ELT Materials Design of a Speaking Unit based on Needs Analysis

In this article,the authors design a speaking unit based on needs analysis following Hutchinson and Waters'(1987) model.First,the rationale in designing this unit is introduced,which involves the teaching approach adopted and relevant theories in organizing the materials.Then,the teaching plan of this speaking unit is provided and some activities are designed to create an authentic and optimal situation for students to practice their speaking skill.

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design

Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.

Machine learning of materials design and state prediction for lithium ion batteries

With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.

Machine learning in materials design:Algorithm and application

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.

Advances and challenges in DFT-based energy materials design

The growing worldwide energy needs call for developing novel materials for energy applications.Ab initio density functional theory(DFT)calculations allow the understanding and prediction of material properties at the atomic scale,thus,play an important role in energy materials design.Due to the fast progress of computer power and development of calculation methodologies,DFT-based calculations have greatly improved their predictive power,and are now leading to a paradigm shift towards theory-driven materials design.The aim of this perspective is to introduce the advances in DFT calculations which accelerate energy materials design.We first present state-of-the-art DFT methods for accurate simulation of various key properties of energy materials.Then we show examples of how these advances lead to the discovery of new energy materials for photovoltaic,photocatalytic,thermoelectric,and battery applications.The challenges and future research directions in computational design of energy materials are highlighted at the end.

From atomistic modeling to materials design:computation-driven material development in lithium-ion batteries

As an advanced energy storage system,lithium-ion batteries play an essential role in modern technologies.Despite their ubiquitous success,there is a great demand for continuous improvements of the battery performance,including higher energy density,lower safety risk,longer cycling life,and lower cost.Such performance improvement requires the design and development of novel electrode and electrolyte materials that exhibit desirable properties and satisfy strict requirements.Atomistic modeling can provide a unique perspective to fundamentally understand and rationally design battery materials.In this paper,we review a few recent successful examples of computation-driven discovery and design in electrode and electrolyte materials.Particularly,we highlight how atomistic modeling can reveal the underlying mechanisms,predict the important properties,and guide the design and engineering of electrode and electrolyte materials.We have a conclusion with a discussion of the unique capability of atomistic modeling in battery material development and provide a perspective on future challenges and directions for computation-driven battery material developments.

Alloy as advanced catalysts for electrocatalysis:From materials design to applications

With the deep integration of electrochemical research with energy,environment,catalysis,and other fields,more and more new electrochemical catalytic reactions have entered our research field.Alloy catalysts have recently emerged as a new type of nanomaterial due to the rapid development of kinetic controlled synthesis technology.These materials offer several advantages over monometallic catalysts,including larger element combinations,complex geometries,bifunctional sites,and reduced use of precious metals.This paper provides a review of alloy electrocatalysts that are designed and prepared specifically for electrocatalytic applications.The use of alloy materials in electrocatalyst design is also discussed,highlighting their widespread application in this field.First,various synthesis methods and synthesis mechanisms are systematically summarized.Following that,by correlating the properties of materials with the structure,relevant strategies toward advanced alloy electrocatalysts including composition regulation,size,morphology,surface engineering,defect engineering,interface engineering and strain engineering are classified.In addition,the important electrocatalytic applications and mechanisms of alloy electrocatalysts are described and summarized.Finally,the current challenges and prospects regarding the development of alloy nanomaterials are proposed.This review serves as a springboard from a fundamental understanding of alloy structural dynamics to design and various applications of electrocatalysts,particularly in energy and environmental sustainability.

An approach for full space inverse materials design by combining universal machine learning potential,universal property model,and optimization algorithm

We present a full space inverse materials design(FSIMD)approach that fully automates the materials design for target physical properties without the need to provide the atomic composition,chemical stoichiometry,and crystal structure in advance.Here,we used density functional theory reference data to train a universal machine learning potential(UPot)and transfer learning to train a universal bulk modulus model(UBmod).Both UPot and UBmod were able to cover materials systems composed of any element among 42 elements.Interfaced with optimization algorithm and enhanced sampling,the FSIMD approach is applied to find the materials with the largest cohesive energy and the largest bulk modulus,respectively.NaCl-type ZrC was found to be the material with the largest cohesive energy.For bulk modulus,diamond was identified to have the largest value.The FSIMD approach is also applied to design materials with other multi-objective properties with accuracy limited principally by the amount,reliability,and diversity of the training data.The FSIMD approach provides a new way for inverse materials design with other functional properties for practical applications.

