High-density storage technology beyond hard disk drives and flash memory is required. Efforts are underway to develop new high-density storage technology based on scanning probe-based data storage. One of the candidates for scanning probe-type storage is thermomechanical data storage (also known as millipede, developed by IBM Zürich), and another is ferroelectric data storage. In this article, probe data-storage technologies are overviewed. Thermomechanical data storage and ferroelectric data storage are described in detail for next-generation high-density data-storage technology based on scanning probe microscopy. Ferroelectric data storage and scanning nonlinear dielectric microscopy-based and field-effect transistor-type probe-based probe data storage are also described.

The piezoelectric properties of lead-free ferroelectric materials have been dramatically improved over the past two decades. For some limited applications, their properties have reached the same levels or have even surpassed the properties of the benchmark lead-based material Pb(Zr,Ti)O3 (PZT). Initial commercial lead-free products, including powders, ceramic components, films, and devices (e.g., ultrasonic cleaner, knocking sensor), are now available on the market. Several prototype devices, such as inkjet printheads, ultrasonic motors, angular sensors, and energy harvesters, have been developed. Their overall performance is still inferior to that of PZT-based devices; however, these prototypes and products point the way for future applications. Here, we provide an overview of recent industrial developments in the field and discuss the main advantages and disadvantages of lead-free piezoceramics for individual applications.

This article discusses recent studies of piezotronics and piezo-phototronics of two-dimensional (2D) materials. Two-dimensional semiconductor materials have demonstrated excellent electronic and optoelectronic properties, and these ultrathin materials are candidates for next-generation devices. Among 2D semiconductors, transition-metal dichalcogenides in particular have large in-place piezoelectricity due to the noncentrosymmetry along the armchair direction. A strong coupling of piezoelectric and semiconducting properties has been reported for Schottky contacts and p–n junctions, even in single-layer materials. Since the carrier concentration of ultrathin 2D materials can be easily modulated by external piezocharges, layered composites of ferroelectric/2D materials also show promising piezotronic and piezo-phototronic properties.

The piezophotonic effect is the coupling between piezoelectric properties and photoexcitation, where strain-induced piezopotential modulates and controls the relevant optical process. Specifically, metal ions as activators are capable of responding to photoexcitation and subsequent emission of light, also called mechanoluminescence, in general, and piezoluminescence specifically for piezoelectrics. These phenomena are helpful for understanding the materials fundamentals and conceiving widespread device applications. In this article, we briefly introduce the physical mechanisms of piezophotonics, including piezoluminescence. Selected host materials and metal-ion activators are described for demonstrating the piezophotonic effect. We provide a unified profile and recent prototypical demonstrations of light emission triggered by mechanical stimuli. The devices based on these materials offer the advantages of remote detection, nondestructive analysis, and repeatability, hence they are promising candidates for applications in stress sensing, structural health diagnosis, three-dimensional handwriting, magnetic-optical sensing, energy harvesting, biomedicine, novel light sources, and displays.

The Materials Genome Initiative (MGI) seeks to accelerate the discovery, design, development, and deployment of new materials through the creation of a materials innovation infrastructure. This infrastructure is essentially a system for providing data and tools that encapsulate our existing knowledge about materials, and the means to create new knowledge. Given this approach, MGI is also deeply linked to the ongoing exponential growth in applications of machine learning and artificial intelligence (AI) to materials research. This article explores the connections between MGI, the consequent need for data publication, the implications for data-driven science, and the application of AI to materials design. Examples will demonstrate how materials research is transforming in remarkable ways, and that the MGI vision of accelerated materials discovery is within reach.

Materials chemistry was in its infancy when I started my independent research efforts in India after returning from the United States in the late 1950s. I have investigated phase transformations of TiO2 and CsCl, and also carried out defect calculations. While working on rare-earth oxides, I made TbO2 and PrO2 by a simple solution route; this is probably an early example of chimie douce (soft chemistry). I started working on transition-metal oxides by building simple instruments, including a thermobalance and furnaces. In 1987, we were able to fully characterize the first liquid N2 superconductor (YBa2Cu3O7) using a home-built AC susceptometer. Oxides have been of great interest to me because of the variety of phenomena exhibited by them. I have studied various aspects of transition-metal oxides, including metal-insulator transitions, colossal magnetoresistance, and multiferroics. The last two decades have included research on synthesis, characterization, and properties of various nanomaterials, and in particular, two-dimensional nanosheets (graphene and its inorganic analogues). Two-dimensional sheets and other nanomaterials have been covalently cross-linked to derive new materials with novel properties. Work on water splitting and reduction of CO2, besides using aliovalent anion substitution to generate novel inorganics (Zn2NF in place of ZnO), has also been conducted.

A hallmark of life is plasticity, which enables reproduction, evolution, and environmental adaptivity. It is natural to wonder if these remarkable features in nature and biology can be realized in the materials world and implemented in the emerging fields of autonomous systems, artificial intelligence, and animal–machine interfaces. First, we describe fundamental features of neurons and synapses in the brain that are responsible for information processing. Then we discuss mechanisms governing electronic plasticity in correlated electronic quantum materials that mimic organismic behavior. We give examples of learning networks and circuits designed using quantum materials that can be implemented for machine intelligence. We conclude with suggestions for future interdisciplinary research wherein synergistic interactions between orbital filling, defects, and strain could give rise to new functionality of relevance to sensory interfaces (e.g., haptics), neural information processing, and neuroscience.

This article reviews a grounding in thin-film science and technology, an interest in combining materials science with applied physics and electrical engineering, and the active pursuit of collaborations with experts in other disciplines. That basis has enabled participation in the beginnings of integrated-circuit technology, the invention of new solar cells, the understanding of hydrogenated amorphous silicon for solar cells and thin-film transistors, the development of the principles of flexible, conformable, and stretchable electronics, and the devising and demonstration of large-area electronic systems.

It is curious that first-principles quantum simulations for establishing the electronic structure and bonding patterns of molecules and materials are conducted using fields, yet the standard theoretical approach to understanding their thermal behavior, phase transitions, and self-assembly on larger length- and time scales relies on classical force fields acting on particle degrees of freedom. This article discusses how equilibrium models of classical particle assemblies can be exactly reframed as statistical field theories, and how these theories can be numerically simulated. Today, such field-theoretic simulations have emerged as a highly efficient way to study phase transitions and self-assembly behavior in broad classes of soft materials, including block polymers, polyelectrolyte complexes, and polymeric emulsions.

Harnessing the nonvolatility of magnetism and the power of electric control, magnetoelectric devices that control magnetism electrically promise to deliver next-generation electronics systems that can store and compute large amounts of information with minimal power consumption and ultrafast processing speed. We highlight progress in magnetoelectric memory and logic prototypes using the voltage-controlled magnetic anisotropy (VCMA) effect. First, important performance metrics of VCMA-based magnetoelectric random access memory (MeRAM) are benchmarked against embedded complementary metal oxide semiconductor and other emerging embedded nonvolatile memories. We then discuss scaling of MeRAM from the physics and materials perspectives of the VCMA effect, as well as the use of magnetoelectric logic devices and circuits to realize new computing paradigms with VCMA. Finally, challenges to realize the full potential of VCMA-based memory and logic are presented: VCMA coefficient of 1000 fJ/V-m for energy-efficient write with low errors and tunneling magnetoresistance of 1000% for high density and low noise margin readout. New approaches for deterministic switching based on VCMA are needed. We share perspectives to address these challenges using new materials and device operation schemes.