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 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.

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.

Combinatorial approaches for high-throughput characterization of mechanical properties

Since the first successful story was reported in the middle of 1990s,combinatorial materials science has attracted more and more attentions in the materials community.In the past two decades,a great amount of effort has been made to develop combinatorial high-throughput approaches for materials research.However,few high-throughput mechanical characterization methods and tools were reported.To date,a number of micro-scale mechanical characterization tools have been developed,which provided a basis for combinatorial high-throughput mechanical characterization.Many existing micro-mechanical testing apparatuses can be pertinently modified for high-throughput characterization.For example,automated scanning nanoindentation is used for measuring the hardness and elastic modulus of diffusion multiple alloy samples,and cantilever beam arrays are used to parallelly characterize the thermal mechanical behavior of thin films with wide composition gradients.The interpretation of micro-mechanical testing data from thin films and micro-scale samples is most critical and challenging,as the mechanical properties of their bulk counterparts cannot be intuitively extrapolated due to the well-known size and microstructure dependence.Nevertheless,high-throughput mechanical characterization data from combinatorial micro-scale samples still reflect the dependence trend of the mechanical properties on compositions and microstructure,which facilitates the understanding of intrinsic materials behavior and the fast screening of bulk mechanical properties.After the promising compositions and microstructure are pinned down,bulk samples can be prepared to measure the accurate properties and verify the combinatorial high-throughput characterization results.By developing combinatorial high-throughput mechanical characterization methods and tools,in combination with high-throughput synthesis,the structural materials research would be promoted by accelerating the discovery,development,and deployment of high performance structural materials,and by providing full spectrum of materials data for mapping composition-microstructure-mechanical properties.The latter would significantly improve the advanced structural materials design using materials genome engineering approach in the future.

Toward Rational and Modular Molecular Design in Soft Matter Engineering

This essay discusses some preliminary thoughts on the development of a rational and modular approach for molecular design in soft matter engineering and proposes ideas of structural and functional synthons for advanced functional materials. It echoes the Materials Genome Initiative by practicing a tentative retro-functional analysis （RFA） scheme. The importance of hierarchical structures in transferring and amplifying molecular functions into macroscopic properties is recognized and emphasized. According to the role of molecular segments in final materials, there are two types of building blocks： structural synthon and functional synthon. Guided by a specific structure for a desired function, these synthons can be modularly combined in various ways to construct molecular scaffolds. Detailed molecular structures are then deduced, designed and synthesized precisely and modularly. While the assembled structure and property may deviate from the original design, the study may allow further refinement of the molecular design toward the target function, The strategy has been used in the development of soft fullerene materials and other giant molecules. There are a few aspects that are not yet well addressed： （1） function and structure are not fully decoupled and （2） the assembled hierarchical structures are sensitive to secondary interactions and molecular geometries across different length scales. Nevertheless, the RFA approach provides a starting point and an alternative thinking pathway by provoking creativity with considerations from both chemistry and physics. This is particularly useful for engineering soft matters with supramolecular lattice formation, as in giant molecules, where the synthons are relatively independent of each other.

How Machine Learning Accelerates the Development of Quantum Dots?

With the rapid developments in the field of information technology,the material research society is looking for an alternate scientific route to the traditional methods of trial and error in material research and process de-velopment.Machine learning emerges as a new research paradigm to accel-erate the application-oriented material discovery.Quantum dots are ex-panded as functional nanomaterials to enhance cutting-edge photonic technology.However,they suffer from uncertainty in industrial fabrication and application.Here,we discuss how machine learning accelerates the development of quantum dots.The basic principles and operation proce-dures of machine learning are described with a few representative examples of quantum dots.We emphasize how machine learning contributes to the optimization of synthesis and the analysis of material characterizations.To conclude,we give a short perspective discussing the problems of combining machine learning and quantum dots.