Atomistic insight into nucleation and electrochemistry: Machine learning multiscale simulation

Developing battery technologies requires atomistic insight into electrochemistry, nucleation, and degradation, but simulation is presented with a challenging combination of lengthscale, timescale and accuracy demands.

This presents a great opportunity for Scientific Machine Learning to work closely with experimental techniques such as transmission electron microscopy, and to learn to simulate nucleation and electrochemistry processes.

In this project, we will use machine learned interatomic potentials to make simulated training data for ML models of nucleation.

This will be paired with TEM imaging that captures atomic-level electrochemical processes in situ on 2D materials as they occur and constrains and informs our models.