Optimizing design of lattice materials based on finite element simulation

The optimized design of simple cross-truss and column lattice structures was carried out by the SolidWorks simulation module.The effective density of the structure was calculated according to the weight reduction requirements proposed by the project.Then,the vari-ation curve between the maximum bearing stress of the unit structure and the structural variables was obtained by simulation.Meanwhile,the mathematical equation between the maximum bearing stress and the structural variables could be obtained through MATLAB fitting.The results indicated that with the decrease in the number of cells,the compressive strength of the prepared column lattice increased(400 to 4 cells,compressive strength 29 MPa to 160 MPa).However,the yield strength increased with the number of cells.The compression strength of the simple cross-truss lattice samples indicated an increase trend with the decrease of the pillar size(an increase of the number of units),reaching 91 MPa(pillar diameter 0.52 mm,number of units 25).While the yield strength increased with the increasing of the number of units.In addition,the additive manufacturing processes of simple cubic lattice and simple cross-pillar lattice were investigated using selective laser melting.The compression performance obtained from the experiment is compared with the simulation results,which are in good agreement.The results of this paper can provide an important reference for optimizing design of lattice materials.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Application of machine learning in perovskite materials and devices:A review

Metal-halide hybrid perovskite materials are excellent candidates for solar cells and photoelectric devices.In recent years,machine learning(ML)techniques have developed rapidly in many fields and provided ideas for material discovery and design.ML can be applied to discover new materials quickly and effectively,with significant savings in resources and time compared with traditional experiments and density functional theory(DFT)calculations.In this review,we present the application of ML in per-ovskites and briefly review the recent works in the field of ML-assisted perovskite design.Firstly,the advantages of perovskites in solar cells and the merits of ML applied to perovskites are discussed.Secondly,the workflow of ML in perovskite design and some basic ML algorithms are introduced.Thirdly,the applications of ML in predicting various properties of perovskite materials and devices are reviewed.Finally,we propose some prospects for the future development of this field.The rapid devel-opment of ML technology will largely promote the process of materials science,and ML will become an increasingly popular method for predicting the target properties of materials and devices.

Designing high-efficiency light-to-thermal conversion materials for solar desalination and photothermal catalysis

Light-to-thermal conversion materials(LTCMs)have been of great interest to researchers due to their impressive energy conversion capacity and wide range of applications in biomedical,desalination,and synergistic catalysis.Given the limited advances in existing materials(metals,semiconductors,π-conjugates),researchers generally adopt the method of constructing complex systems and hybrid structures to optimize performance and achieve multifunctional integration.However,the development of LTCMs is still in its infancy as the physical mechanism of light-to-thermal conversion is unclear.In this review,we proposed design strategies for efficient LTCMs by analyzing the physical process of light-tothermal conversion.First,we analyze the nature of light absorption and heat generation to reveal the physical processes of light-to-thermal conversion.Then,we explain the light-to-thermal conversion mechanisms of metallic,semiconducting andπ-conjugated LCTMs,and propose new material design strategies and performance improvement methods.Finally,we summarize the challenges and prospects of LTCMs in emerging applications such as solar water evaporation and photothermal catalysis.

Advances in the structure design of substrate materials for zinc anode of aqueous zinc ion batteries

Aqueous zinc ion batteries(AZIBs) demonstrate tremendous competitiveness and application prospects because of their abundant resources,low cost, high safety, and environmental friendliness. Although the advanced electrochemical energy storage systems based on zinc ion batteries have been greatly developed, many severe problems associated with Zn anode impede its practical application, such as the dendrite formation,hydrogen evolution, corrosion and passivation phenomenon. To address these drawbacks, electrolytes, separators, zinc alloys, interfacial modification and structural design of Zn anode have been employed at present by scientists. Among them, the structural design for zinc anode is relatively mature, which is generally believed to enhance the electroactive surface area of zinc anode, reduce local current density, and promote the uniform distribution of zinc ions on the surface of anode. In order to explore new research directions, it is crucial to systematically summarize the structural design of anode materials. Herein, this review focuses on the challenges in Zn anode, modification strategies and the three-dimensional(3D) structure design of substrate materials for Zn anode including carbon substrate materials, metal substrate materials and other substrate materials. Finally, future directions and perspectives about the Zn anode are presented for developing high-performance AZIBs.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

The design and engineering strategies of metal tellurides for advanced metal-ion batteries

Owning various crystal structures and high theoretical capacity,metal tellurides are emerging as promising electrode materials for high-performance metal-ion batteries(MBs).Since metal telluride-based MBs are quite new,fundamental issues raise regarding the energy storage mechanism and other aspects affecting electrochemical performance.Severe volume expansion,low intrinsic conductivity and slow ion diffusion kinetics jeopardize the performance of metal tellurides,so that rational design and engineering are crucial to circumvent these disadvantages.Herein,this review provides an in-depth discussion of recent investigations and progresses of metal tellurides,beginning with a critical discussion on the energy storage mechanisms of metal tellurides in various MBs.In the following,recent design and engineering strategies of metal tellurides,including morphology engineering,compositing,defect engineering and heterostructure construction,for high-performance MBs are summarized.The primary focus is to present a comprehensive understanding of the structural evolution based on the mechanism and corresponding effects of dimension control,composition,electron configuration and structural complexity on the electrochemical performance.In closing,outlooks and prospects for future development of metal tellurides are proposed.This work also highlights the promising directions of design and engineering strategies of metal tellurides with high performance and low cost.

