Computational Materials Science & AI for Accelerated Materials Discovery
Our research in Computational Materials Science and AI for Accelerated Materials Discovery leverages the power of data, simulation, and advanced characterisation to transform how materials are designed and understood. By integrating physics-based modelling with AI-driven approaches, we can predict material behaviours before they are synthesised — vastly accelerating the pace of innovation. Combined with state-of-the-art characterisation techniques at the atomic scale, our work enables faster, smarter discovery of materials for real-world impact.
Computational Materials Science:
Using simulations and modelling to predict material behaviours before they are made.
Materials Discovery with AI for Materials:
Accelerating innovation with machine learning to design materials faster and smarter.
State of the Art Characterisation:
Deploying cutting-edge techniques to uncover material properties at the atomic level.
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Related Publications
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Polyethylene-Glycol-Conjugated Peptide Coacervates with Tunable Size for Intracellular mRNA Delivery
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Robustness of Machine Learning Predictions for Fe-Co-Ni Alloys Prepared by Various Synthesis Methods
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