Discovering the Ultralow Thermal Conductive High-Entropy Pyrochlore Oxides Through the Hybrid Knowledge-Assisted Data-Driven Machine Learning

Abstract

Lattice engineering and distortion have been considered one kind of effective strategies discovering advanced materials. The instinct chemical flexibility of high-entropy pyrochlore oxides (HEPOs) motivate/accelerate 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 HEPOs with low thermal conductivity (𝜅) through 17 rare-earth (RE = Sc, Y, La ~ Lu) solutes optimized A-site of A2B2O7. 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 HEPOs. Among the smart-designed 6188 (5RE0.2)2Zr2O7 HEPOs, 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 scatting 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 match well with the experimental κ = 1.69 Wm−1 K− 1 at 300K and could be further decreased to 0.14 Wm−1 K− 1 at 1473K. 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 HEPOs with properties/performance at multi-scales.