important underpinning capability for wider adaptation of green solvents in manufacturing. Unfortunately, the scarcity of reaction data in green solvents is a key obstacle in developing this capability. Thus, there is an urgent need for ML models which predict reactivity in green solvents based on available data in traditional solvents. In addition to addressing the short time-scale of early-stage process development, these will increase the confidence in utilising green solvents at discovery stage, support sophisticated synthetic routes planning tools which takes into account side products, impurity and purification methods, and act as valuable regulatory tools for assessing hazardous impurities.
This project will address these challenges through the following objectives:
O1 Addressing the scarcity of reactivity data in the literature through curation of reaction data with reliable reaction time and inclusion of rate laws.
O2 Developing solvent-dependent reactivity and reaction selectivity prediction models for green solvents.
O3 Producing a set of standard substrates based on cheminformatics analysis of industrially relevant reactions and collecting their reactivity data in green solvents.
These outputs will have transformative impacts in the chemical manufacture industry, delivering rapid, more sustainable and better quality-controlled processes through shorter development time, and confidence in predicting reaction outcomes in green solvents. The project will be carried out with support from industrial partners working in the field of cheminformatics and AI/Machine learning, e.g. Lhasa Ltd. and Molecule One. Its outputs will be guided and exploited by partners who are end-users in the High Value Chemical Manufacturing sectors: AstraZeneca, CatSci, and Concept Life Science.
|
|
Key Findings |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
http://www.leeds.ac.uk |