A team presents an end-to-end framework to assess whether quantum computing can deliver a real advantage on an industrial materials problem: screening strongly correlated oxygen-electrode materials for solid oxide fuel cells (SOFCs) and solid oxide electrolysers (SOECs). The goal is to identify compounds that improve oxygen surface exchange and ionic conduction, two properties that currently limit the performance of these devices.

The problem is formulated around interface observables: oxygen-vacancy formation energies, vacancy migration barriers and electronic conductivity. These quantities would be obtained through a diagrammatic GW-based embedding scheme, either GW+DMFT or self-energy embedding theory (SEET), applied to a shortlist pre-screened with density functional theory (DFT). The atomistic electrode/electrolyte interface defines the electronic structure problem from which the correlated impurity models are constructed.

The bottleneck lies in the quantum impurity solvers, which must provide finite-temperature Green's functions. As the interacting active space grows, classical solvers face increasing difficulties, while quantum Hamiltonian simulation scales polynomially for efficiently represented discretized models: this is where the potential quantum advantage lies. The authors identify the benchmarks needed (convergence of vacancy and migration energetics, comparison with the best applicable classical solvers), provide illustrative logical qubit counts and detail the costs of thermal state preparation, simulation, measurement and repeated solves for a complete resource assessment.

The work also connects microscopic observables to macroscopic engineering metrics through a continuum model for mixed ionic-electronic conducting oxygen electrodes that couples oxygen surface exchange, solid-state oxygen transport, microstructure and finite electrode thickness. Finally, it estimates the potential worldwide business value of improved electrode materials under SOFC and SOEC deployment scenarios, using public technical targets and electrolyser deployment data as reference.