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Deep-learning-based inverse design model for intelligent discovery of organic molecules
npj Computational Materials ( IF 9.4 ) Pub Date : 2018-12-03 , DOI: 10.1038/s41524-018-0128-1
Kyungdoc Kim , Seokho Kang , Jiho Yoo , Youngchun Kwon , Youngmin Nam , Dongseon Lee , Inkoo Kim , Youn-Suk Choi , Yongsik Jung , Sangmo Kim , Won-Joon Son , Jhunmo Son , Hyo Sug Lee , Sunghan Kim , Jaikwang Shin , Sungwoo Hwang

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.



中文翻译:

基于深度学习的逆向设计模型,用于智能发现有机分子

高性能功能材料的发现对于克服现代工业中的技术问题至关重要。不仅在实验上,而且从材料设计的角度,都致力于加速和促进该过程。最近,机器学习吸引了相当大的关注,因为它可以为有效的材料探索提供合理的指导,而无需耗时的迭代或先验的人类知识。在这方面,这里我们针对目标分子设计开发了基于深度编码器-解码器架构的逆设计模型。受到神经机器语言翻译的启发,深度神经网络编码器提取了分子结构与其材料特性之间的隐藏特征,而递归神经网络解码器将提取的特征重构为具有目标特性的新分子结构。在材料设计任务中,所提出的完全数据驱动的方法成功地从给定的数据库中学习了设计规则,并通过创建新的配体和组合规则,为磷光有机发光二极管生成了有前途的光吸收分子和主体材料。

更新日期:2019-01-26
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