A universal spatial group contribution method by <scp>3D</scp>‐structures for predicting the thermodynamic properties

Abstract 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 ( T c ), critical pressure ( P c ), critical volume ( V c ), boiling point ( T b ), and melting point ( T m ), and the squared correlation coefficients ( R 2 training ) 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 ( R 2 test : 0.8690–0.9988) and stability ( Q 2 : 0.8344–0.9981). Compared with the atomic adjacent group (AAG) model, which is a traditional group contribution method, the absolute mean relative errors (AARE training ) 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.