SOURCE-LINKED INTELLIGENCE

Computationally driven discovery of organic dyes for photoredox catalysis from physicochemical principles and mechanistic information

CORDIS · observation · Publication date unknown

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 t

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recordType

award

status

TERMINATED

region

EU

value

290444.16

unit

EUR