Annual Review of Materials Research - Volume 50, 2020

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.

The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compounds tend to derive their fascinating properties from a myriad of interactions spanning multiple scales, from localized chemical bonding to macroscopic interactions between grains. This diversity has limited the ability of researchers to develop new materials on a reasonable timeline. Fortunately, the advent of machine learning in materials science has provided a new approach to analyze high-dimensional space and identify correlations among the structure-composition-property-processing relationships that may have been previously missed. In this review, we examine some successful examples of using data science to improve known structural materials by analyzing fatigue and failure, and we discuss approaches to develop entirely new classes of structural materials in complex composition spaces including high-entropy alloys and bulk metallic glasses. Highlighting the recent advancement in this field demonstrates the power of data-driven methodologies that will hopefully lead to the production of market-ready structural materials.

The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmation of ML predictions, either via experiment or via physics-based simulations. In this review, we first identify the core components common to materials informatics discovery pipelines, such as training data, choice of ML algorithm, and measurement of model performance. Then we discuss some prominent examples of validated ML-driven materials discovery across a wide variety of materials classes, with special attention to methodological considerations and advances. Across these case studies, we identify several common themes, such as the use of domain knowledge to inform ML models.

Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.

Understanding the nanoscale electrodynamic properties of a material at microwave frequencies is of great interest for materials science, condensed matter physics, device engineering, and biology. With specialized probes, sensitive detection electronics, and improved scanning platforms, microwave microscopy has become an important tool for cutting-edge materials research in the past decade. In this article, we review the basic components and data interpretation of microwave imaging and its broad range of applications. In addition to the general-purpose mapping of permittivity and conductivity, microwave microscopy is now exploited to perform quantitative measurements on semiconductor devices, photosensitive materials, ferroelectric domains and domain walls, and acoustic-wave systems. Implementation of the technique in low-temperature and high-magnetic-field chambers has also led to major discoveries in quantum materials with strong correlation and topological order. We conclude the review with an outlook of the ultimate resolution, operation frequency, and future industrial and academic applications of near-field microwave microscopy.

The recently discovered topological quantum materials (TQMs) have electronic structures that can be characterized by certain topological invariants. In these novel materials, the unusual bulk and surface electrons not only give rise to many exotic physical phenomena but also foster potential new technological applications. To characterize the unusual electronic structures of these new materials, investigators have used angle-resolved photoemission spectroscopy (ARPES) as an effective experimental tool to directly visualize the unique bulk and surface electronic structures of TQMs. In this review, we first give a brief introduction of TQMs and ARPES, which is followed by examples of the application of ARPES to different TQMs ranging from topological insulators to Dirac and Weyl semimetals. We conclude with a brief perspective of the current development of ARPES and its potential application in the study of TQMs.

Transition metal dichalcogenide (TMD) monolayers and heterostructures have emerged as a compelling class of materials with transformative properties that may be harnessed for novel device technologies. These materials are commonly fabricated by exfoliation of flakes from bulk crystals, but wafer-scale epitaxy of single-crystal films is required to advance the field. This article reviews the fundamental aspects of epitaxial growth of van der Waals–bonded crystals specific to TMD films. The structural and electronic properties of TMD crystals are initially described along with sources and methods used for vapor phase deposition. Issues specific to TMD epitaxy are critically reviewed, including substrate properties and film-substrate orientation and bonding. The current status of TMD epitaxy on different substrate types is discussed along with characterization techniques for large-areaepitaxial films. Future directions are proposed, including developments in substrates, in situ and full-wafer characterization techniques, and heterostructure growth.

Integrating silicon and III-nitride technologies for high-speed and large bandwidth communication demands optically interconnected active components that detect, process, and emit photons and electrons. It is imperative that multifunctional materials can enhance the performance and simplify fabrication of such devices. Spontaneously grown GaN in the nanowall network (NwN) architecture simultaneously displays unprecedented optical and electrical properties. A two-order increase in band-edge emission makes it suitable for high-brightness light-emitting diodes and laser applications. Decorating this NwN with silver nanoparticles further enhances emission through plasmonic interactions and renders it an excellent surface-enhanced Raman spectroscopy substrate for biomolecular detection. The observation of very high electron mobility (approximately 10⁴ cm²/Vs) and large phase-coherence length (60 μm) is a consequence of two-dimensional (2D) electron gas formation applicable for high electron mobility transistors. Detecting ballistic transport in the nanowalls confirms proximity-induced superconductivity (<5 K and 8 T). Charge separation properties render it a device material for UV photodetectors, photoanodes for water splitting, and thermionic field emitters.

