热力学标记使得CO2在小数据约束下可预测聚合物的内在微孔性
Johnathan W Campbell1, Ashley P DeRegis1, Jeffrey A Laub1
1Department of Chemistry, University of Tennessee, Knoxville, Tennessee, USA.
Macromolecular rapid communications
|September 30, 2025
概括
数据驱动型号使用计算描述器准确预测多孔聚合物中的二氧化碳吸附强度. 这加速了用于高效碳捕获技术的先进聚合物膜的设计.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 多孔聚合物,特别是内在微孔性聚合物 (PIMs),由于其高表面积和可调节性质,对于二氧化碳捕获至关重要.
- 吸附的同质热量 (Qst) 是一个关键参数,表明CO2-聚合物相互作用强度,对于吸附剂设计至关重要.
研究的目的:
- 开发精确的数据驱动模型,用于使用计算物理化学描述器预测PIM中的Qst.
- 确定影响PIM中CO2吸附强度的关键分子特征.
- 选PIM以提高二氧化碳捕获能力.
主要方法:
- 使用了机器学习模型,包括带有辐射基函数 (RBF) 的内核回归 (KRR),拉索回归和XGBoost.
- 贝叶斯优化用于模型参数调整.
- 用SHAP分析来解释特征重要性和选PIM.
主要成果:
- 对于Qst预测,KRR RBF模型获得了高R2得分 (0.9854训练,0.9653测试).
- 一个整体模型表现出强大的预测性能 (0.9844训练,0.9651测试).
- 密度被确定为增强Qst的最有影响力的描述因素,并确定了最佳组合.
结论:
- 使用适度数据集的数据驱动工作流可以准确预测Qst并指导聚合物吸附剂的设计.
- 该方法有助于发现用于二氧化碳捕获的先进聚合物膜.
- 这种可适应的方法可以扩展到其他气体分离挑战.
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