High-throughput design of functional materials using materials genome approach

High-throughput computational materials design provides one efficient solution to accelerate the discovery and development of functional materials. Its core concept is to build a large quantum materials repository and to search for target materials with desired properties via appropriate materials descriptors in a high-throughput fashion, which shares the same idea with the materials genome approach. This article reviews recent progress of discovering and developing new functional materials using high-throughput computational materials design approach. Emphasis is placed on the rational design of high-throughput screening procedure and the development of appropriate materials descriptors, concentrating on the electronic and magnetic properties of functional materials for various types of industrial applications in nanoelectronics.

Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics

The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development and optimization of the technologies.Traditional manual methods are time-consuming and laborintensive in screening and optimizing material properties.Materials genome engineering(MGE)advances an innovative approach that combines efficient experimentation,big database and artificial intelligence(AI)algorithms to accelerate materials research and development.High-throughput(HT)research platforms perform multidimensional experimental tasks rapidly,providing a large amount of reliable and consistent data for the creation of materials databases.Therefore,the development of novel experimental methods combining HT and AI can accelerate materials design and application,which is beneficial for establishing material-processing-property relationships and overcoming bottlenecks in the development of emerging PV technologies.This review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.

Materials genome strategy for metallic glasses

Metallic glasses(MGs)have attracted extensive attention in the past decades due to their unique chem-ical,physical and mechanical properties promising for a wide range of engineering applications.A thor-ough understanding of their structure-property relationships is the key to the development of novel MGs with desirable performance.New strategies,as proposed by Materials Genome Initiative(MGI),construct a new paradigm for high-throughput materials discovery and design,and are being increas-ingly implemented in the search of new MGs.While a few reports have summarized the application of high-throughput and/or machine learning techniques,a comprehensive assessment of materials genome strategies for developing MGs is still missing.Herein,this paper aims to present a timely overview of key advances in this fascinating subject,as well as current challenges and future opportunities.A holistic approach is used to cover the related topics,including high-throughput preparation and characterization of MGs,and data-driven machine learning strategies for accelerating the development of novel MGs.Fi-nally,future research directions and perspectives for MGI-assisted design of MGs are also proposed and surmised.

Ab initio artificial intelligence:Future research of Materials Genome Initiative

The marriage of artificial intelligence(AI)and Materials Genome Initiative(MGI)could profoundly change the landscape of modern materials research,leading to a new paradigm of data-driven and AI-driven materials discovery.In this perspective,we will give an overview on the central role of AI in the MGI research.In particular,an emerging research field of ab initio AI,which applies state-of-the-art AI techniques to help solve bottleneck problems of ab initio computation,will be introduced.The development of ab initio AI will greatly accelerate highthroughput computation,promote the construction of large materials database,and open new opportunities for future research of MGI.

Building materials genome from ground-state configuration to engineering advance

Individual phases are commonly considered as the building blocks of materials.However,the accurate theoretical prediction of properties of individual phases remains elusive.The top-down approach by decoding genomic building blocks of individual phases from experimental observations is nonunique.The density functional theory(DFT),as a state-of-the-art solution of quantum mechanics,prescribes the existence of a ground-state configuration at 0 K for a given system.It is self-evident that the ground-state configuration alone is insufficient to describe a phase at finite temperatures as symmetry-breaking non-ground-state configurations are excited statistically at temperatures above 0 K.Our multiscale entropy approach(recently terms as Zentropy theory)postulates that the entropy of a phase is composed of the sum of the entropy of each configuration weighted by its probability plus the configurational entropy among all configurations.Consequently,the partition function of each configuration in statistical mechanics needs to be evaluated by its free energy rather than total energy.The combination of the ground-state and symmetry-breaking non-ground-state configurations represents the building blocks of materials and can be used to quantitatively predict free energy of individual phases with the free energy of each configuration predicted from DFT as well as all properties derived from free energy of individual phases。

Machine learning in materials genome initiative:A review

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

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.

The materials data ecosystem： Materials data science and its role in data-driven materials discovery

Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data, for the first time, has emerged as an extremely significant approach in materials discovery. Data science has been applied in different disciplines as an interdisciplinary field to extract knowledge from data. The concept of materials data science has been utilized to demonstrate its application in materials science. To explore its potential as an active research branch in the big data era, a three-tier system has been put forward to define the infrastructure for the classification, curation and knowledge extraction of materials data.

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.

First Principle Material Genome Approach for All Solid-State Batteries

Due to ever-increasing concern about safety issues in using alkali metal ionic batteries, all solid-state batteries (ASSBs) have attracted tremendous attention. The foundation to enable high-performance ASSBs lies in delivering ultra-fast ionic conductors that are compatible with both alkali anodes and high-voltage cathodes. Such a challenging task cannot be fulfilled, without solid understanding covering materials stability and properties, interfacial reactions, structural integrity, and electrochemical windows. Here in this work, we will review recent advances on fundamental modeling in the framework of material genome initiative based on the density functional theory (DFT), focusing on solid alkali batteries. Efforts are made in offering a dependable road chart to formulate competitive materials and construct "better" batteries.

