溶解度
人工智能
分子描述符
机器学习
计算机科学
密度泛函理论
分子
有机分子
生物系统
生化工程
化学
计算化学
数量结构-活动关系
有机化学
工程类
生物
作者
Jinliang Wang,Asif Mahmood,Yahya Sandali
摘要
Solubility plays a critical role in many aspects of research (drugs to materials). Solubility parameters are very useful for selecting appropriate solvents/non-solvents for various applications. In the present study, Hansen solubility parameters are predicted using machine learning. More than 40 machine models are tried in the search for the best model. Molecular descriptors and fingerprints are used as inputs to get a comparative view. Machine learning models trained using molecular descriptors have shown higher prediction ability than the model trained using molecular fingerprints. Machine learning models trained using molecular descriptors have shown their potential to be easy and fast models compared to the density functional theory (DFT)/thermodynamic approach. Machine learning creates a "black box" connection to the properties. Therefore, minimal computational cost is required. With the help of the best-trained machine learning model, green solvents are selected for small molecule donors that are used in organic solar cells. Our introduced framework can help to select solvents for organic solar cells in an easy and fast way.
科研通智能强力驱动
Strongly Powered by AbleSci AI