This project, funded by the Department of Energy, integrates machine-learned force field-driven molecular dynamics simulations with X-ray absorption experiments at DOE synchrotron facilities. The goal is to validated the MLFFs for simulating nanoparticle and nanoparticle-adsorbate interactions over reaction-relevant time scales. Once fully developed, MLFFs will enable fast and accurate materials simulations, which will dramatically accelerate the discovery of new materials for the energy sector and beyond.
Machine learned force fields (MLFFs) are extremely exciting tools capable of simulating materials systems from aa single atom to billions of atoms with quantum mechanical accuracy over nanoseconds to milliseconds. In principle, entire processes, for example catalytic reactions, can be modeled in one simulation. However, a method for the validation of the bonding dynamics that MLFFs produce is lacking. This project aims to address this challenge by developing a pipeline that links MLFF-driven dynamic simulations with simulated and experimental spectroscopy.
X-ray absorption spectroscopy (XAS) is appealing for this task because the XAS spectrum encodes "snapshots" of all atomic configurations around the absorbing atoms in the material. By calculating the XAS spectrum on molecular dynamics trajectories, it is possible to benchmark the MLFF that drives the MD trajectories. If the simulated XAS matches experiment, it is evidence that MLFF is accurately capturing the bonding dynamics of the material.
Now that a method exists for validating MLFF dynamics, we are able to screen MLFFs for their ability to capture atomic mechanisms.