Theoretical design of multifunctional half-Heusler materials based on first-principles calculations

The family of ABX half-Heusler materials, also called filled-tetrahedral structures, is a special class of ternary compounds hosting a variety of material functionalities including thermoelectricity, topological insulation, magnetism, transparent conductivity and superconductivity. This class of compounds can be derived from two substitution approaches, i.e.,from Heusler materials by removing a portion of atoms forming ordered vacancies thus becoming half-Heusler, or from tetrahedral zinc blende compounds by adding atoms on the interstitial sites thus become filled-tetrahedral structures. In this paper, we briefly review the substitution approaches for material design along with their application in the design of half-Heusler compounds; then we will review the high-throughput search of new half-Heusler filled-tetrahedral structures and the study of their physical properties and functionalities.

Materials discovery and design using machine learning

The screening of novel materials with good performance and the modelling of quantitative structureactivity relationships(QSARs),among other issues,are hot topics in the field of materials science.Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations.Thus,it is imperative to develop a new method of accelerating the discovery and design process for novel materials.Recently,materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy.In this review,we first outline the typical mode of and basic procedures for applying machine learning in materials science,and we classify and compare the main algorithms.Then,the current research status is reviewed with regard to applications of machine learning in material property prediction,in new materials discovery and for other purposes.Finally,we discuss problems related to machine learning in materials science,propose possible solutions,and forecast potential directions of future research.By directly combining computational studies with experiments,we hope to provide insight into the parameters that affect the properties of materials,thereby enabling more efficient and target-oriented research on materials discovery and design.

Machine learning in materials genome initiative:A review

Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

Accelerating materials discovery using machine learning

The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which is time-consuming and laborious.Recently,machine learning(ML)methods have made great progress in the researches of materials science with the arrival of the big-data era,which gives a deep revolution in human society and advance science greatly.However,there exist few systematic generalization and summaries about the applications of ML methods in materials science.In this review,we first provide a brief account of the progress of researches on materials science with ML employed,the main ideas and basic procedures of this method are emphatically introduced.Then the algorithms of ML which were frequently used in the researches of materials science are classified and compared.Finally,the recent meaningful applications of ML in metal materials,battery materials,photovoltaic materials and metallic glass are reviewed.

Machine learning for advanced energy materials

The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse challenges,including low success probabilities,high time consumption,and high computational cost associated with the traditional methods of developing energy materials.Following this,new research concepts and technologies to promote the research and development of energy materials become necessary.The latest advancements in ar-tificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of en-ergy materials.Furthermore,the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment.In this article,the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented.A comprehensive introduction to the fundamentals of machine learning is also provided,including open-source databases,feature engineering,machine learning algorithms,and analysis of machine learning model.Afterwards,the latest progress in data-driven materials science and engineering,including alkaline ion battery materials,pho-tovoltaic materials,catalytic materials,and carbon dioxide capture materials,is discussed.Finally,relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.

Novel model of material removal rate on ultrasonic-assisted chemical mechanical polishing for sapphire

Ultrasonic-assisted chemical mechanical polishing(UA-CMP)can greatly improve the sapphire material removal and surface quality,but its polishing mechanism is still unclear.This paper proposed a novel model of material removal rate(MRR)to explore the mechanism of sapphire UA-CMP.It contains two modes,namely two-body wear and abrasive-impact.Furthermore,the atomic force microscopy(AFM)in-situ study,computational fluid dynamics(CFD)simulation,and polishing experiments were conducted to verify the model and reveal the polishing mechanism.In the AFM in-situ studies,the tip scratched the reaction layer on the sapphire surface.The pit with a 0.22 nm depth is the evidence of two-body wear.The CFD simulation showed that abrasives could be driven by the ultrasonic vibration to impact the sapphire surface at high frequencies.The maximum total velocity and the air volume fraction(AVF)in the central area increased from 0.26 to 0.55 m/s and 20%to 49%,respectively,with the rising amplitudes of 1–3μm.However,the maximum total velocity rose slightly from 0.33 to 0.42 m/s,and the AVF was nearly unchanged under 40–80 r/min.It indicated that the ultrasonic energy has great effects on the abrasive-impact mode.The UA-CMP experimental results exhibited that there was 63.7%improvement in MRR when the polishing velocities rose from 40 to 80 r/min.The roughness of the polished sapphire surface was R_(a)=0.07 nm.It identified that the higher speed achieved greater MRR mainly through the two-body wear mode.This study is beneficial to further understanding the UA-CMP mechanism and promoting the development of UA-CMP technology.

A machine-learning-enabled approach for bridging multiscale simulations of CNTs/PDMS composites

Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.

Mathematical Modeling of Carbon Content and Intercritical Annealing Temperature in DP Steels by Factorial Design Method

2k factorial design is employed to find the mathematical relation between the carbon content and intercritical annealing temperature （IAT） in order to predict the responses namely martensite volume fraction （MVF）, microhardness （H）, yield strength （YS）, ultimate tensile strength （UTS）, total elongation （TEL）, yield ratio （YR） and Charpy impact energy （CIE） in dual phase （DP） steels. Steels containing different carbon contents （0.085% C and 0.380% C） had been chosen for this purpose. The main advantages of factorial design are its easy implementation and the effective computation compared with the other optimization techniques, which were employed for predicting mentioned responses in the literature. To verify the proposed approach based on factorial design, experiments for verification were performed. The results of the verification experiments and the mathematical models are in accordance with each other and the literature.