Machine Learning in Materials Science
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The authors demonstrate how to build more robust models, how to make sure that your colleagues trust the results, and how to use ML to accelerate or augment simulations, by introducing methods in which ML can be applied to analyze and process experimental data. They also cover how to build integrated closed-loop experiments where ML is used to plan the course of a materials optimization experiment and how ML can be utilized in the discovery of materials on computers.
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Keith T. Butler is a senior scientist at the Rutherford Appleton Laboratory, in the Scientific Machine Learning (SciML) team, where he leads projects that apply machine learning to the discovery and characterisation of materials. Keith obtained his bachelor's from Trinity College Dublin and his PhD from University College London. Before joining RAL, Keith spent time as a post-doctoral researcher in the groups of Aron Walsh (University of Bath/Imperial College London) and John Harding (University of Sheeld), and was a visiting researcher in The University of Toronto and Tokyo Institute of Technology. Keith's research focuses on using machine learning to accelerate the characterisation of materials and also to predict new, previously undiscovered materials for renewable energy applications, such as photovoltaics and photocatalysts. Keith is an active developer of several open source materials design packages (SMACT, SuperResTomo, Macro-Density) and a strong advocate of open science.
Felipe Oviedo is an applied researcher at Microsoft AI for Good, focusing on scientific machine learning for sustainability and healthcare applications. Prior to joining Microsoft, Felipe completed a PhD at the intersection of material science and computer science at MIT under the guidance of Prof. Tonio Buonassisi (MIT Mechanical Engineering) and Dr. John Fisher (MIT CSAIL). His dissertation was focused on accelerated development of photovoltaics by physics-informed machine learning. Felipe developed and deployed machine learning algorithms to accelerate the experimental screening and optimization of renewable energy materials and technologies. Before MIT, Felipe briefly worked in the energy industry and CERN.
Pieremanuele Canepa is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He received his bachelorâs and masterâs degrees in Chemistry from the University of Torino (Italy) and a PhD from the University of Kent (UK). Prior to NUS, he was a Postdoctoral fellow at the Lawrence Berkeley National Laboratory and the Massachusetts Institute of Technology under the guidance of Prof. Gerbrand Ceder. His research contributes to the rational design of materials for clean energy technologies, including electrode materials for batteries, and electrolytes for sustainable energy storage devices. In 2021, Pieremanuele was elected as fellow of the Royal Society of Chemistry.
