Autopylot: Pragmatic Benchmarking of Excited-State Electronic Structure

Accurate excited-state modeling in photochemical studies hinges on the choice of the electronic structure method, which governs predicted pathways and mechanistic reliability. Yet this selection remains a major challenge, typically relying on chemical intuition and manual screening at a single geometry while overlooking broader regions of the potential energy surface. To overcome these limitations, we developed Autopylot, a Python package that automates excited-state benchmarking by comparing single-structure absorption spectra against a reference across multiple geometries, targeting accurate descriptions of both the Franck-Condon region and excited-state minima. Designed for flexibility, Autopylot supports the seamless addition of new geometry types and electronic structure methods. Reflecting a pragmatic philosophy, it incorporates computational time as a metric, guiding users toward optimal cost-accuracy trade-offs upon request. We benchmarked Autopylot on a set of 28 small organic molecules, where it consistently identified methods that closely reproduce the reference spectra within minutes. This performance marks a major step toward the high-throughput, automated selection of excited-state electronic structure methods.