Improved stability of superelasticity and elastocaloric effect in Ti-Ni alloys by suppressing Lüders-like deformation under tensile load

Functional stability of superelasticity is crucial for practical applications of shape memory alloys.It is degraded by a Lüders-like deformation with elevated local stress concentration under tensile load.By increasing the degree of solute supersaturation and applying appropriate thermomechanical treatments,a Ti-Ni alloy with nanocrystallinity and dispersed nanoprecipitates is obtained.In contrast to conventional Ti-Ni alloys,the superelasticity in the target alloy is accompanied by homogeneous deformation due to the sluggish stress-induced martensitic transformation.The alloy thus shows a fully recoverable strain of 6%under tensile stress over 1 GPa and a large adiabatic temperature decrease of 13.1 K under tensile strain of 4.5%at room temperature.Moreover,both superelasticity and elastocaloric effect exhibit negligible degradation in response to applied strain of 4%during cycling.We attribute the improved functional stability to low dislocation activity resulting from the suppression of localized deformation and the combined strengthening effect of nanocrystalline structure and nanoprecipitates.Thus,the design of such a microstructure enabling homogeneous deformation provides a recipe for stable superelasticity and elastocaloric effect.

Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics

In material science and engineering,obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult.In this work,we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system,i.e.,(Ba_(1−x−y)Ca_(x)Sr_(y))(Ti_(1−u−v−w)Zr_(u)Sn_(v)Hf_(w))O_(3).The deep neural network model uses physical features as inputs and directly outputs the full spectrum,in addition to yielding the octahedral factor,Matyonov–Batsanov electronegativity,ratio of valence electron to nuclear charge,and core electron distance(Schubert)as four key descriptors.Owing to the physically meaningful features,our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid solutions.And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature.Furthermore,the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information.The prediction is also experimentally validated by typical samples of(Ba_(0.85)Ca_(0.15))(Ti_(0.98–x)Zr_(x)Hf_(0.02))O_(3).This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.

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.

A property-oriented design strategy for high performance copper alloys via machine learning

Traditional strategies for designing new materials with targeted property including methods such as trial and error,and experiences of domain experts,are time and cost consuming.In the present study,we propose a machine learning design system involving three features of machine learning modeling,compositional design and property prediction,which can accelerate the discovery of new materials.We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600–950 MPa and an electrical conductivity of 50.0%international annealed copper standard.There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions.Our results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning.

Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization

Designing a material with multiple desired properties is a great challenge,especially in a complex material system.Here,we propose a material design strategy to simultaneously optimize multiple targeted properties of multi-component Co-base superalloys via machine learning.The microstructural stability,γ′solvus temperature,γ′volume fraction,density,processing window,freezing range,and oxidation resistance were simultaneously optimized.

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.

Ferroic glasses

Ferroic glasses(strain glass,relaxor and cluster spin glass)refer to frozen disordered states in ferroic systems;they are conjugate states to the long-range ordered ferroic states—the ferroic crystals.Ferroic glasses exhibit unusual properties that are absent in ferroic crystals,such as slim hysteresis and gradual property changes over a wide temperature range.In addition to ferroic glasses and ferroic crystals,a third ferroic state,a glass-ferroic(i.e.,a composite of ferroic glass and ferroic crystal),can be produced by the crystallization transition of ferroic glasses.It can have a superior property not possessed by its two components.These three classes of ferroic materials(ferroic crystal,ferroic glass and glass-ferroic)correspond to three transitions(ferroic phase transition,ferroic glass transition and crystallization transition of ferroic glass,respectively),as demonstrated in a generic temperature vs.defectconcentration phase diagram.Moreover,through constructing a phase field model,the microstructure evolution of three transitions and the phase diagram can be reproduced,which reveals the important role of point defects in the formation of ferroic glass and glass-ferroic.The phase diagram can be used to design various ferroic glasses and glass-ferroics that may exhibit unusual properties.