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.

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.

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.

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.

Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

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.

Databases of 2D material-substrate interfaces and 2D charged building blocks

Discovery of materials using“bottom-up”or“top-down”approach is of great interest in materials science.Layered materials consisting of two-dimensional(2D)building blocks provide a good platform to explore new materials in this respect.In van der Waals(vdW)layered materials,these building blocks are charge neutral and can be isolated from their bulk phase(top-down),but usually grow on substrate.In ionic layered materials,they are charged and usually cannot exist independently but can serve as motifs to construct new materials(bottom-up).In this paper,we introduce our recently constructed databases for 2D material-substrate interface(2DMSI),and 2D charged building blocks.For 2DMSI database,we systematically build a workflow to predict appropriate substrates and their geometries at substrates,and construct the 2DMSI database.For the 2D charged building block database,1208 entries from bulk material database are identified.Information of crystal structure,valence state,source,dimension and so on is provided for each entry with a json format.We also show its application in designing and searching for new functional layered materials.The 2DMSI database,building block database,and designed layered materials are available in Science Data Bank at https://doi.org/10.57760/sciencedb.j00113.00188.

Integration of Environmental and Social Values in Cultural Spaces:Sustainable and Inclusive Wayfinding Materials for Museums

In the context of use of large museum centers,numerous national and international methodological experiments show that the wayfinding project must consider the needs of both habitual users(user-centered design)and local communities(design for communities)and the importance of environmental protection(eco-design)as a priority interest of the community.This“double target”,“user-centered”and“environment-centered”can be applied during the selection process of materials to be used in the project.With respect to these possibilities,this contribution intends to present the results of research focused on material characterization of the reception and distribution spaces of large museum centers.This characterization is based on use of sensory materials and aims to evaluate their impact on the usability and sustainability of wayfinding systems.The paper directed towards a proposal for organization of integrated information on new generation so-called smart materials;within the design of a wayfinding system,these can balance the aesthetic-perceptual and performance and environmental impact,in order to allow designers to make informed decisions oriented towards inclusion and sustainability.The study was addressed by conducting two phases of systematic literature and library review of materials.The investigations conducted led to achievement of a first research result which consists in the identification of a“standard sheet”for the mapping and cataloging of the materials used for wayfinding.The“standard sheet”allows organizing the information on smart,sensorial,and eco-friendly materials,balancing the aesthetic-perceptive component with the performance on the environmental impact along the entire life cycle in a circular perspective.This tool could guide designers towards an environmental communication project oriented towards sustainability and is effective for usability and wayfinding.

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.

A strategy for lightweight designing of a railway vehicle car body including composite material and dynamic structural optimization

Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative materials can be combined in order to achieve this goal.In this framework,we propose the redesign and optimization process of the car body roof for a light rail vehicle,introducing a sandwich structure.Bonded joint was used as a fastening system.The project was carried out on a single car of a modern tram platform.This preliminary numerical work was developed in two main steps:redesign of the car body structure and optimization of the innovated system.Objective of the process was the mass reduction of the whole metallic structure,while the constraint condition was imposed on the first frequency of vibration of the system.The effect of introducing a sandwich panel within the roof assembly was evaluated,focusing on the mechanical and dynamic performances of the whole car body.A mass saving of 63%on the optimized components was achieved,corresponding to a 7.6%if compared to the complete car body shell.In addition,a positive increasing of 17.7%on the first frequency of vibration was observed.Encouraging results have been achieved in terms of weight reduction and mechanical behaviour of the innovated car body.

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.

The action mechanisms and structures designs of F-containing functional materials for high performance oxygen electrocatalysis

Non-renewable fossil fuels have led to serious problems such as global warming,environmental pollution,etc.Oxygen electrocatalysis including oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)plays a central role in clean energy conversion,enabling a number of sustainable processes for future air battery technologies.Fluorine,as the most electronegative element(4.0)not only can induce more efficient regulation for the electronic structure,but also can bring more abundant defects and other novel effects in materials selection and preparation for favorable catalysis with respect to the other nonmetal elements.However,an individual and comprehensive overview of fluorine-containing functional materials for oxygen electrocatalysis field is still blank.Therefore,it is very meaningful to review the recent progresses of fluorine-containing oxygen electrocatalysts.In this review,we first systematically summarize the controllable preparation methods and their possible development directions based on fluorine-containing materials from four preparation methods.Due to the strong electron-withdrawing properties of fluorine,its control of the electronic structure can effectively enhance the oxygen electrocatalytic activity of the materials.In addition,the catalytic enhancement effect of fluorine on carbonbased materials also includes the prevent oxidation and the layer peeling,and realizes the precise atomic control.And the catalytic improvement mechanism of fluorine containing metal-based compounds also includes the hydration of metal site,the crystal transformation,and the oxygen vacancy induction.Then,based on their various dimensions(0D–3D),we also have summarized the advantages of different morphologies on oxygen electrocatalytic performances.Finally,the prospects and possible future researching direction of F-containing oxygen electrocatalysts are presented(e.g.,novel pathways,advanced methods for measurement and simulation,field assistance and multi-functions).The review is considered valuable and helpful in exploring the novel designs and mechanism analyses of advanced fluorine-containing electrocatalysts.

