Issue 4, 2020

Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning

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.

Graphical abstract: Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning

Supplementary files

Article information

Article type
Paper
Submitted
16 Dit 2019
Accepted
02 Qun 2020
First published
02 Qun 2020

Phys. Chem. Chem. Phys., 2020,22, 2337-2342

Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning

T. Badloe, I. Kim and J. Rho, Phys. Chem. Chem. Phys., 2020, 22, 2337 DOI: 10.1039/C9CP05621A

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