Joint Discrete-Continuous Flow Matching for Open-Vocabulary Inverse Design of Multilayer Optical Coatings
arXiv:2607.08392v1 Announce Type: cross Abstract: 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-materia
The convergence of advanced AI techniques like flow-matching with materials science is enabling novel approaches for inverse material design, a long-standing challenge. This development reflects progress in making AI design more open-ended and less constrained by predefined parameters.
This development allows for more flexible and creative material design, moving beyond fixed component vocabularies and discrete variables, which can accelerate innovation in fields like optics and semiconductors. It represents a leap in AI's capability to assist in complex engineering inverse problems.
AI-driven materials design is becoming 'open-vocabulary,' meaning it can generate designs from a continuous space of possibilities rather than being limited to predefined components. This significantly broadens the scope and efficiency of AI in discovering new materials with desired properties.
- · Materials scientists
- · Optical coating manufacturers
- · AI researchers
- · Semiconductor industry
- · Traditional materials discovery methods
- · Trial-and-error R&D
Accelerated discovery of novel materials and functional coatings for electronics, optics, and energy applications.
Reduced cost and time-to-market for new high-performance products reliant on advanced material properties.
Potential for AI to design materials with unprecedented properties, enabling entirely new technological paradigms.
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Read at arXiv cs.LG