基于分子反应性描述符和机器学习方法的气体介质电强度的预测模型
Lingyun Luo1, Shuai Yang2, Zhao Yang1
1Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
Journal of molecular modeling
|January 18, 2025
概括
机器学习模型,特别是渐变增强决策树,使用分子反应性描述器准确预测气体的电强度. 与传统的统计方法相比,这种方法提供了更高的预测准确性和稳定性.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 物理化学 物理化学
背景情况:
- 气体排放的电离和吸附现象与化学电友和核友反应有相似之处.
- 分子描述器,包括静电电位描述器,对于预测环保气体的电强度至关重要.
研究的目的:
- 研究分子描述符与73种不同分子的电强度之间的相关性.
- 将统计模型的预测性能与用于预测电力强度的机器学习模型进行比较.
- 确定用于预测气体电强度的最有效的建模方法.
主要方法:
- 使用高斯16软件与M06-2X功能和def2基础集进行了分子结构的优化.
- 通过使用Multiwfn软件的波函数分析获得了分子表面描述符.
- 多重线性回归,多项式回归,K-最近邻居,随机森林和梯度增强决策树被用于预测建模.
主要成果:
- 机器学习模型在预测准确性和稳定性方面通常优于统计模型.
- 渐变增强决策树在所有测试模型中表现最好.
- 梯度增强决策树模型在1000次训练代后,在测试组中实现了0.864的确定系数和0.105的平均平方误差.
结论:
- 分子反应性描述器与机器学习方法相结合,为预测气体介质的电强度提供了有效的策略.
- 梯度增强决策树代表了这个预测任务的高度准确和稳定的方法.
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