The history and current status of materials data activities from handbook to database are reviewed, with introduction to some important products. Through an example of prediction of interfacial thermal resistance based on data and data science methods, we show the advantages and potential of material informatics to study material issues which are too complicated or time consuming for conventional theoretical and experimental methods. Materials big data is the fundamental of material informatics. The challenges and strategy to construct materials big data are discussed, and some solutions are proposed as the results of our experiences to construct National Institute for Materials Science (NIMS) materials databases.

The family of ABX half-Heusler materials, also called filled-tetrahedral structures, is a special class of ternary compounds hosting a variety of material functionalities including thermoelectricity, topological insulation, magnetism, transparent conductivity and superconductivity. This class of compounds can be derived from two substitution approaches, i.e., from Heusler materials by removing a portion of atoms forming ordered vacancies thus becoming half-Heusler, or from tetrahedral zinc blende compounds by adding atoms on the interstitial sites thus become filled-tetrahedral structures. In this paper, we briefly review the substitution approaches for material design along with their application in the design of half-Heusler compounds; then we will review the high-throughput search of new half-Heusler filled-tetrahedral structures and the study of their physical properties and functionalities.

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.

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.

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.

MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, MatCloud delivers two-fold functionalities:a computational materials platform where users can do on-line job setup, job submission and monitoring only via Web browser, and a materials properties simulation database. It is developed under Chinese Materials Genome Initiative and is a China own proprietary high-throughput computational materials infrastructure. MatCloud has been on line for about one year, receiving considerable registered users, feedbacks, and encouragements. Many users provided valuable input and requirements to MatCloud. In this paper, we describe the present MatCloud, future visions, and major challenges. Based on what we have achieved, we will endeavour to further develop MatCloud in an open and collaborative manner and make MatCloud a world known China-developed novel software in the pressing area of high-throughput materials calculations and materials properties simulation database within Material Genome Initiative.

Metal oxides play an essential role in modern optoelectronic devices because they have many unique physical properties such as structure diversity, superb stability in solution, good catalytic activity, and simultaneous high electron conductivity and optical transmission. Therefore, they are widely used in energy-related optoelectronic applications such as photovoltaics and photoelectrochemical (PEC) fuel generation. In this review, we mainly discuss the structure engineering and defect control of oxides for energy applications, especially for transparent conducting oxides (TCOs) and oxide catalysts used for water splitting. We will review our current understanding with an emphasis on the contributions of our previous theoretical modeling, primarily based on density functional theory. In particular, we highlight our previous work:(i) the fundamental principles governing the crystal structures and the electrical and optical behaviors of TCOs; (ii) band structures and defect properties for n-type TCOs; (iii) why p-type TCOs are difficult to achieve; (iv) how to modify the band structure to achieve p-type TCOs or even bipolarly dopable TCOs; (v) the origin of the high-performance of amorphous TCOs; and (vi) band structure engineering of bulk and nano oxides for PEC water splitting. Based on the understanding above, we hope to clarify the key issues and the challenges facing the rational design of novel oxides and propose new and feasible strategies or models to improve the performance of existing oxides or design new oxides that are critical for the development of next-generation energy-related applications.

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.

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 superconductor-to-insulator quantum phase transition with unprecedented accuracy in tuning the chemical doping level.