Summary
Photoredox catalysis has become a quintessential part of organic synthesis and gained wide interest in the chemical industry as a mild and robust synthetic methodology. Acridinium salts are a prominent substitute for transition metal-based photocatalysts (PCs) due to the modularity of the core, which makes them and structurally related dyes an optimal platform for structure-property tuning. However, a limited understanding of how structure influences reactivity prevents rational design, while the synthetically realizable chemical space is far too vast to explore using even high-throughput experimental methods.
The objective of the proposed action is to study computationally the elementary mechanistic steps integral to photoredox catalysis to establish how they are each influenced by PC structure. We will develop an automated workflow for high-throughput computational molecular PC construction and analysis, in which known molecular fragments are coupled together by chemically precedented steps. Finally, Bayesian optimization will be used to guide the machine learning model building. This is distinct from conventional approaches in the field as we aim to increase the quality of our model with mechanistic understanding from quantum chemical calculation.
My expertise in the fields of synthetic, physical, and computational organic chemistry, provide me the necessary skillset and ability to learn new concepts and successfully execute this project. The gained knowledge and results from the project will be an asset and help to build sustainable processes in the chemical industry in EU. The work will be implemented in the research groups of Prof. Robert Paton and Prof. Jeremy Harvey, with a secondment period in the group of Prof. Abigail Doyle. The investigators’ combined expertise ranges from computational and empirical reaction mechanism study to data sciences, and hence enables the project to incorporate complex mechanistic information to rational PC design.
The objective of the proposed action is to study computationally the elementary mechanistic steps integral to photoredox catalysis to establish how they are each influenced by PC structure. We will develop an automated workflow for high-throughput computational molecular PC construction and analysis, in which known molecular fragments are coupled together by chemically precedented steps. Finally, Bayesian optimization will be used to guide the machine learning model building. This is distinct from conventional approaches in the field as we aim to increase the quality of our model with mechanistic understanding from quantum chemical calculation.
My expertise in the fields of synthetic, physical, and computational organic chemistry, provide me the necessary skillset and ability to learn new concepts and successfully execute this project. The gained knowledge and results from the project will be an asset and help to build sustainable processes in the chemical industry in EU. The work will be implemented in the research groups of Prof. Robert Paton and Prof. Jeremy Harvey, with a secondment period in the group of Prof. Abigail Doyle. The investigators’ combined expertise ranges from computational and empirical reaction mechanism study to data sciences, and hence enables the project to incorporate complex mechanistic information to rational PC design.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101062692 |
| Start date: | 01-10-2022 |
| End date: | 30-09-2025 |
| Total budget - Public funding: | - 290 444,00 Euro |
Cordis data
Original description
Photoredox catalysis has become a quintessential part of organic synthesis and gained wide interest in the chemical industry as a mild and robust synthetic methodology. Acridinium salts are a prominent substitute for transition metal-based photocatalysts (PCs) due to the modularity of the core, which makes them and structurally related dyes an optimal platform for structure-property tuning. However, a limited understanding of how structure influences reactivity prevents rational design, while the synthetically realizable chemical space is far too vast to explore using even high-throughput experimental methods.The objective of the proposed action is to study computationally the elementary mechanistic steps integral to photoredox catalysis to establish how they are each influenced by PC structure. We will develop an automated workflow for high-throughput computational molecular PC construction and analysis, in which known molecular fragments are coupled together by chemically precedented steps. Finally, Bayesian optimization will be used to guide the machine learning model building. This is distinct from conventional approaches in the field as we aim to increase the quality of our model with mechanistic understanding from quantum chemical calculation.
My expertise in the fields of synthetic, physical, and computational organic chemistry, provide me the necessary skillset and ability to learn new concepts and successfully execute this project. The gained knowledge and results from the project will be an asset and help to build sustainable processes in the chemical industry in EU. The work will be implemented in the research groups of Prof. Robert Paton and Prof. Jeremy Harvey, with a secondment period in the group of Prof. Abigail Doyle. The investigators’ combined expertise ranges from computational and empirical reaction mechanism study to data sciences, and hence enables the project to incorporate complex mechanistic information to rational PC design.
Status
CLOSEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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