Explainable distributional structure of MXene compositions revealed by Shapley analysis

Abstract

Explainable artificial intelligence methods are increasingly essential for extracting physical insight from high-throughput computational materials data. We apply the Shapley behavioral transformation framework to two MXene datasets to characterize how individual compositions contribute to dataset statistics. By decomposing variance, skewness, kurtosis, and entropy using Shapley values, we create four complementary “behavioral spaces” that reveal compositional patterns invisible in raw feature representations. All behavioral spaces exhibit strong clustering tendency, with the skewness space providing the clearest chemical interpretation. Regional analysis in skewness space identifies six distinct behavioral zones with characteristic property distributions, including a fluorine-terminated MXenes cluster with elevated intercalation voltages and deep valence band positions. Critically, no MXenes appear as an outlier in all four behavioral spaces, demonstrating that each statistic captures genuinely complementary aspects of distributional behavior. The maximum cross-space agreement is 3/4 spaces, achieved by Sc2C in the electrochemistry dataset and by eight compositions in the electronic structure dataset. Among these consistent outliers, Y2CBr2 and Hf3C2(NH)2 exhibit conduction band positions favorable for photocatalytic hydrogen evolution. This model-agnostic framework complements predictive machine learning by answering not “what property will this composition have?” but “how does this composition contribute to the statistical structure of the dataset?” The latter question is directly relevant for identifying synthesis priorities and understanding structure–property relationships in complex materials spaces.