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Workshop: AI for Science: from Theory to Practice

Reinforcement Learning-Enabled Environmentally Friendly and Multi-functional Chrome-looking Plating

Taigao Ma · Anwesha Saha · L. Jay Guo · Haozhu Wang


Although decorative chrome plating (DCP) is ubiquitous in metal finishings and coatings, the industrial process of chromium deposition is fraught with adverse health effects for the workers involved and causes environmental pollution. In this work, we seek to find an environmentally friendly replacement to DCP by mimicking the chrome color used for decoration. To discover a suitable replacement efficiently, we employ a reinforcement learning (RL) algorithm to perform an automatic inverse design in optical multilayer thin film structures. The RL algorithm successfully figures out two different structures with environmentally friendly materials while still showing a chrome color. One structure is further designed to have high transmission in the radio frequency regime, a property that general metals cannot have, which can broaden the decorative chrome applications to include microwave operating devices. We also experimentally fabricate these structures and validate their performance.

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