skip to primary navigationskip to content
 

Projects and supervisors

The Departments and academics contributing to the CDT and a list of projects.

The University Departments and the academics participating in the CDT, together with subjects areas suitable for PhD study from 2nd year, are listed below. Projects which are co-supervised by industrial partner are indicated by asterisk. Please note that the project areas are subject to change, and further projects will be added as the Centre develops.

Click on the department name to see details of all the project listed.

Department of Chemistry

  1. Studying metastable states in biomolecules by analysing rare events (Robert Jack)
  2. Self-assembly of mesoscopic structures and nanodevices (David Wales)
  3. Free energy landscapes and observation time scales (David Wales)
  4. Structure prediction and machine learning for variable pathogens (David Wales)

Department of Physics

  1. Predicting New Materials for Engineering Applications: Employing Data Science with Machine Learning (Jacqui Cole)
  2. Creating and Applying New Software Tools for Chemical Data Science (Jacqui Cole)
  3. Predicting New Materials for Optoelectronic Applications: Employing Data Science with Machine Learning (Jacqui Cole)
  4. Functionalising Batteries with Computation, Data Science & Machine Learning (Jacqui Cole)
  5. Exploring the energy landscape of histone tail proteins: structural disorder, epigenetic effects, and DNA binding (Rosana Collepardo-Guevara/David Wales)
  6. Design of metallic materials for additive manufacturing applications using artificial intelligence approach (Gareth Conduit)
  7. Topological materials at finite temperature (Bartomeu Monserrat)
  8. Photovoltaics at finite temperature (Bartomeu Monserrat)
  9. Superconductivity at extreme pressures (Bartomeu Monserrat)
  10. Probabilistic deep learning for drug discovery (Alpha Lee)
  11. Tackling pharmaceutical formulations with physics-based machine learning (Alpha Lee)
  12. Realising a GPS for chemistry: Accelerating chemical synthesis using machine learning (Alpha Lee)

Department of Materials Science and Metallurgy

  1. Computational discovery of new superconductors (Chris Pickard)
  2. Navigating material structure space (Chris Pickard)
  3. Structure prediction of interfaces in two-dimensional materials (Chris Pickard)
  4. Modelling and characterisation of Hydrogen-assisted failure in nuclear pressure vessel steels (Enrique Galindo-Nava)
  5. Modelling and experimentation of microstructure and hydrogen embrittlement in welds of dissimilar materials (Enrique Galindo-Nava)
  6. Phase field modelling of phase transitions in additively mnufactured metallic alloys (Enrique Galindo-Nava)

Engineering Department

  1. Patterned shape changes in liquid crystal elastomers: when the material is the machine (John Biggins)
  2. Surface-tension driven deformations in soft solids (John Biggins)
  3. Tensile elastic instabilities (John Biggins)
  4. Machine Learning and Drug Discovery (Gabor Csanyi)
  5. Understanding material failure at all length scales (Gabor Csanyi)
  6. Machine learning quantum mechanics (Gabor Csanyi)

Department of Chemical Engineering and Biotechnology

  1. Tracking functional materials by high throughput screening and machine learning (David Fairen-Jimenez)