Projects per year
Personal profile
Research interests
Based in the Department of Chemistry at the University of Bath, the Grayson Group’s research interests are centred around the use of molecular modelling and machine learning to address challenges in synthetic chemistry, toxicology, and drug design.
Current research areas include:
Machine learning activation barriers
Development of new methods for neural network interpretation
Modelling of catalytic reactions
In silico toxicology & drug design
Willing to supervise doctoral students
Please get in touch to discuss undergraduate, Master’s, PhD and Postdoctoral research opportunities.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Chemistry, Doctor of Philosophy, University of Cambridge
2010 → 2014
Chemistry, Master of Natural Science, University of Cambridge
2009 → 2010
Natural Sciences, Bachelor of Arts, University of Cambridge
2006 → 2009
External positions
Lindemann Trust Fellow, University of California, Los Angeles
1 Jan 2015 → 31 Dec 2015
Junior Research Fellow, University of Cambridge
1 Oct 2014 → 30 Jun 2018
Keywords
- Computational Chemistry
- Machine Learning
- Molecular Modelling
- Organic Chemistry
- Organocatalysis
- Transition Metal Catalysis
- Toxicology
- Automation
- Drug design
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Catalytic Chemical Sorting of Intractably Mixed Plastics
Engineering and Physical Sciences Research Council
1/10/23 → 30/09/26
Project: Research council
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Machine Learning and Molecular Modelling: A Synergistic Approach to Rapid Reactivity Prediction
Engineering and Physical Sciences Research Council
1/07/22 → 30/06/24
Project: Research council
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Using Machine Learning to Access Challenging Hydrogenations: A combined theoretical and experimental approach
4/10/21 → 3/10/25
Project: UK industry
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Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach
Espley, S. G., Farrar, E. H. E., Buttar, D., Tomasi, S. & Grayson, M. N., 1 Aug 2023, In: Digital Discovery. 2, 4, p. 941-951 11 p.Research output: Contribution to journal › Article › peer-review
Open Access2 Citations (SciVal) -
Photochemical Fingerprinting Is a Sensitive Probe for the Detection of Synthetic Cannabinoid Receptor Agonists; Toward Robust Point-of-Care Detection
Andrews, R., May, B., Hernández, F. J., Townsend, P., Cozier, G., Sutcliffe, O. B., Haines, T. S. F., Freeman, T., Scott, J., Husbands, S., Blagbrough, I., Bowman, R., Lewis, S., Grayson, M., Crespo-Otero, R., Carbery, D. & Pudney, C., 17 Jan 2023, In: Analytical Chemistry. 95, 2, p. 703-713Research output: Contribution to journal › Article › peer-review
Open AccessFile4 Citations (SciVal)45 Downloads (Pure) -
Photoredox-HAT Catalysis for Primary Amine α-C–H Alkylation: Mechanistic Insight with Transient Absorption Spectroscopy
Sneha, M., Thornton, G., Borrell, L., Ryder, A., Espley, S., Clark, I. P., Cresswell, A., Grayson, M. & Orr-Ewing, A., 16 Jun 2023, In: ACS Catalysis. 13, 12, p. 8004-8013 10 p.Research output: Contribution to journal › Article › peer-review
Open Access2 Citations (SciVal) -
Reformulating Reactivity Design for Data-Efficient Machine Learning
Lewis-Atwell, T., Beechey, D., Şimşek, Ö. & Grayson, M., 20 Oct 2023, In: ACS Catalysis. 13, 20, p. 13506–13515 10 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Atom and step economical synthesis of acyclic quaternary centers via iridium-catalyzed hydroarylative cross-coupling of 1,1-disubstituted alkenes
Cooper, P., Dalling, A. G., Farrar, E. H. E., Aldhous, T. P., Grélaud, S., Lester, E., Feron, L. J., Kemmitt, P. D., Grayson, M. N. & Bower, J. F., 7 Oct 2022, In: Chemical Science. 13, 37, p. 11183-11189 7 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (SciVal)3 Downloads (Pure)
Datasets
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Dataset for "Computational Modelling and Machine Learning Approaches Towards Understanding Asymmetric Catalytic Organic Reactions"
Farrar, E. (Creator) & Grayson, M. (Supervisor), University of Bath, 12 Aug 2022
DOI: 10.15125/BATH-01148
Dataset
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Dataset for "Reformulating Reactivity Design for Data-Efficient Machine Learning"
Lewis-Atwell, T. (Creator), Beechey, D. (Creator), Şimşek, Ö. (Creator) & Grayson, M. (Creator), University of Bath, 6 Oct 2023
DOI: 10.15125/BATH-01240, https://github.com/the-grayson-group/finding_barriers
Dataset
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Dataset for "Machine learning and semi-empirical calculations: A synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction"
Farrar, E. (Creator) & Grayson, M. (Creator), University of Bath, 14 Jun 2022
DOI: 10.15125/BATH-01092
Dataset
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Dataset for "Comparing the Performances of Force Fields in Conformational Searching of Hydrogen Bond-Donating Catalysts"
Lewis-Atwell, T. (Creator), Townsend, P. (Creator) & Grayson, M. (Creator), University of Bath, 27 Apr 2022
DOI: 10.15125/BATH-01065
Dataset
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Dataset for "Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach"
Espley, S. (Creator), Farrar, E. (Creator), Grayson, M. (Supervisor), Tomasi, S. (Supervisor) & Buttar, D. (Supervisor), University of Bath, 31 May 2023
DOI: 10.15125/BATH-01229
Dataset