Che, Yu 
ORCID: 0000-0002-8589-6795
(2023)
Data-driven approach of discovering organic photocatalysts and developing molecular force field by machine learning.
PhD thesis, University of Liverpool.
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Abstract
Machine learning techniques are becoming more prevalent in chemistry research as they offer an effective approach for handling large, complex chemical datasets generated from high-throughput experiments and molecular simulations. To gain a comprehensive under- standing of datasets, it is crucial to employ efficient methods for data representation and analysis. This PhD project utilized classical machine learning algorithms to effectively visualize high-dimensional chemical data, ascertain connections between chemical struc- ture and properties, facilitate the discovery of novel organic catalysts, and developing a machine learning potential to describe intermolecular interactions.
| Item Type: | Thesis (PhD) |
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| Uncontrolled Keywords: | Bayesian optimization, data visualization, machine learning, machine learning potential, photocatalyst |
| Divisions: | Faculty of Science and Engineering > School of Physical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 10 Aug 2023 15:54 |
| Last Modified: | 10 Aug 2023 15:55 |
| DOI: | 10.17638/03171161 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3171161 |
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