论UI扁平化设计体验感的实现——以Material Design为例

互联网行业的飞速发展推动了UI设计领域的不断进步。近年来,UI设计风格主流趋势逐渐由靠近真实的拟物化设计发展为如今的扁平化设计,随着大众审美的改变和对用户体验标准的提升,扁平化设计也在逐步拓展。在各种拓展中,Material Design遵从了设计美学原则、视觉语言原则和功能表达原则,创造了全新的扁平化UI交互体验,这样的交互体验是如何实现的,值得深思。

Computational design of promising 2D electrode materials for Li-ion and Li–S battery applications

Lithium-ion batteries(LIBs)and lithium-sulfur(Li–S)batteries are two types of energy storage systems with significance in both scientific research and commercialization.Nevertheless,the rational design of electrode materials for overcoming the bottlenecks of LIBs and Li–S batteries(such as low diffusion rates in LIBs and low sulfur utilization in Li–S batteries)remain the greatest challenge,while two-dimensional(2D)electrodes materials provide a solution because of their unique structural and electrochemical properties.In this article,from the perspective of ab-initio simulations,we review the design of 2D electrode materials for LIBs and Li–S batteries.We first propose the theoretical design principles for 2D electrodes,including stability,electronic properties,capacity,and ion diffusion descriptors.Next,classified examples of promising 2D electrodes designed by theoretical simulations are given,covering graphene,phosphorene,MXene,transition metal sulfides,and so on.Finally,common challenges and a future perspective are provided.This review paves the way for rational design of 2D electrode materials for LIBs and Li–S battery applications and may provide a guide for future experiments.

Inverse design for material anisotropy and its application for a compact X-cut TFLN on-chip wavelength demultiplexer

Inverse design focuses on identifying photonic structures to optimize the performance of photonic devices.Conventional scalar-based inverse design approaches are insufficient to design photonic devices of anisotropic materials such as lithium niobate(LN).To the best of our knowledge,this work proposes for the first time the inverse design method for anisotropic materials to optimize the structure of anisotropic-material based photonics devices.Specifically,the orientation dependent properties of anisotropic materials are included in the adjoint method,which provides a more precise prediction of light propagation within such materials.The proposed method is used to design ultra-compact wavelength division demultiplexers in the X-cut thin-film lithium niobate(TFLN)platform.By benchmarking the device performances of our method with those of classical scalar-based inverse design,we demonstrate that this method properly addresses the critical issue of material anisotropy in the X-cut TFLN platform.This proposed method fills the gap of inverse design of anisotropic materials based photonic devices,which finds prominent applications in TFLN platforms and other anisotropicmaterial based photonic integration platforms.

Preparation and Performance Study of Cementitious Capillary Crystalline Waterproof Materials

Cementitious capillary crystalline waterproof materials(CCCW for short)offer durability and excellent waterproofing properties,making them a popular option for building waterproofing.Some scholars have studied the proportioning of such materials.However,these studies lack the relationship between the impermeability pressure of mortar and the components,and the mechanism of action is somewhat debatable.Therefore,we adopted a two-step method in our experiments.Firstly,we screened out the components that significantly impact impermeability from a variety of active components by orthogonal test.We then optimized the design of the active group ratio using the simplex lattice method.Lastly,we conducted a performance test of the optimal ratio and explored the waterproofing mechanism of homemade CCCW.

Real-time OHT Dispatching Mechanism for the Interbay Automated Material Handling System with Shortcuts and Bypasses

As a key to improve the performance of the interbay automated material handling system （AMHS） in 300 mm semiconductor wafer fabrication system, the real- time overhead hoist transport （OHT） dispatching problem has received much attention. This problem is first formu- lated as a special form of assignment problem and it is proved that more than one solution will be obtained by Hungarian algorithm simultaneously. Through proposing and strictly proving two propositions related to the char- acteristics of these solutions, a modified Hungarian algo- rithm is designed to distinguish these solutions. Finally, a new real-time OHT dispatching method is carefully designed by implementing the solution obtained by the modified Hungarian algorithm. The experimental results of discrete event simulations show that, compared with con- ventional Hungarian algorithm dispatching method, the proposed dispatching method that chooses the solution with the maximum variance respectively reduces on average 4 s of the average waiting time and average lead time of wafer lots, and its performance is rather stable in multiple dif- ferent scenarios of the interbay AMHS with different quantities of shortcuts. This research provides an efficient real-time OHT dispatching mechanism for the interbay AMHS with shortcuts and bypasses.

Research of Dynamic Material Handling in an Automobile General Assembly Line Based on Real-time Information

The status of material delivery of an automobile general assembly line is analyzed, and the technique to achieve the real-time tracking of assembly status information is proposed based on RFID（ Radio Frequency Identification）. Thus the consumption of line-side buffer is obtained dynamically, then the type and quantity of needed material are fed back to the subsystem of material handling; the algorithm for determining the best time departure time of delivery driver based on minimizing of total time penalty function is proposed. This approach makes the material amount of a single delivery trip maximized and improves the efficiency of delivery drivers significantly in the case of does not affect the assembly line normal throughput. Additionally, although this dynamic material handling method is developed for the automobile assembly plant, it should be pointed out that this method is also applicable to other mixed model assembly plants such as electronics, semiconductor and aerospace industry.

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.

An Intelligent Manufacturing Platform of Polymers:Polymeric Material Genome Engineering

Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing.Polymeric material genome engineering(PMGE)is becoming a vital platform for the intelligent manufacturing of polymeric materials.However,the development of PMGE is still in its infancy,and many issues remain to be addressed.In this perspective,we elaborate on the PMGE concepts,summarize the state-of-the-art research and achievements,and highlight the challenges and prospects in this field.In particular,we focus on property estimation approaches,including property proxy prediction and machine learning prediction of polymer properties.The potential engineering applications of PMGE are discussed,including the fields of advanced composites,polymeric materials for communications,and integrated circuits.