Classical group contribution method, as one of the main methods for estimating thermodynamic properties, is developed with the number of groups, ignoring the influence of group characters. In this work, the spatial group contribution (SGC) method combining Euclidean distance and quantum properties is proposed, which uses the spatial group factor (SGF) and the spatial position factor (SPF) to reflect the spatial differences of the groups, thereby improving the limitations of the previous methods that only rely on topological structures. Five SGC models are established, including critical temperature (Tc), critical pressure (Pc), critical volume (Vc), boiling point (Tb), and melting point (Tm), and the squared correlation coefficients (R2training) of 0.9935, 0.9925, 0.9988, 0.9828, and 0.8690 are obtained, respectively. After a series of rigorous validation procedures (external validation and internal validation), all models present excellent predictability (R2test: 0.8690–0.9988) and stability (Q2: 0.8344–0.9981). Compared with the atomic adjacent group (AAG) model, which is a traditional group contribution method, the absolute mean relative errors (AAREtraining) of five models are reduced by 24.67%–69.26%. The position factor and spatial group factor crucially improve the models based on the number of groups. The spatiality‐based SGC method is of great significance for the prediction of thermodynamic properties and has the potential to be extended to more thermodynamic properties such as phase transition properties of enthalpy and entropy as well as saturated vapor pressure.

中文翻译：

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一种基于 3D 结构的通用空间群贡献法，用于预测热力学性质


经典的群贡献法作为估计热力学性质的主要方法之一，是以群数发展起来的，忽略了群性状的影响。在这项工作中，提出了结合欧几里得距离和量子性质的空间群贡献（SGC）方法，该方法利用空间群因子（SGF）和空间位置因子（SPF）来反映群的空间差异，从而改善了以前仅依赖拓扑结构的方法的局限性。建立了临界温度（Tc）、临界压力（Pc）、临界体积（Vc）、沸点（Tb）和熔点（Tm）5个SGC模型，得到了0.9935、0.9925、0.9988、0.9828和0.8690的平方相关系数（R2training）。经过一系列严格的验证程序（外部验证和内部验证），所有模型都表现出优异的可预测性 （R2test： 0.8690–0.9988） 和稳定性 （Q2： 0.8344–0.9981）。与传统的群贡献法原子相邻群 （AAG） 模型相比，5 种模型的绝对平均相对误差 （AAREtraining） 降低了 24.67%–69.26%。位置因子和空间群因子对基于组数的模型进行关键改进。基于空间性的 SGC 方法对于热力学性质的预测具有重要意义，并有可能扩展到更多的热力学性质，例如焓和熵的相变性质以及饱和蒸气压。