machine learning enhanced Ab Initio Mesoscale electrochemical Simulator

Objective

The transition to a sustainable energy future depends on advanced electrochemical technologies such as batteries and fuel cells. However, the development of critical materials and interfaces for these devices is often hindered by a laborious and resource-intensive cycle of experimental trial-and-error. While a paradigm shift towards in silico rational design is underway, a significant gap persists in modeling these electrochemical interfaces at the mesoscale (1-100 nm) with predictive accuracy. Current computational methods are either too slow for this scale, like atomistic simulations, or too inaccurate, like continuum models. The AIMS project will bridge this critical gap by developing a novel computational framework: the machine learning-enhanced Ab Initio Mesoscale electrochemical Simulator (AIMS). This framework will systematically embed microscopic information from molecular dynamics simulations into efficient continuum models, synergistically combining the researcher's data-driven expertise with the host's physics-driven theoretical approach. The resulting tool will be validated on systems of increasing complexity, from simple gold surfaces to realistic platinum nanoparticle catalysts. The long-term impact of this work is substantial; it will establish a new scientific framework for electrochemical modeling, accelerate the economic R&D cycle for energy technologies, and ultimately contribute to a more sustainable future by enabling the design of low-cost, high-stability renewable energy devices.