基于机器学习的SF6替代气体的预测
Guocheng Ding1, Wei Liu1, Mengxuan Ling2
1Electric Power Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei, Anhui 230601, P. R. China.
ACS omega
|October 27, 2025
概括
机器学习模型通过分析大气寿命和辐射效率,准确地预测SF6替代品的全球变暖潜力 (GWP). 这使得可用于工业应用的环保更安全气体的高通量选成为可能.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 全球变暖潜力 (GWP) 对于评估SF6替代品至关重要.
- 辐射效率 (RE) 和大气寿命 (τ) 决定了气体的GWP.
- 需要高通量选来识别合适的SF6替代气体.
研究的目的:
- 开发和优化机器学习 (ML) 模型,用于预测GWP,t和RE.
- 探索分子描述符和GWP参数之间的相关性.
- 使用开发的ML模型选潜在的SF6替代气体.
主要方法:
- 使用了六种修改后的机器学习方法,包括直方图梯度增强回归,梯度增强回归和极端树.
- 采用分子描述器来分析影响t和RE的关系.
- 应用训练有素的模型来选QuandDB数据集中的853个分子的GWP100.
主要成果:
- 最优的ML模型实现了高准确度 (R2>0.90) 预测GWP,t和RE.
- 确定了占有率最高的分子轨道及其能量差距作为影响t和RE的关键分子描述因素.
- 选了853个分子,确定了6个具有可取性质的有希望的SF6替代候选者.
结论:
- 开发了准确的ML模型来预测GWP及其关键参数,促进SF6替代品的高效选.
- 提供了对气体对环境影响的分子层面因素的见解.
- 确定了具有有利物理和电气性能的特定低GWP SF6 替代品.
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