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

Materials 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.

Automated summary

Materials descriptors with multivariate, multiphase, and multiscale characteristics have been used to study the composition-processing-structure-property-performance (CPSPP) relationships in advanced materials. This study presents a hybrid data-driven and knowledge-enabled model to explain the composition-property-performance relationships, and applies it to design cost-effective superhard high-entropy diboride ceramics (HEBs). Machine learning and first-principles calculations are used to identify dominant features and validate the model. The results show that hardness is influenced by mean electronegativity, electron work function, and average d valence electrons of composition.