针对制冷剂分子特性，定制化构建了更精准的预测模型。
Predicting performance-related properties of refrigerant based on tailored small-molecule functional group contribution
- 基于制冷剂特性的功能基团，结合机器学习构建新预测模型。
- 在5个关键性能参数上实现高精度预测，误差显著低于传统方法。
- 适合制冷剂研发人员快速评估新分子性能，加速绿色制冷剂设计。
现有基团贡献（GC）方法多适用于大分子，应用于小分子制冷剂时误差较大。为此，本文聚焦制冷系统运行效率相关的五个关键属性——正常沸点、临界温度、临界压力、汽化焓和偏心因子，收集潜在制冷剂分子数据库。基于专为小分子制冷剂设计的功能基团，将GC方法与机器学习（ML）结合，构建新型预测模型。通过分析模型表现，揭示各基团对性能参数的贡献机制。同时，基于内部与外部扩展数据库，验证并展示了模型在新分子预测中的实际价值。
原文摘要 · Abstract (English)
As current group contribution (GC) methods are mostly proposed for a wide size-range of molecules, applying them to property prediction of small refrigerant molecules could lead to unacceptable errors. In this sense, for the design of novel refrigerants and refrigeration systems, tailoring GC-based models specifically fitted to refrigerant molecules is of great interest. In this work, databases of potential refrigerant molecules are first collected, focusing on five key properties related to the operational efficiency of refrigeration systems, namely normal boiling point, critical temperature, critical pressure, enthalpy of vaporization, and acentric factor. Based on tailored small-molecule groups, the GC method is combined with machine learning (ML) to model these performance-related properties. Following the development of GC-ML models, their performance is analyzed to highlight the potential group-to-property contributions. Additionally, the refrigerant property databases are extended internally and externally, based on which examples are presented to highlight the significance of the developed models.
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