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 K of advanced HEOs.Among the smart-designed 6188(5RE0.2)2Zr2O7 HEOs,the best candidates are addressed and validated by the princi-ples of severe lattice distortion and local phase transformation,which effectively reduce K 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 ex-perimental κ=1.69 Wm1 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 bond-ing charge density and the lattice distortion(r)identified by cation radius ratio(rA/rB)should be the KPPs to decrease K efficiently.This work supports an intelligent designing strategy with limited atomic and electronic KPPs to accelerate the development of advanced multi-component HEOs with proper-ties/performance at multi-scales.
基金项目
National defense basic scientific research(2022-JCKY-JJ-1086)
National defense basic scientific research(211-CXCY-N103-03-04-00)
NETL
NSTL
万方数据