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
| Mar 31, 2024 | New publication Dual-Anion Strategy Induces Dual Enhancement Toward Ultrashort Phase-Matching Wavelength in Deep-UV Transparent d0 Transition Metal Oxyfluorides |
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| Feb 4, 2024 | New PhD position available in MDIG. Machine Learning Enhanced Spectropscopy |
| Jan 17, 2024 | New publication Effects of Grain Boundaries and Surfaces on Electronic and Mechanical Properties of Solid Electrolytes |
| Jan 1, 2024 | New preprint Mapping Inorganic Crystal Space |
| Nov 4, 2023 | New PhD position available in MDIG. Foundation Models for Materials Science |
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