Our Team

Alex Ganose is a Lecturer in the Department of Chemistry at Imperial College London. His work is focused on using computational materials chemistry, machine learning, and high-throughput calculations to design new materials for energy applications. Through Alchemy he is looking to develop new collaborations for applying AI to materials discovery.

Gabriella Pizzuto is a lecturer in Robotics and Chemistry Automation and Royal Academy of Engineering Research Fellow at the University of Liverpool. Background in cognitive robotics, learning-based robot manipulation and applied computer vision. She is keen to collaborate on new Chemistry applications using robotics systems and learn from experts about AI for improving robotic chemists.

Expertise in machine learnt quantum mechanical and force-field models of molecules and materials. Considerable background in both periodic and vacuum electronic structure methods, particularly as applied to excited states in semiconductors. He is most keen to develop collaborations and apply this expertise into material and molecule design via Alchemy. 

Kourosh Darvish is a postdoctoral researcher at the University of Toronto with expertise in human-robot collaboration and AI. His research focuses on developing intelligent robotic systems capable of assisting humans in complex tasks, such as those found in chemistry laboratories.

Gilian is a Lecturer in the School of Chemistry at the University of Leeds. Her group’s research combines high-throughput experimentation with real-time mass spectrometry to discover and optimise base-metal catalysts, uncovering mechanistic insights even at ultra-low loadings. By integrating expansive datasets with kinetic profiling, her group is building predictive models to map and accelerate base metal-catalysed transformations across unexplored chemical space.

Emma Wolpert is a Leverhulme Early Career Fellow in the Department of Chemistry at UCL. She develops coarse-grained statistical mechanical models and integrates them with machine learning to predict and design how molecular materials assemble into targeted phases.