Applications:
Complex Systems, Engineering, Physical Science
Methodologies:
Artificial Intelligence, Computer Vision, Machine Learning, Mathematical and Statistical Modeling

Rebecca Lindsey

Chemical Engineering

Assistant Professor of Chemical Engineering and Assistant Professor of Nuclear Engineering and Radiological Sciences, College of Engineering

Research in the Lindsey Lab focuses on using simulation to enable on-demand design, discovery, and synthesis of bespoke materials.

These efforts are made possible by Dr. Lindsey’s ChIMES framework, which comprises a unique physics-informed machine-learned (ML) interatomic potential (IAP) and artificial intelligence-automated development tool that enables “quantum accurate” simulation of complex systems on scales overlapping with experiment, with atomistic resolution. Using this tool, her group elucidates fundamental materials behavior and properties that can be manipulated through advanced material synthesis and modification techniques. At the same time, her group develops new approaches to overcome grand challenges in machine learning for physical sciences and engineering, including: training set generation, model uncertainty quantification, reproducibility and automation, robustness, and accessibility to the broader scientific community. Her also group seeks to understand what the models themselves can teach us about fundamental physics and chemistry.

Artists interpretation of a new laser-driven shockwave approach for nanocarbon synthesis predicted by ChIMES simulations and later validated experimentally.