A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories

TL;DR

This paper introduces a physics-based, data-driven model for optimizing gas diffusion electrodes in CO2 electroreduction, enabling efficient exploration of design parameters in automated laboratories.

Contribution

It presents a flexible, interpretable modeling framework with uncertainty quantification, tailored for active learning in GDE optimization using experimental data.

Findings

Model effectively captures GDE behavior with Tafel kinetics.

Uncertainty-aware Gaussian process enhances model accuracy.

Framework accelerates parameter space exploration in automated labs.

Abstract

The electrochemical reduction of atmospheric CO$_2$ into high-energy molecules with renewable energy is a promising avenue for energy storage that can take advantage of existing infrastructure especially in areas where sustainable alternatives to fossil fuels do not exist. Automated laboratories are currently being developed and used to optimize the composition and operating conditions of gas diffusion electrodes (GDEs), the device in which this reaction takes place. Improving the efficiency of GDEs is crucial for this technology to become viable. Here we present a modeling framework to efficiently explore the high-dimensional parameter space of GDE designs in an active learning context. At the core of the framework is an uncertainty-aware physics model calibrated with experimental data. The model has the flexibility to capture various input parameter spaces and any carbon products which can be modeled with Tafel kinetics. It is interpretable, and a Gaussian process layer can capture deviations of real data from the function space of the physical model itself. We deploy the model in a simulated active learning setup with real electrochemical data gathered by the AdaCarbon automated laboratory and show that it can be used to efficiently traverse the multi-dimensional parameter space.