Skyrmions are widely regarded as promising candidates for emergent spintronic devices. Dzyaloshinskii–Moriya interaction (DMI) is often critical to the generation and manipulation of skyrmions. However, there is a fundamental lack of understanding of the origin of DMI or the mechanism by which DMI generates skyrmions in magnetic bilayers. Very little is known of the material parameters that determine

Exploring unconventional topological quasiparticles and their associated exotic physical properties has become a hot topic in condensed matter physics, thus stimulating extensive interest in recent years. Here, in contrast to the double-Weyl phonons (the topological chiral charge +2) in the trigonal and hexagonal crystal systems, we propose that the unconventional double-Weyl without counterparts in

Quantum computers hold promise to enable efficient simulations of the properties of molecules and materials; however, at present they only permit ab initio calculations of a few atoms, due to a limited number of qubits. In order to harness the power of near-term quantum computers for simulations of larger systems, it is desirable to develop hybrid quantum-classical methods where the quantum computation

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Mass spectrometry imaging (MSI) plays a pivotal role in investigating the chemical nature of complex systems that underly our understanding in biology and medicine. Multiple fields of life science such as proteomics, lipidomics and metabolomics benefit from the ability to simultaneously identify molecules and pinpoint their distribution across a sample. However, achieving the necessary submicron spatial

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative

The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. The same network architecture is successfully applied to two very different three-phase microstructures: A lithium-ion battery

Thermal conductivity is one of the most fundamental properties of materials with the value being determined by nearly all-scale structural features and multiple physical processes. Rapidly judging material’s thermal conductivity is extremely important but challenging for the applications. The material genome paradigm offers a revolutionary way to efficiently screen and discover materials with designed

The generalized stacking fault energy is a key ingredient to mesoscale models of dislocations. Here we develop an approach to quantify the dependence of generalized stacking fault energies on the degree of chemical disorder in multicomponent alloys. We introduce the notion of a “configurationally-resolved planar fault” (CRPF) energy and extend the cluster expansion method from alloy theory to express

Heterogeneous catalysts constitute a crucial component of many industrial processes, and to gain an understanding of the atomic-scale features of such catalysts, ab initio density functional theory is widely employed. Recently, growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates, which

Magnetic-field-free current-controlled switching of perpendicular magnetization via spin–orbit torque (SOT) is necessary for developing a fast, long data retention, and high-density SOT magnetoresistive random access memory (MRAM). Here, we use both micromagnetic simulations and atomistic spin dynamics (ASD) simulations to demonstrate an approach to field-free SOT perpendicular magnetization switching

The interplay of magnetism and topology opens up the possibility for exotic linear response effects, such as the anomalous Hall effect and the anomalous Nernst effect, which can be strongly enhanced by designing a large Berry curvature in the electronic structure. Magnetic Heusler compounds are a promising class of materials for this purpose because they are versatile, show magnetism, and their electronic

In the present study, we show that time-consuming manual tuning of parameters in the Rietveld method, one of the most frequently used crystal structure analysis methods in materials science, can be automated by considering the entire trial-and-error process as a blackbox optimisation problem. The automation is successfully achieved using Bayesian optimisation, which outperforms both a human expert

After a decade of periodic truss-, plate-, and shell-based architectures having dominated the design of metamaterials, we introduce the non-periodic class of spinodoid topologies. Inspired by natural self-assembly processes, spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase separation. Their theoretical parametrization is so intriguingly simple that

Recent developments in computer processing power lead to new paradigms of how problems in many-body physics and especially polymer physics can be addressed. Parallel processors can be exploited to generate millions of molecular configurations in complex environments at a second, and concomitant free-energy landscapes can be estimated. Databases that are complete in terms of polymer sequences and architecture

Multiferroics in which giant ferroelectric polarization and magnetism coexist are of tremendous potential for engineering disruptive applications in information storage and energy conversion. Yet the functional properties of multiferroics are thought to be affected detrimentally by the presence of point defects, which may be abundant due to the volatile nature of some constituent atoms and the high

