Data-centric approaches have become increasingly popular in materials science, also known as informational materials science. Nanostructures often play essential roles in materials properties. Nanoinformatics is an important subset of informational materials science and a powerful tool for characterization and design of nanostructures. It allows discovery of meaningful and useful information and patterns from experimental and theoretical data and databases. This article reviews progress in nanoinformatics and informational materials science. Data-centric approaches for materials property description, construction of interatomic potentials, discovery of new inorganic compounds, efficient characterization of ionic transport and interfacial structures, hyperspectral image data analysis, and design of catalytic nanoparticles are outlined.

Improvements in computational resources over the last decade are enabling a new era of computational prediction and design of novel materials. The resulting resources are databases such as the Materials Project (www.materialsproject.org), which is harnessing the power of supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials, to design novel materials, and to make the data available for free to the community, together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for more than 70,000 materials and millions of associated materials properties. The software infrastructure carries out thousands of calculations per week, enabling screening and predictions for both novel solids as well as molecular species with targeted properties. As the rapid growth of accessible computed materials properties continues, the next frontier is harnessing that information for automated learning and accelerated discovery. In this article, we highlight some of the emerging and exciting efforts, and successes, as well as current challenges using descriptor-based and machine-learning methods for data-accelerated materials design.

The near-exponential expansion in computing resources over the last few decades has enabled a rapid increase in the capabilities of computational science, including applications to materials research. In order to harness the available resources and accelerate the field of materials design, it is critically important to develop robust and reusable automation software for preparing and performing multistep computational workflows, starting with crystal structures and ending with material properties. In the domain of first-principles calculations of crystalline materials, we highlight emerging tools for automated symmetry analysis of the atomic and electronic structure. With automation capabilities in hand, the ever-increasing amount of data also becomes a serious bottleneck in terms of organization, analysis, and reproducibility. We describe some of the progress and strategic challenges in the development of a general infrastructure for coupling computational automation with data management, emphasizing data reproducibility and provenance capture.

The expansion of programmatically accessible materials data has cultivated opportunities for data-driven approaches. Workflows such as the Automatic Flow Framework for Materials Discovery not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data is ideal for training machine-learning algorithms, which have already been employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis, and ultimately transform the practice of traditional materials discovery to one of rational and autonomous materials design.

Hafnium diselenide (HfSe2) has a high theoretical carrier mobility but is among the most reactive transition-metal dichalcogenides (TMDs). Herein, we have investigated the air stability of 2H polytype HfSe2 single-crystal thin films by spectroscopic and microscopic techniques. Raman spectroscopy measurements in conjunction with atomic force microscopy reveal the formation of selenium-rich blisters on the surface of the crystals upon air exposure. Transmission electron microscopy analysis indicates that 2H-HfSe2 undergoes a spontaneous phase change to 1T-HfSe2. These results offer Raman spectroscopy as a fast, convenient, non-destructive technique to reliably monitor the surface degradation of TMDs and present an opportunity for further study of phase changes in this material.

Graphene-based electronic DNA sequencing techniques have received significant attention over the past decade and are hoped to provide a new generation of portable, low-cost devices capable of rapid and accurate DNA sequencing. However, these devices are yet to demonstrate DNA sequencing. This is partly due to complex fabrication requirements resulting in low device yields and limited throughput. In this paper, we review the challenging fabrication of graphene-based electronic DNA sequencing devices. We will place a particular focus on common fabrication challenges and look toward the development of high-throughput, high-yield fabrication of these devices.

We report results of the studies relating to the development of the emerging nanostructured molybdenum trioxide (nMoO3)-based biocompatible label-free biosensing platform for breast cancer detection. The structural and morphological studies of the synthesized nMoO3 nanorods were investigated by XRD, SEM, X-ray photoelectron spectroscopic, and TEM techniques. This biocompatible one-dimensional (1D) nMoO3-based biosensing platform exhibited high sensitivity (0.904 µAmL/ng/cm2), wide linear detection range (2.5–110 ng/mL), and a lower detection limit as 2.47 ng/mL toward human epidermal growth factor receptor-2 detection. The results obtained using this sensor platform on serum samples of breast cancer patients were validated using ELISA.