Discovering the ultralow thermal conductive A_(2)B_(2)O_(7)-type high-entropy oxides through the hybrid knowle dge-assiste d data-driven machine learning

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 A_(2)B_(2)O_(7)-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 targetedκof advanced HEOs.Among the smart-designed 6188(5RE_(0.2))_(2)Zr_(2)O_(7)HEOs,the best candidates are addressed and validated by the princi-ples of severe lattice distortion and local phase transformation,which effectively reduceκby the strong multi-phonon scattering and weak interatomic interactions.Particularly,(Sc_(0.2)Y_(0.2)La_(0.2)Ce_(0.2)Pr_(0.2))_(2)Zr_(2)O_(7)with predictedκbelow 1.59 Wm^(−1)K^(−1)is selected to be verified,which matches well with the ex-perimentalκ=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 bond-ing charge density and the lattice distortion(r∗)identified by cation radius ratio(r A/r B)should be the KPPs to decreaseκefficiently.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.

Creep anisotropy behavior,deformation mechanism,and its efficient suppression method in Inconel 625 superalloy

Mill products,such as sheets,usually show obvious anisotropy in their mechanical properties,which greatly affects both their applications and workability.In this study,the orientation-dependent tensile and creep behaviors of Inconel 625 alloy sheets with weak local textures were systematically investigated at 650°C.The results showed that Inconel 625 superalloy exhibits nearly isotropic tensile properties;how-ever,obvious creep anisotropy appears when loading along different directions.Creep life in the rolling direction(150±5 h)was approximately 4.5 times longer than in the transverse direction(33±1 h).Se-vere creep anisotropy was found to be determined by two aspects:(i)the change in deformation mecha-nisms along the rolling direction(a combination of boundary sliding,dislocation slipping,and twinning)and the transverse direction(dislocation slipping).Stronger impingement of slip bands on grain bound-aries accelerated intergranular crack initiation and propagation during loading along the transverse direc-tion,which resulted in a short creep life.(ii)The differences in strain compatibility at grain boundaries(i.e.,creep loading along the transverse direction resulted in the initial grains forming strong Brass,Cu,and S textures).Creep cracks preferentially nucleated at the junctions between Cu/Brass texture grains,as these interfaces exhibit the worst strain compatibility(Luster-Morris parameter m≤0.22).Furthermore,pre-stress aging(PSA)treatment is proposed as an efficient method to suppress creep anisotropy.The discrepancy rate of creep life was found to be reduced by nearly 50%after PSA treatment.

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical.We review how methods from the information sciences enable us to accelerate the search and discovery of new materials.In particular,active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations.The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data.We discuss several utility functions and demonstrate their use in materials science applications,impacting both experimental and computational research.We summarize by indicating generalizations to multiple properties and multifidelity data,and identify challenges,future directions and opportunities in the emerging field of materials informatics.

Temperature-field history dependence of the elastocaloric effect for a strain glass alloy

The singular change of the order parameter at the first order martensitic transformation(MT)temperature restricts the caloric response to a narrow temperature range.Here the MT is tuned into a sluggish strain glass transition by defect doping and a large elastocaloric effect appears in a wide temperature range.Moreover,an inverse elastocaloric effect is observed in the strain glass alloy with history of zerofield cooling and is attributed to the slow dynamics of the nanodomains in response to the external stress.This study offers a design recipe to expand the temperature range for good elastocaloric effect.