# Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.12465 · HTML: https://mlsanity.com/paper/1910.12465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures](https://mlsanity.com/paper/2209.04447/) (2022)
- [Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization](https://mlsanity.com/paper/2506.20056/) (2025)

## Abstract (arXiv)

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.