Data and ML Driven Analysis of Atomic Dynamics in Energy Materials

2024

A team of students led by researchers in Energy Materials and Machine Learning groups, supervised by Prof. Olivier Delaire and Prof. David Carlson, will develop means to evaluate and quantity motions of atoms in novel materials for energy conversion and energy storage. This project will advance our understanding of two particularly exciting classes of materials enabling sustainable energy technologies: 1) superionic conductors, which could enable a new generation of solid-state rechargeable batteries; 2) metal halide perovskites, a novel class of candidate materials for high-efficiency, affordable solar panels. The team will use machine learning techniques to analyze large datasets from neutron and x-ray scattering experiments performed at Department of Energy national labs, and will compare them with atomistic simulations. This work will provide the basis toward understanding how to optimize materials that can address the current climate crisis.

Project Leads: Olivier Delaire, David Carlson

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Assistant Professor of Civil and Environmental Engineering