Jeweler-in-the-Loop: Personalized Alloy Color Optimization via Preference-Based BO

Abstract

Many materials design attributes that are central to adoption, such as aesthetics, perceived quality, or user-specific preferences, are difficult to quantify directly, making preference feedback a practical proxy for optimization. Here we present a preference-driven Bayesian optimization framework and demonstrate it using ring color as a concrete case study. This case study simulates a scenario in which a jeweler synthesizes a ring with a given chemistry and presents it to a stakeholder, who expresses a preference relative to an incumbent ring color. Our approach, preferential Bayesian optimization (PBO), learns a latent utility function over alloy composition space, with perceived color obtained via a Thermo-Calc optical forward model. Using preference feedback, the framework iteratively proposes new alloy chemistries predicted to better align with user aesthetic preferences. After a limited number of proposed alloys and their associated colors, the user selects the most desirable option, guiding the search toward optimal aesthetic outcomes. We then evaluate the cost of the proposed alloys and identify a cost–aesthetic Pareto front, enabling informed trade-offs between affordability and visual appeal. The proposed framework is readily applicable to materials design problems in which subjective or hard-to-measure attributes play a dominant role in decision-making.