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.

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.

Design,preparation,application of advanced array structured materials and their action mechanism analyses for high performance lithium-sulfur batteries

Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.

Exploration on the Integration of Curriculum Ideological and Political Concept into the Teaching Design of"Culinary Raw Materials":A Case Study of Livestock and Poultry Raw Materials

Comprehensively promoting curriculum ideological and political education is a strategic measure to implement the fundamental task of establishing moral integrity and educating people.Culinary raw materials is the core curriculum of culinary majors,which is rich in ideological and political elements.In this study,taking livestock and poultry raw materials as an example,the teaching design of integrating curriculum ideological and political education into culinary raw materials were investigated by sorting out its teaching contents and excavating its ideological and political elements.Taking livestock and poultry raw materials in Teochew Cuisine Raw Materials as an example,the teaching design of curriculum ideological and political education was conducted,aiming to provide some reference and guidance for curriculum ideological and political education of the curriculum.

Application of Energy-Saving Materials in Architectural Design

The conventional process of building construction is associated with issues such as the waste of construction materials and environmental pollution.Sustainable development highlights the importance of energy conservation and eco-friendly practices.It is essential to use energy-efficient and green materials in building designs to ensure the healthy growth of construction companies.This article discusses the advantages and principles of incorporating energy-saving materials in architectural design.It examines the strategies and critical control points for using energy-saving materials in architectural design,offering guidance for the sustainable development of the construction industry.

A Comparative Study on Several Anti-Corrosion Materials for Power FGD System

In the study organic-inorganic hybrid composite, epoxy modified silicone coating and vinyl ester flake mastic were comparatively used as several anti-corrosion materials that provided protection for flue gas desulfu-rization (FGD). The relationship between curing conversion rate of hybrid polymer and temperature was investigated by differential scanning calorimeter (DSC). The adhesion strength, coefficient of thermal expansion and flame retardant properties of three anti-corrosion materials were measured and analyzed. A corrosion test in 8% H2SO4 and 5% HCl at temperature cycle of 40°C~ 160°C was applied to study corrosion resistance of several anti-corrosion materials. Gravimetric measurement and morphological observation of three materials before and after corrosion test were comparatively analyzed in the paper. The small weight change and good morphological structure of hybrid composite during corrosion test demonstrate that hybrid composite has better anti-corrosion properties than epoxy modified silicone coating and vinyl ester flake mastic.

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.

Architectural Design and Additive Manufacturing of Mechanical Metamaterials:A Review

Mechanical metamaterials can be defined as a class of architected materials that exhibit unprecedented mechanical properties derived from designed artificial architectures rather than their constituent materials.While macroscale and simple layouts can be realized by conventional top-down manufacturing approaches,many of the sophisticated designs at various length scales remain elusive,due to the lack of adequate manufacturing methods.Recent progress in additive manufacturing(AM)has led to the realization of a myriad of novel metamaterial concepts.AM methods capable of fabricating microscale architectures with high resolution,arbitrary complexity,and high feature fidelity have enabled the rapid development of architected meta materials and drastically reduced the design-computation and experimental-validation cycle.This paper first provides a detailed review of various topologies based on the desired mechanical properties,including stiff,strong,and auxetic(negative Poisson’s ratio)metamaterials,followed by a discussion of the AM technologies capable of fabricating these metamaterials.Finally,we discuss current challenges and recommend future directions for AM and mechanical metamaterials.

An Electronic Approach to Materials Design

A molecular orbital approach to materials design has recently made great progress. This approach is based onthe electronic structure calculations by the DV-Xα cluster method. In this paper recent progress in this approachis reviewed. In particular, it is stressed that New PHACOMP approach is useful for predicting the formation oftopologically close-packed (TCP) phases (e.g., σ phase andμ phase ) in nickel based superalloys. Compared to thecurrent PHACOMP, New PHACOMP provides a better tool for designing those alloys which are free from such TCPprecipitates at service temperatures. In addition, the d-electrons concept is shown for alloy design and development.

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.

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.

Discovery and design of lithium battery materials via high-throughput modeling

This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.

High-throughput computational screening and design of nanoporous materials for methane storage and carbon dioxide capture

The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.

Recycling of minute metal scraps by semisolid processing:Manufacturing of design materials

As a new attempt to recycle minute metal scraps, the possibility of manufacturing design materials by semisolid extrusion processing was shown.A design material with an intended shape, such as a character or petal shape, was manufactured using minute metal scraps.Similarly, a design material with an intended color pattern for each metal, such as red copper in a white aluminum matrix, resembling grainlike wood, was manufactured by mixing two or more types of minute metal scrap.In addition, secondary design materials, which have engraved patterns on the surface of the target metal made by an electric discharge machine using the above primary design material as an electrode, were manufactured.

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.

Polypyrrole-coated triple-layer yolk-shell Fe_(2)O_(3)anode materials with their superior overall performance in lithium-ion batteries

Iron oxide(Fe_(2)O_(3))emerges as a highly attractive anode candidate among rapidly expanding energy storage market.Nonethe-less,its considerable volume changes during cycling as an electrode material result in a vast reduced battery cycle life.In this work,an ap-proach is pioneered for preparing high-performance Fe_(2)O_(3)anode materials,by innovatively synthesizing a triple-layer yolk-shell Fe_(2)O_(3)uniformly coated with a conductive polypyrrole(Ppy)layer(Fe_(2)O_(3)@Ppy-TLY).The uniform polypyrrole coating introduces more reac-tion sites and adsorption sites,and maintains structure stability through charge-discharge process.In the uses as lithium-ion battery elec-trodes,Fe_(2)O_(3)@Ppy-TLY demonstrates high reversible specific capacity(maintaining a discharge capacity of 1375.11 mAh·g^(−1)after 500 cycles at 1 C),exceptional cycling stability(retaining the steady charge-discharge performance at 544.33 mAh·g^(−1)after 6000 ultrafast charge-discharge cycles at a 10 C current density),and outstanding high current charge-discharge performance(retaining a reversible ca-pacity of 156.75 mAh·g^(−1)after 10000 cycles at 15 C),thereby exhibiting superior lithium storage performance.This work introduces in-novative advancements for Fe_(2)O_(3)anode design,aiming to enhance its performance in energy storage fields.

Optimization design and residual thermal stress analysis of PDC functionally graded materials

The distribution of thermal stresses in functionally graded polycrystalline diamond compact (PDC) and in single coating of PDC are analyzed respectively by thermo-mechanical finite element analysis (FEA). It is shown that they each have a remarkable stress concentration at the edge of the interfaces. The diamond coatings usually suffer premature failure because of spallation, distortion or defects such as cracks near the interface due to these excessive residual stresses. Results showed that the axial tensile stress in FGM coating is reduced from 840 MPa to 229 MPa compared with single coating, and that the shear stress is reduced from 671 MPa to 471 MPa. Therefore, the single coating is more prone to spallation and cracking than the FGM coating. The effects of the volume compositional distribution factor (n) and the number of the graded layers (L) on the thermal stresses in FGM coating are also discussed respectively. Modelling results showed that the optimum value of the compositional distribution factor is 1.2, and that the best number of the graded layers is 6.

Design of Co-sedimentation Experiments Used to Fabricate Functionally Graded Materials with a Continuous Change of Composition

In the process of particle settling in a dilute,a density graded distribution of the liquid below the suspension needs to be designed according to the gravity of the suspension prior to sedimentation.In the present paper a compositionally graded W-Mo composite was formed via the settling of the W and Mo particles,with a density gradient distributed in the initial clear liquid along the settling direction.

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.