A Multi-Fidelity Machine Learning Approach to High Throughput Materials Screening
- Clyde Fare
- Peter Fenner
- et al.
- 2022
- npj Computational Materials
Dr Edward O. Pyzer-Knapp is the UK lead for AI and Machine Learning Research. He obtained his PhD from the University of Cambridge using state of the art computational techniques to accelerate materials design. He then moved to Harvard where he was in charge of the day-to-day running of the Harvard Clean Energy Project - a collaboration with IBM which combined massive distributed computing, quantum-mechanical simulations, and machine-learning to accelerate discovery of the next generation of organic photovoltaic materials. He made the trip back across the pond to lead the large-scale machine learning effort at IBM Research UK, with a goal of generating step-changes in the way industries do research through the use of high-performance cognitive computing techniques such as Bayesian optimization and deep learning.
Edward has published his work in high-impact journals such as the Journal of the American Chemical Society, Chemical Science, and Advanced Functional Materials and has been an invited speaker at multiple international conferences. He is also the author of two book chapters - 'What is High-Throughput Virtual Screening: A Perspective from Organic Materials Discovery' (Annual Reviews series) and 'Cognitive Chemistry? The Marriage of Machine Learning and Chemistry to Accelerate Materials Discovery' (Materials Informatics, Wiley, to be published in 2018) and a textbook to be published in 2019.
Recently, Edward was given the honary position of Visiting Professor of Industrially Applied AI at the University of Liverpool. He supervises PhD students at Imperial College London, and Liverpool.
For more project information please see the UK Machine Learning Group's website