MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery
- Clyde Fare
- George Holt
- et al.
- 2024
- AAAI 2024
Dr. Clyde Fare is joined IBM Research in 2018 as a post-doctoral researcher in the ML group before becoming a Staff Research Scientist in 2019. His research is focused on the application and development of machine learning algorithms, particularly Bayesian optimisation and deep learning applied to problems within the chemical and biochemical sector. He is currently working on reinforcement learning applied to materials discovery.
Clyde received a PhD in Computational Photochemistry from Imperial College London in 2017 for his thesis entitled 'Hybrid Methods for Molecular Spectroscopy and Reactivity'. His PhD research under Mike Bearpark and Jasper Von Thor involved analysis and application of the hybrid method ONIOM to model extended molecular systems alongside experimental spectroscopic studies. Systems examined included poly-aromatic hydrocarbons and the photoactive protein EOSFP.
Clyde also holds a Master's degree in Molecular Modelling from University College London and a Master's degree in Nanomaterials from Imperial College London.