Heteroanionic oxysulfide perovskite compounds represent an emerging class of new materials allowing for a wide range of tunability in the electronic structure that could lead to a diverse spectrum of novel and improved functionalities. Unlike cation ordered double perovskites—where the origins and design rules of various experimentally observed cation orderings are well known and understood—anion ordering

Excited states are different quantum states from their ground states, and spectroscopy methods that can assess excited states are widely used in materials characterization. Understanding the spectra reflecting excited states is thus of great importance for materials science. However, understanding such spectra remains difficult because excited states have usually different atomic or electronic configurations

Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties, including high strength-to-weight ratio and fracture toughness, at high temperatures. Here we elucidate the complex interplay between segregation, short-range order, and strengthening in the NbMoTaW MPEA through atomistic simulations with a highly accurate machine learning interatomic potential. In the single

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The microscopic mechanism of coercivity at finite temperature is a crucial issue for permanent magnets. Here we present the temperature dependence of the coercivity of an atomistic spin model for the highest-performance magnet Nd2Fe14B. For quantitative analysis of the magnetization reversal with thermal fluctuations, we focus on the free energy landscape as a function of the magnetization. The free

Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high-throughput (HT) computational materials design is an emergent field that promises to accelerate reliable and cost-effective

Anionic redox processes play a key role in determining the accessible capacity and cycle life of Li-rich cathode materials for batteries. We present a framework for investigating the anionic redox processes based on data readily available from standard DFT calculations. Our recipe includes a method of classifying different anionic species, counting the number of species present in the structure and

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

We present a new solid-state material phase which is a disordered solid solution but offers many ordered line-compound features. The emergent physical phenomena are rooted in the perfect short-range order which conserves the local octet rule. We model the dual-sublattice-mixed semiconductor alloy \({\mathrm{(ZnSnN}}_{\mathrm{2}}{\mathrm{)}}_{1 - x}{\mathrm{(ZnO)}}_{2x}\) using first-principles calculations

Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric, and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute Γ-point phonons, infrared intensities, Born-effective

Designing a material with multiple desired properties is a great challenge, especially in a complex material system. Here, we propose a material design strategy to simultaneously optimize multiple targeted properties of multi-component Co-base superalloys via machine learning. The microstructural stability, γ′ solvus temperature, γ′ volume fraction, density, processing window, freezing range, and oxidation

For the last few years, the research interest in magnetoelectric (ME) effect, which is the cross-coupling between ferroelectric and magnetic ordering in multiferroic materials, has experienced a significant revival. The extensive recent studies are not only conducted towards the design of sensors, actuators, transducers, and memory devices by taking advantage of the cross-control of polarization (or

The dielectric constant (ϵ) is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors, microelectronic devices, and high-voltage insulations. However, agile discovery of polymer dielectrics with desirable ϵ remains a challenge, especially for high-energy, high-temperature applications. To aid accelerated polymer dielectrics discovery, we have developed a

Despite numerous experimental and theoretical investigations of the mechanical behavior of high-capacity Si and Ge Li-ion battery anodes, our basic understanding of swelling-driven fracture in these materials remains limited. Existing theoretical studies have provided insights into elasto-plastic deformations caused by large volume change phase transformations, but have not modeled fracture explicitly

We study the anomalous Hall effect, magneto-optical properties, and nonlinear optical properties of twisted bilayer graphene aligned with hexagonal boron nitride substrate, as well as twisted double bilayer graphene systems. We show that non-vanishing valley polarizations in twisted graphene systems would give rise to the anomalous Hall effect, which can be tuned by in-plane magnetic fields. The valley

Successful application of two-dimensional transition metal dichalcogenides in optoelectronic, catalytic, or sensing devices heavily relies on the materials’ quality, that is, the thickness uniformity, presence of grain boundaries, and the types and concentrations of point defects. Raman spectroscopy is a powerful and nondestructive tool to probe these factors but the interpretation of the spectra,

Over the past decade, evolutionary algorithms, data mining, and other methods showed great success in solving the main problem of theoretical crystallography: finding the stable structure for a given chemical composition. Here, we develop a method that addresses the central problem of computational materials science: the prediction of material(s), among all possible combinations of all elements, that

