Discovering the ultralow thermal conductive A2B2O7-type high-entropy oxides through the hybrid knowledge-assisted data-driven machine learning

Abstract

Lattice engineering and distortion have been considered one kind of effective strategies for discovering advanced materials. The instinct chemical flexibility of high-entropy oxides (HEOs) motivates/accelerates to tailor the target properties through phase transformations and lattice distortion. Here, a hybrid knowledge-assisted data-driven machine learning (ML) strategy is utilized to discover the A2B2O7-type HEOs with low thermal conductivity (κ) through 17 rare-earth (RE = Sc, Y, La–Lu) solutes optimized A-site. A designing routine integrating the ML and high throughput first principles has been proposed to predict the key physical parameter (KPPs) correlated to the targeted κ of advanced HEOs. Among the smart-designed 6188 (5RE0.2)2Zr2O7 HEOs, the best candidates are addressed and validated by the principles of severe lattice distortion and local phase transformation, which effectively reduce κ by the strong multi-phonon scattering and weak interatomic interactions. Particularly, (Sc0.2Y0.2La0.2Ce0.2Pr0.2)2Zr2O7 with predicted κ below 1.59 Wm−1 K−1 is selected to be verified, which matches well with the experimental κ = 1.69 Wm−1 K−1 at 300 K and could be further decreased to 0.14 Wm−1 K−1 at 1473 K. Moreover, the coupling effects of lattice vibrations and charges on heat transfer are revealed by the cross-validations of various models, indicating that the weak bonds with low electronegativity and few bonding charge density and the lattice distortion (r*) identified by cation radius ratio (rA/rB) should be the KPPs to decrease κ efficiently. This work supports an intelligent designing strategy with limited atomic and electronic KPPs to accelerate the development of advanced multi-component HEOs with properties/performance at multi-scales.