AI3SD Winter Seminar #3: Enhancing Experiments through Machine Learning Talk 1 - Dr Keith Butler

This video forms part of the AI3SD Winter Seminar Series 20/2021.

This video is the first talk in the third seminar of the series: Enhancing Experiments through Machine Learning.

Interpretable machine learning for materials design and characterization – Dr Keith Butler

Abstract: In a plenary lecture at a recent international conference, one leading researcher in theoretical chemistry remarked “at least 50% of the machine learning papers I see regarding electronic structure theory are junk, and do not meet the minimal standards of scientific publication”, specifically referring to the lack of insight in many publications applying ML in that field. But is knowledge inevitably lost in machine learning studies, if not how can it be extracted and how does this apply to machine learning in the context of materials science? In this talk I will look at how we can open up black box machine learning models, to understand the results and gain confidence in predictions. I will present topical examples from designing new dielectric crystals, understanding inelastic neutron scattering data and trusting deep neural networks for tomographic reconstruction. By understanding how and why these models work, we can trust the results and even discover new physical relationships.

Bio: Keith Butler is as a senior data scientist working on materials science research in the SciML team at Rutherford Appleton Laboratory. SciML is a team in the Scientific Computing Division and we work with the large STFC facilities (Diamond, ISIS Neutron and Muon Source and Central Laser Facility for example) to use machine learning to push the boundaries of fundamental science.

This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1