Materials Design and Informatics Group

Department of Chemistry

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Department of Chemistry

University College London

London WC1H 0AJ

Materials Design and Informatics Group (MDIG) is a research collective, working to accelerate develoment of new green energy materials. We are based at UCL, in the Department of Chemistry.

We use a combination of data-driven methods (such as deep learning and Bayesian statistics) and quantum mechanics calculations to design new materials on computers and to help accelerate the experimental characterisation of materials. We work with other academics, national facilities and companies.

news

Oct 1, 2024 Welcoming Cyprien Bone, who is joining us as a PhD student and Bradley MArtin who is joining as a Research Fellow.
Sep 12, 2024 New paper alert - Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling
Sep 1, 2024 Welcoming Mueen Taj, who is joining us as a PhD student to work on developing machine learning appraoches to analysing neutron scattering data.
Jul 1, 2024 Welcoming Jaivin Gohil, who is joining us as a summer student to work on MACE machine learning potentials for polymers.
May 31, 2024 Welcoming Irina Stanojevic who is joining us for a research visit to look at band offsets in metal halide and metal chalcogenide semiconductors.

selected publications

  1. Computational screening of all stoichiometric inorganic materials
    Daniel W Davies, Keith T Butler, Adam J Jackson, and 4 more authors
    Chem 2016
  2. Machine learning for molecular and materials science
    Keith T Butler, Daniel W Davies, Hugh Cartwright, and 2 more authors
    Nature 2018
  3. Designing interfaces in energy materials applications with first-principles calculations
    Keith T Butler, Gopalakrishnan Sai Gautam, and Pieremanuele Canepa
    npj Computational Materials 2019
  4. Distributed representations of atoms and materials for machine learning
    Luis M Antunes, Ricardo Grau-Crespo, and Keith T Butler
    npj Computational Materials 2022
  5. Entropy-based active learning of graph neural network surrogate models for materials properties
    Johannes Allotey, Keith T Butler, and Jeyan Thiyagalingam
    The Journal of Chemical Physics 2021
  6. Interpretable and explainable machine learning for materials science and chemistry
    Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi, and 1 more author
    Accounts of Materials Research 2022