Materials Design and Informatics Group
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
| Aug 12, 2025 | New 3 yr PostDoc position available in MDIG. Research Fellow in Machine Learning for Materials Design |
|---|---|
| Jul 25, 2025 | New preprint alert - Learning disentangled latent representations facilitates discovery and design of functional materials |
| Jul 17, 2025 | New preprint alert The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? |
| Apr 3, 2025 | New paper alert - REMatch plus SOS: Machine-learning-accelerated structure prediction for supported metal nanoclusters |
| Jan 27, 2025 | New paper alert - On the use of clustering workflows for automated microstructure segmentation of analytical STEM datasets |
selected publications
- Computational screening of all stoichiometric inorganic materialsChem 2016
- Machine learning for molecular and materials scienceNature 2018
- Designing interfaces in energy materials applications with first-principles calculationsnpj Computational Materials 2019
- Distributed representations of atoms and materials for machine learningnpj Computational Materials 2022
- Entropy-based active learning of graph neural network surrogate models for materials propertiesThe Journal of Chemical Physics 2021
- Interpretable and explainable machine learning for materials science and chemistryAccounts of Materials Research 2022