Research on Copyright Protection Method of Material Genome Engineering Data Based on Zero-Watermarking

In order to effectively solve the problem of copyright protection of materials genome engineering data,this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology.First,the important attribute values are selected from the materials genome engineering database;then,use the method of remainder to group the selected attribute values and extract eigenvalues;then,the eigenvalues sequence is obtained by the majority election method;finally,XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center.When a copyright dispute requires copyright authentication for the database to be detected.First,the zero-watermarking construction algorithm is used to obtain an eigenvalues sequence;then,this sequence is XORed with the watermarking information stored in the third-party authentication center to obtain copyright information to-be-detected.Finally,the ownership is determined by calculating the similarity between copyright information to-be-detected and copyright information that has practical significance.The experimental result shows that the zero-watermarking method proposed in this paper can effectively resist various common attacks,and can well achieve the copyright protection of material genome engineering database.

Combinatorial synthesis and high-throughput characterization of copper-oxide superconductors

Fast synthesis and screening of materials are vital to the advance of materials science and are an essential component of the Materials Genome Initiative. Here we use copper-oxide superconductors as an example to demonstrate the power of integrating combinatorial molecular beam epitaxy synthesis with high-throughput electric transport measurements. Leveraging this method, we have generated a phase diagram with more than 800 compositions in order to unravel the doping dependence of interface superconductivity. In another application of the same method, we have studied the superconductorto-insulator quantum phase transition with unprecedented accuracy in tuning the chemical doping level.

High-throughput research on superconductivity

As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods.

A Prediction Method of Fracture Toughness of Nickel-Based Superalloys

Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity.To solve these problems of com-plexity,time-consuming,and low accuracy in obtaining the fracture toughness value of nickel-based superalloys through experiments.A combination prediction model is proposed based on the principle of materials genome engineering,the fracture toughness values of nickel-based superalloys at different temperatures,and different compositions can be predicted based on the existing experimental data.First,to solve the problem of insufficient feature extraction based on manual experience,the Deep Belief Network(DBN)is used to extract features,and an attention mechanism module is introduced.To achieve the purpose of strengthen-ing the important features,an attention weight is assigned to each feature accord-ing to the importance of the feature.Then,the feature vectors obtained by the DBN module based on the Attention mechanism(A-DBN)are spliced with the original features.Thus,the prediction accuracy of the model is improved by extracting high-order combined features and low-order linear features between input and output data.Finally,the spliced feature vectors are put into the Support Vector Regression(SVR)model to further improve the regression prediction abil-ity of the model.The results of the contrast experiment show that the model can effectively improve the prediction accuracy of the fracture toughness value of nickel-based superalloys.

Liquid metal material genome： Initiation of a new research track towards discovery of advanced energy materials

As the basis of modern industry, the roles materials play are becoming increasingly vital in this day and age. With many superior physical properties over conventional fluids, the low melting point liquid metal material, especially room-temperature liquid metal, is recently found to be uniquely useful in a wide variety of emerging areas from energy, electronics to medical sciences. However, with the coming enormous utilization of such materials, serious issues also arise which urgently need to be addressed. A biggest concern to impede the large scale application of room-temperature liquid metal technologies is that there is currently a strong shortage of the materials and species available to meet the tough requirements such as cost, melting point, electrical and thermal conductivity, etc. Inspired by the Material Genome Initiative as issued in 2011 by the United States of America, a more specific and focused project initiative was proposed in this paper--the liquid metal material genome aimed to discover advanced new functional alloys with low melting point so as to fulfill various increasing needs. The basic schemes and road map for this new research program, which is expected to have a worldwide significance, were outlined. The theoretical strategies and experimental methods in the research and development of liquid metal material genome were introduced. Particularly, the calculation of phase diagram （CALPHAD） approach as a highly effective way for material design was discussed. Further, the first-principles （FP） calculation was suggested to combine with the statistical thermo- dynamics to calculate the thermodynamic functions so as to enrich the CALPHAD database of liquid metals. When the experimental data are too scarce to perform a regular treatment, the combination of FP calculation, cluster variation method （CVM） or molecular dynamics （MD）, and CALPHAD, referred to as the mixed FP-CVM- CALPHAD method can be a promising way to solve the problem. Except for the theoretical strategies, several parallel processing experimental methods were also analyzed, which can help improve the efficiency of finding new liquid metal materials and reducing the cost. The liquid metal material genome proposal as initiated in this paper will accelerate the process of finding and utilization of new functional materials.

Data mining-aided materials discovery and optimization

Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper,and an introduction to the materials data mining(MDM)process is provided using case studies.Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery,design,and optimization.State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co_(3)O_(4) superstructures,materials design of layered double hydroxide,battery materials discovery,and thermoelectric materials design.The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation,and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.

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.

Advances in data-assisted high-throughput computations for material design

Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.

High-throughput experimental techniques for corrosion research:A review

High-throughput experimental techniques can accelerate and economize corrosion evaluation,and thus,have great potential in the development of new materials for corrosion protection such as corrosion-resistant metals,corrosion inhibitors,and anticorrosion coatings.This concise review highlights high-throughput experimental techniques that have been recently applied for corrosion research,including(i)the high-throughput preparation of metal samples in the form of thin films or bulk materials,(ii)high-throughput experiments based on corrosive solutions with independent or gradient parameters,(iii)high-throughput evaluation of changes in physicochemical properties,and(iv)high-throughput corrosion evaluation by electrochemical methods.To advance automated and intelligent corrosion research,future directions for the development of the high-throughput corrosion experimental and characterization techniques are also discussed.

On the application of high-throughput experimentation and data-driven approaches in metallic glasses

Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.