Project title: MLMLLM: Machine Learning, Multiple Likelihoods and the Laws of Metallo-proteins
PI: David Gavaghan
The proposed research aims to advance the field of bio-electrochemistry by leveraging advanced machine learning techniques to gain a deeper understanding of electron-transfer reactions in complex systems. This has the potential to drive the development of novel catalysts and biosensing technologies with significant real-world applications in sustainable hydrogen production and disease detection. It showcases a pioneering approach to bio-electrochemistry, combining expertise in electrochemistry, statistical inference, Bayesian statistics, and machine learning.
