SCHEDULED MAINTENANCE
Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning†
Junsuk
Rho 
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Abstract
By learning the optimal policy with a double deep Q-learning network (DDQN), we design ultra-broadband, biomimetic, perfect absorbers with various materials, based the structure of a moth's eye. All absorbers achieve over 90% average absorption from 400 to 1600 nm. By training a DDQN with moth-eye structures made up of chromium, we transfer the learned knowledge to other, similar materials to quickly and efficiently find the optimal parameters from the ∼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.
- This article is part of the themed collection: Emerging AI Approaches in Physical Chemistry
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