Neighborhood Path Complex for the Quantitative Analysis of the Structure and Stability of Carboranes

Abstract

Thanks to the tremendous progress in data, computing power and algorithms, AI-based material mining and design have gained much attention. However, building high-performance AI models requires efficient material structure representation. In this work, we propose a structural characterization method based on the neighborhood path complex for the first time. Specifically, we use persistent neighborhood path homology to obtain the structural features by introducing a filtration. This approach preserves more elemental information, as well as the corresponding physicochemical information, through the directed edges of the neighborhood digraph. To validate our model, we perform cross-validation with the carborane structures. The Pearson coefficient for stability prediction is as high as 0.903, which is a 15.5% improvement compared to the traditional persistent homology method. In addition, we constructed a prediction model based on the neighborhood path complex, and the Pearson coefficients for the prediction of carboranes’ HOMO, LUMO, and HOMO–LUMO gaps were 0.915, 0.946, and 0.941, respectively. The results show that our proposed method can effectively extract structural information and achieve accurate material property prediction.