Objective
Data-driven methods promise to enable a highly structured approach for the development of asymmetric catalytic reactions, founded both on experimental and computational data. In this new but underdeveloped method, a small number of experiments is performed and that data is used to make a mathematical model to predict how new ligands will behave. Such a model is based on calculated or measured physical descriptors of the ligand, and correlations obtained between enantioselectivity and such descriptors also provide mechanistic insight. In an iterative (“looped”) approach, the model’s predictions are tested experimentally, and fed back to make an improved model, which is again tested experimentally, until satisfactory stereoselectivity and yield is obtained. Importantly, the integration and feedback of computational and experimental data during the research process is a significantly more efficient approach to developing asymmetric catalytic reactions and also provides mechanistic insight as the reaction is developed.
The main research aim of this doctoral network is to develop powerful and readily applicable workflows for data-driven development of stereoselective catalysis.
Using this data-driven approach requires a fundamentally different experimental workflow to developing catalytic reactions than is currently employed in most research laboratories. Since this requires a fundamental change in the way most experimental groups work, the data-driven approach is not widespread and remains underdeveloped. The next generation of chemists requires training in combined and integrated computational and experimental approaches, both in academia and industry.
The main training aim of this doctoral network is to train researchers in comprehensive data-driven experimental approach for realizing challenging asymmetric catalytic methods.
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.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral NetworksCoordinator
09111 Chemnitz
Germany
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Participants (7)
28049 Madrid
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72074 Tuebingen
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9712CP Groningen
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32000 Haifa
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4 Dublin
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15782 Santiago De Compostela
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84105 Beer Sheva
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Partners (11)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
1211 Geneve
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
80523 2001 Fort Collins
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
67063 Ludwigshafen Am Rhein
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
28042 Madrid
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
13353 Berlin
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
02139 Cambridge
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
W2 6BD London
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
64293 Darmstadt
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
DD1 3JT Dundee
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
32000 Haifa
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
OX1 2JD Oxford
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