.png){kind=icon}
返回
Artificial Intelligence for Conjugated Polymers
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.chemmater.3c02358.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Conjugated polymers have garnered significant attention due to their diverse applications in electronics, photonics, and energy storage. However, realizing their full potential poses a formidable challenge, as their design has historically relied on iterative adjustments and continuous inspiration from researchers. Traditional methods often struggle to efficiently navigate their vast chemical landscape. Herein, the application of artificial intelligence (AI), specifically machine learning (ML), needs to be discussed in the realm of conjugated polymers. Our paper emphasizes the importance of understanding the structure-property relationships of these polymers and how ML can facilitate property prediction and inverse-design. We delve into various chemical fingerprints, structural descriptors, and ML algorithms, showcasing their utility across a spectrum of applications, including simulations, glass transition temperature determination, photovoltaics, reorganization energy for charge transport, photocatalysts, and sensors. Finally, we give some outlooks in this filed and propose unexplored areas within the field that hold the potential to benefit from ML techniques.
Keyword:
MOLECULAR-DYNAMICS SIMULATIONS
GLASS-TRANSITION TEMPERATURE
CONDUCTING ORGANIC POLYMERS
FIELD-EFFECT TRANSISTOR
CLEAN ENERGY PROJECT
DESIGN
PHOTOVOLTAICS
DERIVATIVES
CONVERSION
PREDICTION
期刊
IF:
7
论文数:
2.8W
被引数:
11.4W
机构
引用论文
The Construction of a Neurodynamic Structure of an Adaptive Sensor for Measuring Physical Parameters
PREAI
分享收藏Position Estimation of a PMSM in an Electric Propulsion Ship System Based on High-Frequency Injection.png)
Electronics
OAAI
ElectronicsIF0
OAAI分享收藏Comparing BMD‐derived genotoxic potency estimations across variants of the transgenic rodent gene mutation assay在转基因啮齿动物基因突变试验的变体中比较bmd衍生的遗传毒性效力估计OAAI
OAAI分享收藏Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery从哈佛清洁能源项目中学习: 使用神经网络加速材料发现
PREAI
分享收藏Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review基于机器学习的聚合物和消费后再生聚合物性能预测: 综合评述
PREAI
分享收藏
查看更多内容