We present an alternative and, for the purpose of non-crystalline materials design, a more suitable description of covalent and ionic glassy solids as statistical ensembles of crystalline local minima on the potential energy surface. Motivated by the concept of partially broken ergodicity, we analytically formulate the set of approximations under which the structural features of ergodic systems such

Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simulations, accurate energies and interatomic forces are a prerequisite, but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding. Here, we introduce

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for obtaining precise information about the local bonding of materials, but difficult to interpret without a well-vetted dataset of reference spectra. The ability to predict NMR parameters and connect them to three-dimensional local environments is critical for understanding more complex, long-range interactions. New computational methods

In recent years, the inverse design of artificial materials, in the format of thin-films and superlattices, has been an active sub-field in material science. From a joint effort from both experiment and theory, scientists are searching for new engineering methods or design rules so that the materials can be custom designed with desired functionalities in theory before the materials are actually synthesized

Negative Poisson’s ratio (NPR) in auxetic materials is of great interest due to the typically enhanced mechanical properties, which enables plenty of novel applications. In this paper, by employing first-principles calculations, we report the emergence of NPR in a class of two-dimensional honeycomb structures (graphene, silicene, h-BN, h-GaN, h-SiC, and h-BAs), which are distinct from all other known

Ferroelectric domain walls (DWs) are nanoscale topological defects that can be easily tailored to create nanoscale devices. Their excitations, recently discovered to be responsible for GHz DW conductivity, hold promise for faster signal transmission and processing compared to the existing technology. Here we find that DW phonons have unprecedented dispersion going from GHz all the way to THz frequencies

Proper design principles are essential for the efficient development of superionic conductors. However, the existing design principles are mainly proposed from the perspective of crystal structures. In this work, the face-centered cubic (fcc) anion sublattices were creatively constructed to study the effects of anion charge and lattice volume on the stability of lithium ion occupation and lithium ion

The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were, however, tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys, machine-learning methods showed that the formation of solid solutions can be very accurately

A computationally efficient first-principles approach to predict intrinsic semiconductor charge transport properties is proposed. By using a generalized Eliashberg function for short-range electron–phonon scattering and analytical expressions for long-range electron–phonon and electron–impurity scattering, fast and reliable prediction of carrier mobility and electronic thermoelectric properties is

Two-dimensional topological materials (2D TMs) have a variety of properties that make them attractive for applications including spintronics and quantum computation. However, there are only a few such experimentally known materials. To help discover new 2D TMs, we develop a unified and computationally inexpensive approach to identify magnetic and non-magnetic 2D TMs, including gapped and semi-metallic

High-entropy alloys (HEAs) have attracted great attention due to their many unique properties and potential applications. The nature of interatomic interactions in this unique class of complex multicomponent alloys is not fully developed or understood. We report a theoretical modeling technique to enable in-depth analysis of their electronic structures and interatomic bonding, and predict HEA properties

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties, especially in their edges. In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials, more complex edge reconstructions can be realized through careful control over the synthesis conditions

Kohn–Sham density functional theory (DFT) is the basis of modern computational approaches to electronic structures. Their accuracy heavily relies on the exchange-correlation energy functional, which encapsulates electron–electron interaction beyond the classical model. As its universal form remains undiscovered, approximated functionals constructed with heuristic approaches are used for practical studies

Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental

Cellular materials, widely found in engineered and nature systems, are highly dependent on their geometric arrangement. A non-uniform arrangement could lead to a significant variation of mechanical properties while bringing challenges in material design. Here, this proof-of-concept study demonstrates a machine-learning based framework with the capability of accelerated characterization and pattern

The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor’s performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate

Highly correlated orbitals coupled with phonons in two-dimension are identified for paramagnetic and optically active boron vacancy in hexagonal boron nitride by first principles methods which are responsible for recently observed optically detected magnetic resonance signal. Here, we report ab initio analysis of the correlated electronic structure of this center by density matrix renormalization group

An amendment to this paper has been published and can be accessed via a link at the top of the paper.