Using support vector machine for materials design

Materials design is the most important and fundamental work on the background of materials genome initiative for global competitiveness proposed by the National Science and Technology Council of America. As far as the methodologies of materials design, besides the thermodynamic and kinetic methods combing databases, both deductive approaches so-called the first principle methods and inductive approaches based on data mining methods are gaining great progress because of their suc- cessful applications in materials design. In this paper, support vector machine （SVM）, including support vector classification （SVC） and support vector regression （SVR） based on the statistical learning theory （SLT） proposed by Vapnik, is introduced as a relatively new data mining method to meet the different tasks of materials design in our lab. The advantage of using SVM for materials design is discussed based on the applications in the formability of perovskite or BaNiO3 structure, the prediction of energy gaps of binary compounds, the prediction of sintered cold modulus of sialon-corundum castable, the optimization of electric resistances of VPTC semiconductors and the thickness control of In203 semiconductor film preparation. The results presented indicate that SVM is an effective modeling tool for the small sizes of sample sets with great potential applications in materials design.

Physics towards next generation Li secondary batteries materials:A short review from computational materials design perspective

The physics that associated with the performance of lithium secondary batteries(LSB)are reviewed.The key physical problems in LSB include the electronic conduction mechanism,kinetics and thermodynamics of lithium ion migration,electrode/electrolyte surface/interface,structural(phase)and thermodynamics stability of the electrode materials,physics of intercalation and deintercalation.The relationship between the physical/chemical nature of the LSB materials and the batteries performance is summarized and discussed.A general thread of computational materials design for LSB materials is emphasized concerning all the discussed physics problems.In order to fasten the progress of the new materials discovery and design for the next generation LSB,the Materials Genome Initiative(MGI)for LSB materials is a promising strategy and the related requirements are highlighted.

Recent progress in all-inorganic metal halide nanostructured perovskites:Materials design,optical properties,and application

Low-dimensional all-inorganic metal halide perovskite(AIMHP)materials,as a new class of nanomaterials,hold great promise for various optoelectronic devices.In the past few years,tremendous progress has been achieved in the development of efficient and stable AIMHP nanomaterials for optical property studies and related applications.Here,we offer a critical overview on the unique merits and the state-of-the-art design of AIMHP using different composition strategies.Then,the effects of material compositions,dimensionality,morphologies and structures on optical properties are summarized.We also comprehensively present recent advances in the development AIMHP nanomaterials for practical applications including solar cells,light-emitting diodes,lasers and photodetectors.Lastly,the critical challenges and future opportunities in this emerging field are highlighted.

Design strategies of performance-enhanced Se cathodes for Li-Se batteries and beyond

Lithium-selenium(Li-Se)batteries are deemed as an emerging high energy density electrochemical energy storage system owing to their high specific capacity and volume capacity.Prior to their practicality,a series of critical challenges from the Se cathode side need to be overcome including low reactivity of bulk Se,shuttle effect of intermediates,sluggish redox kinetics of polyselenides,and volume change etc.In this review,recent progress on design strategies of functional Se cathodes are comprehensively summarized and discussed.Following the significance and key challenges,various efficient functionalized strategies for Se cathodes are presented,encompassing covalent bonding,nanostructure construction,heteroatom doping,component hybridization,and solid solution formation.Specially,the universality of these functional strategies are successfully extended into Na-Se batteries,K-Se batteries,and Mg-Se batteries.At last,a brief summary is made and some perspectives are offered with the goal of guiding future research advances and further exploration of these strategies.

Development of a computational tool for materials design

An integrated modeling tool coupling thermo- dynamic calculation and kinetic simulation of multicom- ponent alloys is developed under the framework of integrated computational materials engineering. On the basis of PandatTM software for multicomponent phase diagram calculation, the new tool is designed in an inte- grated workspace and is targeted to understand the com- position-processing-structure-property relationships of multicomponent systems. In particular, the phase diagram calculation module is used to understand the phase stability under the given conditions. The calculated phase equilib- rium information, such as phase composition and chemical driving force, provides input for the kinetic simulation. In this paper, the design of the modeling tool will be pre- sented and the calculation examples from the different modules will also be demonstrated.