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.

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。

An Expert System in FRP Composite Material Design

An expert system prototype for fibre-reinforced plastic matrix (FRP) composite material design, ESFRP, has been developed. The system consists of seven main functional parts: a general inference engine, a set of knowledge bases, a material properties algorithm base, an explanation engine, various data bases, several function models and the user interface. The ESFRP can simulate human experts to make design scheme for fibre-reinforced plastics design, FRP layered plates design and FRP typical engineering components design. It can also predict the material properties and make strength analysis according to the micro and macro mechanics of composite materials. A satisfied result can be gained through the reiterative design.

GMA material design and construction quality control for asphalt pavement of steel box girder: Case study of the Hong Kong–Zhuhai–Macao Bridge

To improve the quality of the Hong Kong–Zhuhai–Macao Bridge paving project,a new paving layer material,Guss-mastic asphalt(GMA),was proposed in this paper by combining the advantages of two types of cast asphalt mixtures:mastic asphalt(MA)and Guss asphalt(GA).Based on the characteristics of GMA,to simulate its actual production process,this study developed a small-simulated cooker mixing equipment.Moreover,the flow degree,60C dynamic stability,and impact toughness were proposed to be used to evaluate the construction and ease,high temperature stability,and fatigue resistance of GMA cast asphalt mixtures,respectively.Moreover,the quality control standards for GMA paving materials by indoor tests,field trial mix GMA material performance tests,and accelerated loading tests were finalized.The study showed that the developed simulated cooker yielded consistent mixing results in the same working environment as the engineering cooker device.Increasing the coarse aggregate incorporation rate,coarsening the mastic epure(ME)gradation composition,and using a smaller oil to stone ratio can reduce the flowability of the GMA materials to varying degrees.The four-point bending fatigue life and impact toughness of the different GMA materials are correlated well.A mobility of<20 s,60C dynamic stability of 400–800 times/mm,15C impact toughness of400 N⋅mm,and cooker car mixing temperature control standard of 210C–230C form an appropriate control index system for the design and production of GMA cast asphalt mixtures.Simultaneously,accelerated loading tests verified the accuracy and reliability of the quality control index system that has been used in the GMA paving project of the Hong Kong–Zhuhai–Macao Bridge deck and has achieved good application results.

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.

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.

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.

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.

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.

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.

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.

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.

Data Centric Design:A New Approach to Design of Microstructural Material Systems

Building processing,structure,and property(PSP)relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science.Recent technological advancements in data acquisition and storage,microstructure characterization and reconstruction(MCR),machine learning(ML),materials modeling and simulation,data processing,manufacturing,and experimentation have significantly advanced researchers’abilities in building PSP relations and inverse material design.In this article,we examine these advancements from the perspective of design research.In particular,we introduce a data-centric approach whose fundamental aspects fall into three categories:design representation,design evaluation,and design synthesis.Developments in each of these aspects are guided by and benefit from domain knowledge.Hence,for each aspect,we present a wide range of computational methods whose integration realizes data-centric materials discovery and design.

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.

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.

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.

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.

Efficient sampling for decision making in materials discovery

Accelerating materials discovery crucially relies on strategies that efficiently sample the search space to label a pool of unlabeled data.This is important if the available labeled data sets are relatively small compared to the unlabeled data pool.Active learning with efficient sampling methods provides the means to guide the decision making to minimize the number of experiments or iterations required to find targeted properties.We review here different sampling strategies and show how they are utilized within an active learning loop in materials science.

Electrochemical Proton Storage:From Fundamental Understanding to Materials to Devices

Simultaneously improving the energy density and power density of electrochemical energy storage systems is the ultimate goal of electrochemical energy storage technology.An effective strategy to achieve this goal is to take advantage of the high capacity and rapid kinetics of electrochemical proton storage to break through the power limit of batteries and the energy limit of capacitors.This article aims to review the research progress on the physicochemical properties,electrochemical performance,and reaction mechanisms of electrode materials for electrochemical proton storage.According to the different charge storage mechanisms,the surface redox,intercalation,and conversion materials are classified and introduced in detail,where the influence of crystal water and other nanostructures on the migration kinetics of protons is clarified.Several reported advanced full cell devices are summarized to promote the commercialization of electrochemical proton storage.Finally,this review provides a framework for research directions of charge storage mechanism,basic principles of material structure design,construction strategies of full cell device,and goals of practical application for electrochemical proton storage.