Argonne National Laboratory

MAUI: Modeling, Analysis, and Ultrafast Imaging

MAUI: Modeling, Analysis, and Ultrafast Imaging

The goal of MAUI is to integrate ultrafast time-resolved imaging with large-scale molecular dynamics modeling and in situ data analysis and visualization.

Project Goals

Understanding lattice vibrations in individual nanoparticles can inspire energy applications such as photocatalysis, photonics, thermoelectrics, semiconductor design, groundwater remediation, and heat transfer in battery interfaces.

Our goal is to integrate ultrafast time-resolved imaging with large-scale molecular dynamics modeling and in situ data analysis and visualization. This will allow us to design and conduct experiments with high spatial and temporal resolution. The results of our experiments and models will provide crucial insights for energy research.

Project Details

The temporal behavior of in situ externally stimulated materials beyond equilibrium can lead to breakthroughs, for example, in heat dissipation of next-generation semiconductors, conversion of wasted heat into electricity in thermoelectric materials, and electrochemical processes across liquid-solid interfaces in water purification.

All these diverse applications share a common behavior: they transport energy through phonons (sound waves that carry heat) in a time-evolving crystal lattice. We anticipate that our integrated approach to predict, image, and analyze phonon dynamics can be applied to other externally stimulated (for example, heated, pressurized, laser-pumped, acid-dissolved, or electromagnetically induced) systems measured through various imaging techniques including X-ray, electron, and optical microscopy.

Methodology

We will use laser pump-probe imaging experiments to study the structure dynamics originating from electron-phonon interactions.

We will use molecular dynamics to model the phonon transport and lattice thermal conductivities for the materials systems that we propose to study.

In order to combine the reverse (image reconstruction) with the forward (simulation) models, we will use image analysis techniques to investigate data transformations between model spaces.