Abstract

The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries. Extensive efforts have been devoted toward accelerating and facilitating this process, not only experimentally but also from the viewpoint of materials design. Recently, machine learning has attracted considerable attention, as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge. In this regard, here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design. Inspired by neural machine language translation, the deep neural network encoder extracts hidden features between molecular structures and their material properties, while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties. In material design tasks, the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules.

Details

Title
Deep-learning-based inverse design model for intelligent discovery of organic molecules
Author
Kim, Kyungdoc 1 ; Kang, Seokho 2 ; Yoo, Jiho 1 ; Kwon, Youngchun 1 ; Nam, Youngmin 1 ; Lee, Dongseon 1 ; Kim, Inkoo 1 ; Youn-Suk Choi 1 ; Jung, Yongsik 1 ; Kim, Sangmo 1 ; Won-Joon Son 1 ; Son, Jhunmo 1 ; Hyo Sug Lee 1 ; Kim, Sunghan 1 ; Shin, Jaikwang 1 ; Hwang, Sungwoo 1 

 Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., Gyeonggi-do, Republic of Korea 
 Department of Systems Management Engineering, Sungkyunkwan University, Gyeonggi-do, Republic of Korea 
Pages
1-7
Publication year
2018
Publication date
Dec 2018
Publisher
Nature Publishing Group
e-ISSN
20573960
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2148968942
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.