气体介质的绝缘强度的预测模型,考虑到外部电场的影响
Shaobo Wu1, Shuai Yang2, Lingyun Luo1
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
|November 23, 2024
概括
这项研究通过在外部电场下分析分子描述符来增强气体的绝缘强度预测. 机器学习,特别是随机森林,通过结合这些现场效应,显著提高了预测准确性.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 电气工程 电气工程 电气工程
背景情况:
- 提高气体介电材料的绝缘强度预测是至关重要的.
- 外部电场影响分子显微镜描述器,影响绝缘性能.
研究的目的:
- 研究外部电场对分子描述器的影响.
- 为了提高气体介质的绝缘强度预测模型.
主要方法:
- 优化了71个气体分子使用高斯式16与M06-2X和6-311++G(d,p) 基础集.
- 在外部电场的存在下分析全球和本地分子描述符.
- 使用回归和机器学习模型进行相关性分析.
主要成果:
- 对描述器的外部电场影响提高了绝缘强度预测的准确性.
- 随机森林模型展示了最高的准确性.
- 随机森林模型在测试组中获得了0.9239的平均R2和0.0346的MSE.
结论:
- 考虑到外部电场对分子描述器的影响,可以显著提高绝缘强度的预测.
- 机器学习模型,特别是随机森林,对于预测气体介质中绝缘强度是有效的.
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