Discovering superhard high‐entropy diboride ceramics via a hybrid data‐driven and knowledge‐enabled model

Abstract

AbstractMaterials descriptors with multivariate, multiphase, and multiscale of a complex system have been treated as the remarkable materials genome, addressing the composition–processing–structure–property–performance (CPSPP) relationships during the development of advanced materials. With the aid of high‐performance computations, big data, and artificial intelligence technologies, it is still a challenge to derive an explainable machine learning (ML) model to reveal the underlying CPSPP relationship, especially, under the extreme conditions. This work supports a smart strategy to derive an explainable model of composition–property–performance relationships via a hybrid data‐driven and knowledge‐enabled model, and designing superhard high‐entropy diboride ceramics (HEBs) with a cost‐effective approach. Five dominate features and optimal model were screened out from 149 features and nine algorithms by ML and validated in first‐principles calculations. From Shapley additive explanations (SHAP) and electronic bottom layer, the predicted hardness increases with the improved mean electronegativity and electron work function (EWF) and decreases with growing average d valence electrons of composition. The 14 undeveloped potential superhard HEBs candidates via ML are further validated by first‐principles calculations. Moreover, this EWF‐ML model not only has better capability to distinguish the differences of solutes in same group of periodic table but is also a more effective method for material design than that of valence electron concentration.