通过帕雷托优化SMILES基于深度学习的高精度聚合物性质检测.
Mohammad Anwar Parvez1, Ibrahim M Mehedi2
1Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Polymers
|July 12, 2025
概括
这项研究引入了一种用于聚合物性质分类的新型AI模型,达到98.66%的准确性. 基于使用帕雷托优化算法 (SMILES-PPDCPOA) 检测和分类的简化分子输入线输入系统提供了高效和准确的聚合物信息学.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 聚合物信息学 聚合物信息学
背景情况:
- 传统的聚合物设计依赖于直觉和经验,面临着巨大的设计空间和对新材料的需求带来的挑战.
- 人工智能 (AI),特别是机器学习 (ML) 和深度学习 (DL),为加速材料设计提供了一个有希望的方法.
- 现有的ML和DL方法显示出潜力,但需要改进的模型来准确地分类聚合物和性能预测.
研究的目的:
- 设计和开发一种先进的AI模型,使用化学结构输入来对聚合物属性进行分类.
- 通过创建可扩展和特定领域的解决方案来预测材料特性来增强聚合物信息学.
- 通过捕捉聚合物结构内的复杂化学依赖,改进现有方法.
主要方法:
- 基于使用帕雷托优化算法 (SMILES-PPDCPOA) 模型检测和分类聚合物性质的简化分子输入线输入系统的开发.
- 整合一个一维的卷积神经网络 (1DCNN) 与一个封闭的循环单元 (GRU) 进行特征提取和序列建模.
- 使用帕雷托优化算法 (POA) 优化1DCNN-GRU模型的超参数,以提高性能.
主要成果:
- 在八个聚合物性质类别中,SMILES-PPDCPOA模型实现了98.66%的平均分类精度.
- 该模型展示了高精度和回忆指标,表明了强大的分类性能.
- SMILES-PPDCPOA表现出卓越的计算效率,在4.97秒内完成任务,超过了GCN-LR和ECFP-NN.N.等既定的方法.
结论:
- 拟议的SMILES-PPDCPOA模型为聚合物属性分类提供了一种新且有效的深度学习框架.
- 1DCNN,GRU和帕雷托优化的集成为聚合物信息学提供了可扩展和准确的解决方案.
- 实验验证证证实了SMILES-PPDCPOA作为推动材料科学和工程的有希望的方法的潜力.
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