We live in a research era marked by impressive new tools powering the scientific method to accelerate the discovery, prediction, and control of increasingly complex systems. In common with many disciplines and societal challenges and opportunities, materials and condensed matter sciences are beneficiaries. The volume and fidelity of experimental, computational, and visualization data available, and tools to rapidly interpret them, are remarkable. Conceptual frameworks, including multiscale, multiphysics modeling of this complexity, are fueled by the data and, in turn, guide directions for future experimental and computational strategies. In this spirit, I discuss the importance of competing interactions, length scales, and constraints as pervasive sources of spatiotemporal complexity. I use representative examples drawn from materials and condensed matter, including the important role of elasticity in some technologically important quantum materials.

The emerging field of self-assembled vertically aligned nanocomposite (VAN) thin films effectively enables strain, interface, and microstructure engineering as well as (multi)functional improvements in electric, magnetic, optical, and energy-related properties. Well-ordered or patterned microstructures not only empower VAN thin films with many new functionalities but also enable VAN thin films to be used in nanoscale devices. Comparative ordered devices formed via templating methods suffer from critical drawbacks of processing complexity and potential contamination. Therefore, VAN thin films with spontaneous ordering stand out and display many appealing features for next-generation technological devices, such as electronics, optoelectronics, ultrahigh-density memory systems, photonics, and 3D microbatteries. The spontaneous ordering described in this review contains ordered/patterned structures in both in-plane and out-of-plane directions. In particular, approaches to obtaining spontaneously ordered/patterned structures in-plane are systematically reviewedfrom both thermodynamic and kinetic perspectives. Out-of-plane ordering is also discussed in detail. In addition to reviewing the progress of VAN films with spontaneous ordering, this article also highlights some recent developments in spontaneous ordering approaches and proposes future directions.

Symmetry is fundamental to understanding our physical world. An antisymmetry operation switches between two different states of a trait, such as two time states, position states, charge states, spin states, or chemical species. This review covers the fundamental concepts of antisymmetry and focuses on four antisymmetries, namely, spatial inversion in point groups, time reversal, distortion reversal, and wedge reversion. The distinction between classical and quantum mechanical descriptions of time reversal is presented. Applications of these antisymmetries—in crystallography, diffraction, determining the form of property tensors, classifying distortion pathways in transition state theory, finding minimum energy pathways, diffusion, magnetic structures and properties, ferroelectric and multiferroic switching, classifying physical properties in arbitrary dimensions, and antisymmetry-protected topological phenomena—are described.

The discovery of alternative methods of producing electrical energy that avoid the generation of greenhouse gases and do not contribute to global warming is a compelling problem of our time. Ubiquitous, but often highly distributed, sources of energy on earth exist in the small-temperature-difference regime, 10–250°C. In this review, we discuss a family of methods that can potentially recover this energy based on the use of first-order phase transformations in crystalline materials combined with ferromagnetism or ferroelectricity. The development of this technology will require a better understanding of these phase transformations, especially ferroelectric/ferromagnetic properties, hysteresis, and reversibility, as well as strategies for discovering improved materials.

Constructing hybrid composites with organic and inorganic materials at different length scales provides unconventional opportunities in the field of thermoelectric materials, which are classified as hybrid crystal, superlattice, and nanocomposite. A variety of new techniques have been proposed to fabricate hybrid thermoelectric materials with homogeneous microstructures and intimate interfaces, which are critical for good thermoelectric performance. The combination of organic and inorganic materials at the nano or atomic scale can cause strong perturbation in the structural, electron, and phonon characteristics, providing new mechanisms to decouple electrical and thermal transport properties that are not attainable in the pure organic or inorganic counterparts. Because of their increasing thermoelectric performance, compositional diversity, mechanical flexibility, and ease of fabrication, hybrid materials have become the most promising candidates for flexible energy harvesting and solid-state cooling.

Noble metals (Ru, Os, Rh, Ir, Pd, Pt, Ag, and Au) are known for their extraordinary oxidant-resistant behavior, good electrical and thermal conductivity, high work function, and brilliant luster. All occur in close-packed crystal structures: Ru and Os in hexagonal close-packed (hcp) and the rest in face-centered cubic (fcc) structures, both possessing high-symmetry structures and, therefore, a high degree of stabilization. Numerous studies in the literature have attempted to stabilize these metals away from their conventional crystal structures with the aim of realizing new properties. While obtaining conventional fcc metals in hcp structure or vice versa has been the subject of most studies, there are also examples of fcc metals crystallizing in lower-symmetry structures such as monoclinic. The nonnative crystal structures are generally realized during the crystallite growth itself, with a few exceptions in which a posttreatment was required for lattice transformation. In most cases, the new crystal structures pertain to the nanometer-length scale in the form of nanoparticles, nanoplates, nanoribbons, and nanowires, but there are good examples from microcrystallites as well. In this article, we review this active area of research, focusing on ambient stable crystal systems with some account of their interesting properties as reported in recent literature.

