Project description
Applying data science to photocatalyst design
Organophotoredox catalysis has gained significant attention in sustainable chemistry. This method uses affordable, metal-free dyes that can be tailored for specific reactions. However, predicting how these dyes will behave in different systems is challenging. Traditional approaches often rely on trial-and-error methods that are time-consuming and costly. With the support of the Marie Skłodowska-Curie Actions programme, the PhotoCatData project aims to apply a data-driven solution to streamline photocatalyst design and reaction discovery. The study will utilise machine learning to correlate photocatalyst structures with their performance. Next, the proposed photochemical methodologies will be adapted for flow processes, enabling the efficient, large-scale production of valuable compounds. Project methodologies should help save time, cut costs and expand the potential of organophotoredox catalysis.
Objective
Photocatalysis is now a well-established method for developing sustainable protocols in synthetic organic chemistry. Organophotoredox catalysis, in particular, is gaining significant interest due the use of inexpensive, easy-to-make, metal-free organic dyes with tunable photophysical properties. Structural modification of these organic molecules is crucial to fine-tune their redox features for desired photochemical transformations. However, predicting the final excited-state properties of functionalized dyes remains challenging, especially when compatibility with the reaction system is required. This has led to a resource-intensive trial-and-error approach to identify the optimal photocatalyst structure for specific transformations, limiting its widespread application. A promising solution is data science, which can streamline the relationship between the chemical structure of dyes and their function, facilitating their application. The computer-assisted development of innovative systems in organophotoredox catalysis can offer novel possibilities for large-scale applications, reducing time, reagent consumption, and costs compared to traditional methods. This proposal aims to develop new data-assisted protocols for reaction discovery and photocatalyst design. During the outgoing phase at the University of Utah, under Prof. Sigman's supervision, machine learning algorithms will be used to correlate photocatalysts' structures with their functions. Classification tools will explore reactivity cliffs, and dimensionality reduction techniques will map the chemical space to visualize reactivity patterns with fewer experiments. In the subsequent return phase at the University of Padova, under Prof. Dell'Amico's supervision, these photochemical methodologies will be adapted for flow processes to produce synthetically relevant compounds on a multi-gram scale. The data science knowledge acquired will guide this transition, enabling a more efficient implementation.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences data science
- natural sciences chemical sciences catalysis photocatalysis
- natural sciences chemical sciences organic chemistry
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
35122 Padova
Italy
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