胆化-尿素的σ-配置特征和无限稀释活动系数之间的相关性 DES:实验性确定和机器学习解释
Xinpeng Bi1, Dezhi Cao1, Xinyue Wang1
1State Key Laboratory of Chemistry and Utilization of Carbon-Based Energy Resources, College of Chemical Engineering, Xinjiang University, Urumqi 830017, China.
The journal of physical chemistry. B
|October 1, 2025
概括
这项研究使用反向气相色谱和机器学习来预测深层欧性溶剂相互作用. 开发的XGBoost模型准确预测溶剂行为,帮助绿色溶剂设计和工业分离过程.
科学领域:
- 物理化学 物理化学
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 深度环氧溶剂 (DES) 为绿色化学应用提供可调节的特性.
- 了解溶解物-DES相互作用对于溶剂设计和过程优化至关重要.
- 准确预测热力学特性,如无限稀释活动系数 (γ12∞) 是必不可少的.
研究的目的:
- 研究胆化物-尿素 (1:2) DES与各种有机溶剂的热力学特性和相互作用机制.
- 开发精确的机器学习模型,使用DES描述符和温度来预测γ12∞.
- 为设计高效和可持续的基于DES的分离过程提供理论框架.
主要方法:
- 反向气相色谱 (IGC) 实验用于确定46种有机溶剂的γ12∞.
- 机器学习模型的开发,包括极端梯度提升 (XGBoost),使用量化σ-profile分区描述符和温度.
- 将ML模型的性能与COSMO-SAC模型进行比较.
主要成果:
- 建立了溶解物-DES相互作用强度的等级,碳化合物相互作用最强,酒精相互作用最弱.
- XGBoost模型实现了高预测准确性 (测试组R2 = 0.9979,AARD < 20%),显著超过了COSMO-SAC (R2 = 0.8224).
- 特性重要性分析强调了弱键接受/捐赠区域 (S3,S4) 对 γ12∞ 预测的主要贡献,证实了"类似溶解类似"原则.
结论:
- 集成的IGC-ML框架提供了一种可靠和有效的方法来预测DES的热力学特性.
- 开发的ML模型仅从分子结构中实现了高精度的γ12∞预测,覆盖了广泛的应用领域.
- 这种方法促进了DES的合理设计,用于绿色溶剂应用和优化工业分离过程.
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