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Ultrafast inverse design of quantum dot optical spectra via a joint TD-DFT learning scheme and deep reinforcement learning

Authors :
Hibiki Yoshida
Katsuyoshi Sakamoto
Naoya Miyashita
Koichi Yamaguchi
Qing Shen
Yoshitaka Okada
Tomah Sogabe
Source :
AIP Advances, Vol 12, Iss 11, Pp 115316-115316-9 (2022)
Publication Year :
2022
Publisher :
AIP Publishing LLC, 2022.

Abstract

Here, we report a case study on inverse design of quantum dot optical spectra using a deep reinforcement learning algorithm for the desired target optical property of semiconductor CdxSeyTex−y quantum dots. Machine learning models were trained to predict the optical absorption and emission spectra by using the training dataset by time dependent density functional theory simulation. We show that the trained deep deterministic policy gradient inverse design agent can infer the molecular structure with an accuracy of less than 1 Å at a fixed computational time of milliseconds and up to 100–1000 times faster than the conventional heuristic particle swam optimization method. Most of the effective inverse design problems based on the surrogate machine learning and reinforcement learning model have been focused on the field of nano-photonics. Few attempts have been made in the field of quantum optical system in a similar manner. For the first time, our results, to our knowledge, provide concrete evidence that for computationally challenging tasks, a well-trained deep reinforcement learning agent can replace the existing quantum simulation and heuristics optimization tool, enabling fast and scalable simulations of the optical property of nanometer sized semiconductor quantum dots.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
21583226
Volume :
12
Issue :
11
Database :
Directory of Open Access Journals
Journal :
AIP Advances
Publication Type :
Academic Journal
Accession number :
edsdoj.611f75ed29504deeb6d5798002d0f5e2
Document Type :
article
Full Text :
https://doi.org/10.1063/5.0127546