Symposium X—MRS/The Kavli Foundation Frontiers of Materials

Thursday, November 30
12:15 pm – 1:15 pm
Sheraton, 2nd Floor, Grand Ballroom

How to Build a Self-Driving Lab

Panel

Keith A. Brown
Boston University

Keith A. Brown is an associate professor of Mechanical Engineering, Materials Science & Engineering, and Physics at Boston University. The KABlab studies approaches to accelerate the development of advanced materials and structures with a focus on polymers. The group employs self-driving labs, additive manufacturing, scanning probe techniques, and machine learning to achieve these goals. Brown has co-authored over 100 peer-reviewed publications and six issued patents. He has received the Frontiers of Materials Award from The Minerals, Metals & Materials Society (TMS), been named a “Future Star of the AVS,” and received the Omar Farha Award for Research Leadership from Northwestern University. Brown served on the Nano Letters Early Career Advisory Board, co-organized a National Academies of Sciences, Engineering, and Medicine Workshop on AI for Scientific Discovery, and currently leads the Materials Research Society Artificial Intelligence for Materials Development Staging Task Force. 

John Dunlap
UES, Inc. and Air Force Research Laboratory 

Robert Epps
National Renewable Energy Laboratory

Robert Epps joined the Materials Science Center as a postdoctoral researcher in June 2022. He is an expert in data-driven research and autonomous experimentation system development. Robert received his bachelor’s in chemical engineering from Vanderbilt University, where he worked to develop void space tracking algorithms for micro concrete samples with nuclear waste containment applications. He then received his master’s and doctorate from North Carolina State University, where he developed autonomous microfluidic technologies for high-throughput analysis of colloidal nanocrystal reactions and machine learning driven experimentation. At NREL, Robert is working to build a data integrated lab space with robotics, automation, and machine learning driven experimentation. The aim of this work is to streamline the development of advanced functional materials through human-machine integrated research.

Jason Hattrick-Simpers
University of Toronto

Kiran Vaddi
University of Washington

I am a postdoc scholar in the Department of Chemical Engineering at the University of Washington, Seattle. I am also an affiliate at the eScience Institute as a UW Data Science Postdoctoral Fellow and Co-chair the weekly postdoc seminar series at the eScience. I am working at the interface of Machine learning and Material Sciences with special focus on the realization of autonomous material discovery and design. Previously, I spent five wonderful years at the Indian Institute of Technology Madras and obtained bachelors and masters from the department of Mechanical Engineering.

My main research interests are learning representations for data-efficient scientific discovery and understanding of physical phenomenon. Representations play key role in realizing the dream of autonomous experimental design using techniques such as active learning and reinforcement learning. I am interested in developing frameworks to understand materials based on their topology and (differential) geometry that are both computationally tractable and interpretable.