Feature purify: An examination of spurious correlations in high-entropy alloys

Patterns in datasets, while key to successful predictions, can also be misleading. In this study, a feature filtering workflow based on interpretable dimensionality reduction techniques was developed to diagnose the spurious correlations in high-entropy alloy dataset with phase structure and hardness labels. It is found that the presumed linear relationship between valence electron concentration (VEC) and hardness in high-entropy alloys is spurious, which highly influenced by the constitute elements of alloy system and determined by properties related to atomic radius. In addition, electron work function (w) and cohesive energy (Ec) have similar relationships with hardness, which indicated that these electronic features should be excluded for hardness prediction. Hence, this process can serve as a preliminary step in feature selection to mitigate the influence of non-causal features on prediction.