We review recent applications of positive muon spin relaxation (μSR) spectroscopy as an active probe of ion diffusion in energy storage materials. μSR spectroscopy allows the study of ionic diffusion in solid-state materials on a time scale between 10⁻⁵ and 10⁻⁸ s where most long-range and consecutive short-range jumps of ions between interstitial sites occur. μSR also allows one to probe and model ionic diffusion in materials that contain magnetic ions, since both electronic and nuclear contributions to the muon depolarization can be separated, making μSR an excellent technique for the microscopic study of the ionic motions in crystalline materials. We highlight a series of battery materials for which μSR has provided insight into intrinsic ionic conduction and magnetic properties without interference of external factors, such as the presence of magnetic ions, macroscopic particle morphologies, or elaborate measurement setups.

High-energy diffraction microscopy (HEDM) is an implementation of three-dimensional X-ray diffraction microscopy. HEDM yields maps of internal crystal orientation fields, strain states, grain shapes and locations as well as intragranular orientation distributions, and grain boundary character. Because it is nondestructive in hard materials, notably metals and ceramics, HEDM has been used to study responses of these materials to external fields including high temperature and mechanical loading. Currently available sources and detectors lead to a spatial resolution of ∼1 μm and an orientation resolution of <0.1^○. With the penetration characteristic of high energies (E ≥ 50 keV), sample cross-section dimensions of ∼1 mm can be studied in materials containing elements across much of the Periodic Table. This review describes hardware and software associated with HEDM as well as examples of applications. These applications include studies of grain growth, recrystallization, texture development, orientation gradients, deformation twinning, annealing twinning, plastic deformation, and additive manufacturing. We also describe relationships to other X-ray-based methods as well as prospects for further development.

Dislocations play a vital role in the mechanical behavior of crystalline materials during deformation. To capture dislocation phenomena across all relevant scales, a multiscale modeling framework of plasticity has emerged, with the goal of reaching a quantitative understanding of microstructure–property relations, for instance, to predict the strength and toughness of metals and alloys for engineering applications. This review describes the state of the art of the major dislocation modeling techniques, and then discusses how recent progress can be leveraged to advance the frontiers in simulations of dislocations. The frontiers of dislocation modeling include opportunities to establish quantitative connections between the scales, validate models against experiments, and use data science methods (e.g., machine learning) to gain an understanding of and enhance the current predictive capabilities.

Grain boundaries can undergo phase-like transitions, called complexion transitions, in which their structure, composition, and properties change discontinuously as temperature, bulk composition, and other parameters are varied. Grain boundary complexion transitions can lead to rapid changes in the macroscopic properties of polycrystalline metals and ceramics and are responsible for a variety of materials phenomena as diverse as activated sintering and liquid-metal embrittlement. The property changes caused by grain boundary complexion transitions can be beneficial or detrimental. Grain boundary complexion engineering exploits beneficial complexion transitions to improve the processing, properties, and performance of materials. Here, we review the thermodynamic fundamentals of grain boundary complexion transitions, highlight the strongest experimental and computationalevidence for these transitions, clarify a number of important misconceptions, discuss the advantages of grain boundary complexion engineering, and summarize existing research challenges.

Establishing structure–property correlations is of paramount importance to materials research. The ability to selectively detect observable magnetization from transitions between quantized spin states of nuclei makes nuclear magnetic resonance (NMR) spectroscopy a powerful probe to characterize solids at the atomic level. In this article, we review recent advances in NMR techniques in six areas: spectral resolution, sensitivity, atomic correlations, ion dynamics, materials imaging, and hardware innovation. In particular, we focus on the applications of these techniques to materials research. Specific examples are given following the general introduction of each topic and technique to illustrate how they are applied. In conclusion, we suggest future directions for advanced solid-state NMR spectroscopy and imaging in interdisciplinary research.

Recent advances in the synthesis of block copolymers have enabled the creation of smart and functional designer polymers possessing specific intermolecular interactions. The long-range nature of these interactions strongly affects the molecular packings and microstructures of such polymers, which are intimately related to their properties. In addition to various applications, their unique physicochemical properties, distinguished from conventional block copolymers, are attracting significant attention from polymer and materials scientists. In this review, we describe the current understanding of the structure-property relationship of block copolymers having long-range interactions and suggest possible directions of technological development. We particularly focus on how specific interactions, such as Coulombic, π-π stacking, hydrogen-bonding, and metal/ion-dipole interactions, affect the molecular arrangements of block copolymers on the nanometer and molecular scales. Such information could lead to block copolymers with more advanced functions for future nanotechnologies.