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arXiv · 1910.12465

Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning

Trevon Badloe·Inki Kim·Junsuk Rho
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Abstract

By learning the optimal policy with a double deep Q-learning network, we design ultra-broadband, biomimetic, perfect absorbers with various materials, based the structure of a moths eye. All absorbers achieve over 90% average absorption from 400 to 1,600 nm. By training a DDQN with motheye structures made up of chromium, we transfer the learned knowledge to other, similar materials to quickly and efficiently find the optimal parameters from the around 1 billion possible options. The knowledge learned from previous optimisations helps the network to find the best solution for a new material in fewer steps, dramatically increasing the efficiency of finding designs with ultra-broadband absorption.

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Trevon Badloe, Inki Kim, Junsuk Rho. 2019-10-28. Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning. https://doi.org/10.1039/c9cp05621a

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