Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells

TL;DR

This paper develops a machine-learned interatomic potential for platinum-based fuel cell components, enabling detailed predictions of structure, transport, and reactivity, with implications for optimizing fuel cell performance.

Contribution

The study introduces a MLIP trained on diverse data for complex multicomponent systems like Nafion and platinum, highlighting challenges in active learning and demonstrating its application in fuel cell modeling.

Findings

MLIP accurately predicts polymer structure and reactions within training data.

Transport mechanisms like vehicular and Grotthuss hopping are well captured.

Converged diffusivity calculations remain challenging due to computational demands.

Abstract

Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent systems still remains a challenge. In this work, we train a MLIP to describe hydrated Nafion ionomers and platinum catalysts, which are important components of fuel cells, by constructing a diverse training set to describe the bulk polymer and interfacial catalyst-polymer interactions well. We find that active learning improves the initial dataset little in terms of reducing uncertainty and error, pointing towards a need for more effective methods to efficiently explore the relevant interactions in complex, multicomponent systems. We use our trained MLIP to study the properties of the platinum-Nafion system, including polymer structure, proton mobility in a bulk Nafion polymer and near a platinum-Nafion interface, and reactions near and far from the interface, finding excellent results for structure and reactions contained within our training set. Transport seems well described, with both vehicular transport and Grotthuss hopping captured, although converged calculations of diffusivities were not computed because they require calculations of tens of nanoseconds that are challenging with current state-of-the-art MLIPs. The combined insights that this model provides can be leveraged to optimize fuel cell performance, and the approach can be applied to other chemical processes and devices where structure, transport, and reactivity all contribute to overall observed performance.