Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning.
Phys Chem Chem Phys
; 22(4): 2337-2342, 2020 Jan 28.
Article
en En
| MEDLINE
| ID: mdl-31932814
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 â¼1 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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Phys Chem Chem Phys
Asunto de la revista:
BIOFISICA
/
QUIMICA
Año:
2020
Tipo del documento:
Article