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SynTech Centre for Doctoral Training


PhD project proposals that select ML & AI for chemistry as a core theme will require a working knowledge of commonly used ML, AI-based data-analysis & modelling approaches and their application at the intersection of chemistry & cheminformatics. Students will be better placed to make significant contributions to this fast-growing field via exposure to frontier research at this interface. This will allow students to:

  • identify research questions that can be tackled using ML/AI-based modelling of synthetic chemistry data;
  • understand the key requirements of data for use in ML/AI-based modelling and how this affects experimental automation;
  • obtain, de-noise & curate relevant datasets from experimental partners;
  • invent & discover novel molecular parameterisations that capture physico-chemical mechanisms;
  • implement & extend modern ML/AI-based modelling approaches using existing & novel molecular descriptors;
  • develop an awareness of overfitting & regularization techniques that can alleviate this problem;
  • gain familiarity through RRI with the limitations of their modelling approach; and
  • display knowledge of the advantages & disadvantages of different modelling approaches and understanding which are best suited in different contexts.