scholarly journals Deep Learning for Optoelectronic Properties of Organic Semiconductors

2020 ◽  
Vol 124 (13) ◽  
pp. 7048-7060 ◽  
Author(s):  
Chengqiang Lu ◽  
Qi Liu ◽  
Qiming Sun ◽  
Chang-Yu Hsieh ◽  
Shengyu Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Organic Semiconductors ◽  
Optoelectronic Properties

Recent Advance in 1-D Organic Semiconductors for Waveguide Applications

2019 ◽  
Vol 16 (3) ◽  
pp. 244-252 ◽  
Author(s):  
Rong Zhang ◽  
Xiaobei Jin ◽  
Xuwen Wen ◽  
Qi Chen
Keyword(s):  
Small Molecules ◽  
Organic Semiconductors ◽  
Recent Progress ◽  
Optoelectronic Properties ◽  
Size Reduction ◽  
One Dimensional ◽  
Good System ◽  
Ordered Nanostructures ◽  
Morphology Influence

One dimensional (1-D) micro-/nanostructures provide a good system to investigate the dependence of various properties on dimensionality and size reduction, especially in optoelectronic field. Organic conjugates including small molecules and polymers exhibit good optoelectronic properties and are apt to assemble into ordered nanostructures with well-defined shapes, tunable sizes and defect-free structures. In this review, we focus on recent progress of 1-D organic semiconductors for waveguide applications. Fabrication methods and materials of 1-D organic semiconductors are introduced. The morphology influence on the properties is also summarized.

Effect of Monofluoro Substitution on the Optoelectronic Properties of Benzo[c][1,2,5]thiadiazole Based Organic Semiconductors

2016 ◽  
Vol 49 (16) ◽  
pp. 5806-5816 ◽  
Author(s):  
Wenkai Zhong ◽  
Junfei Liang ◽  
Shuzhi Hu ◽  
Xiao-Fang Jiang ◽  
Lei Ying ◽  
...  
Keyword(s):  
Organic Semiconductors ◽  
Optoelectronic Properties

Substitution degree engineering the crystal packing and optoelectronic properties of benzofuranvinyl-substituted benzene-cored derivatives

2014 ◽  
Vol 2 (29) ◽  
pp. 5954-5962 ◽  
Author(s):  
Jin-Xing Qiu ◽  
Ye-Xin Li ◽  
Xiao-Feng Yang ◽  
Yong Nie ◽  
Zhen-Wei Zhang ◽  
...  
Keyword(s):  
Organic Semiconductors ◽  
Crystal Packing ◽  
Molecular Shape ◽  
Optoelectronic Properties ◽  
Substitution Degree

The molecular shape exerts remarkable effects on solubility, polymorphism, crystal packing and optoelectronic properties – searching for 3D organic semiconductors.

Design of indigo derivatives as environment-friendly organic semiconductors for sustainable organic electronics

2014 ◽  
Vol 2 (36) ◽  
pp. 7621-7631 ◽  
Author(s):  
I. V. Klimovich ◽  
L. I. Leshanskaya ◽  
S. I. Troyanov ◽  
D. V. Anokhin ◽  
D. V. Novikov ◽  
...  
Keyword(s):  
Organic Semiconductors ◽  
Organic Electronics ◽  
Optoelectronic Properties ◽  
Environment Friendly ◽  
Chemical Functionalization

Chemical functionalization can be used to tune optoelectronic properties of indigoids, their stability and semiconductor performance in OFETs.

Enhancing charge mobilities in selectively fluorinated oligophenyl organic semiconductors: a design approach based on experimental and computational perspectives

2019 ◽  
Vol 7 (13) ◽  
pp. 3881-3888 ◽  
Author(s):  
Buddhadev Maiti ◽  
Kunlun Wang ◽  
Srijana Bhandari ◽  
Scott D. Bunge ◽  
Robert J. Twieg ◽  
...  
Keyword(s):  
Organic Semiconductors ◽  
Molecular Level ◽  
Optoelectronic Properties ◽  
Design Approach

Fluorination can be used to tune optoelectronic properties at the molecular level.

scholarly journals Attribute driven inverse materials design using deep learning Bayesian framework

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Piyush M. Tagade ◽  
Shashishekar P. Adiga ◽  
Shanthi Pandian ◽  
Min Sik Park ◽  
Krishnan S. Hariharan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Organic Semiconductors ◽  
High Throughput Screening ◽  
Materials Science ◽  
Structure Learning ◽  
Inorganic Compounds ◽  
Computational Design ◽  
Materials Design ◽  
Learning Approaches ◽  
The Right

AbstractMuch of computational materials science has focused on fast and accurate forward predictions of materials properties, for example, given a molecular structure predict its electronic properties. This is achieved with first principles calculations and more recently through machine learning approaches, since the former is computation-intensive and not practical for high-throughput screening. Searching for the right material for any given application, though follows an inverse path—the desired properties are given and the task is to find the right materials. Here we present a deep learning inverse prediction framework, Structure Learning for Attribute-driven Materials Design Using Novel Conditional Sampling (SLAMDUNCS), for efficient and accurate prediction of molecules exhibiting target properties. We apply this framework to the computational design of organic molecules for three applications, organic semiconductors for thin-film transistors, small organic acceptors for solar cells and electrolyte additives with high redox stability. Our method is general enough to be extended to inorganic compounds and represents an important step in deep learning based completely automated materials discovery.

Biphase micro/nanometer sized single crystals of organic semiconductors: Control synthesis and their strong phase dependent optoelectronic properties

2010 ◽  
Vol 96 (14) ◽  
pp. 143302 ◽  
Author(s):  
Chengliang Wang ◽  
Yaling Liu ◽  
Zhongming Wei ◽  
Hongxiang Li ◽  
Wei Xu ◽  
...  
Keyword(s):  
Single Crystals ◽  
Organic Semiconductors ◽  
Optoelectronic Properties ◽  
Control Synthesis ◽  
Strong Phase
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