A special seminar hosted by the DSI Materials Discovery Analytics Working Group

Guest Speaker

Steven Torrisi, Senior Research Scientist, Toyota Research Institute

Hosted by:

  • Simon Billinge, Professor of Materials Science and Applied Physics and Applied Mathematics, Columbia Engineering
  • Tina Na Narong, Postdoctoral Research Scientist, Data Science Institute
  • Sanat Kumar, Michael Bykhovsky and Charo Gonzalez-Bykhovsky Professor of Chemical Engineering, Columbia Engineering

Event Details

Friday, March 22, 2024 (11:00 AM – 12:00 PM ET) – In-Person Only

Location: 414 CEPSR – 4th Floor (Campus Level) – 530 W 120th St, New York, NY 10027


Talk Information

Machine Learning & Data Science in Materials Design and Discovery

Abstract: Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. Here, I present some ways forward that myself and members of the Energy & Materials team at Toyota Research Institute have used to accelerate the design and discovery of new functional materials. Case studies will draw from studies of predicting synthesizability using databases of thermochemical data, the development of new architectures and representations for working with device-level data, and the role of first-principles methods in the process. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices.