Rebecca K. Lindsey

Assistant Professor of Chemical Engineering

NCRC B28 3005E
T: (734) 763-5142


University of Minnesota, Twin Cities, PhD Chemical Physics, 2016
University of Minnesota, Twin Cities, MS Chemical Physics, 2012
Wayne State University, BS Chemical Engineering, 2010


Dr. Lindsey’s group studies chemistry in inherently multiscaled systems and material evolution under extreme and dynamically changing conditions through advanced simulations and machine learning.

Nanomaterial design and discovery: There is tremendous interest in achieving tunable synthesis of nanomaterials due to their utility in fields spanning quantum computing, catalysis, energy, drug delivery, national security, and more. Shock-compression methods are particularly attractive for nanocarbon synthesis since they can produce products extremely rapidly and enable access to exotic allotropes. However, they remain far from tunable due to the limited understanding of the governing phenomena resulting from difficulties of driving materials from ambient to extreme conditions and observing the rapid, reaction-driven, and multiscaled system evolution. To circumvent these challenges, our group develops and applies advanced simulation approaches enabling an otherwise inaccessible atomistically resolved view of ensuing phenomena. Using these methods, we aim to establishing rational design principles and tools for tunable shock-synthesis of next generation nanocarbon materials.

Machine Learning and Data Science: Atomistic simulations can be a powerful tool for generating scientific insights, guiding, and aiding in interpretation of experiments, and generating input for larger scale, lower resolution simulations (e.g., continuum modeling), but they rely on availability of suitable interatomic models. Atomistic simulations have historically struggled to describe chemistry in condensed phase and inherently multiscaled systems (i.e., those central to H-storage system design, sorbent optimization, heterogenous catalysis, etc.), since they necessitate both the accuracy of quantum-mechanics and computational efficiency of relatively simple molecular mechanics models. To bridge this capability gap, our group develops AI-driven simulation tools enabling agile generation of robust machine-learned interatomic models for quantum-accurate reactive simulation on unprecedented scales. Our group also develops machine learning tools to aid in interpretation of large experimental datasets and to develop material diagnostic and performance models from them.

Chemistry under extreme conditions: Chemistry occurring under extreme and dynamically changing conditions has implications for a myriad of applications spanning next-generation material processing techniques (e.g., laser ablation, sonication and electron-beam modification), defense applications (e.g., detonation science), and sustainable energy (e.g., nuclear power). To facilitate advances in these areas, our group employs multiscale simulation approaches probing ensuing dynamics, equation of state, thermochemistry, and microstructural evolution.