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This paper introduces IrisFlow, an innovative open-vocabulary flow-matching framework for the inverse design of multilayer optical coatings, allowing for dynamic selection of materials and layer configurations at query time. By utilizing discrete flow matching for material sequences and continuous flow matching for layer thicknesses, IrisFlow effectively reconstructs target optical responses across a diverse 224-task benchmark, achieving high fidelity without retraining on a held-out material bank. Notably, the framework successfully designs and fabricates color-displaying coolers with minimal color error while maintaining excellent solar reflectance, showcasing its practical applicability in real-world scenarios.
IrisFlow can dynamically design multilayer optical coatings with unprecedented flexibility, reconstructing target spectra and fabricating devices with minimal error.
Amortized neural inverse design typically remains closed-world: component choices are fixed vocabulary tokens, coordinate grids are frozen at training time, and continuous variables are discretized into sequence tokens. Multilayer optical coatings are an industrially important instance, coupling material sequence, layer thickness and wavelength-dependent response. We present IrisFlow, a query-based, open-vocabulary flow-matching framework instantiated in coatings: the target reflectance/transmittance spectrum, wavelength grid, candidate-material optical constants and layer count are supplied at query time. Candidate materials enter as wavelength-aware optical tokens rather than learned identities; material sequences are sampled by discrete flow matching over the query's candidate bank, thicknesses by continuous flow matching without discretization. A single 136M-parameter model designs 2-100-layer stacks. Across a 224-task benchmark it reconstructs in-distribution targets faithfully and retains same-order accuracy on a 15-material held-out bank without retraining; it reconstructs bands up to 1100 nm beyond its training envelope, designs against analytic application specifications and outperforms an autoregressive baseline on that baseline's material library. With optical constants calibrated to our deposition process, IrisFlow designs four color-displaying coolers, fabricated by ion-assisted evaporation: the three chromatic devices reach a CIEDE2000 color error of 3.1-5.2 while retaining 93-95% solar near-infrared reflectance, demonstrating open-vocabulary design carried through to fabricated coatings.
