Trainable descriptors based on artificial neural networks (ANNs) are presented with the goal of accurately predicting the structure-property relationship in the presence of various lattice defects for Si. In this architecture, interatomic distances and bond angles are directly fed to the ANNs instead of empirical analytic functions. The ANNs are hierarchically connected with another ANN that outputs the atomic energy involving many-body terms of the potential energy. All ANNs are then simultaneously trained using density-functional-theory data, which enables updating the entire functional forms numerically. For training and test datasets containing point defects, surfaces, and grain boundaries in Si, the ANN descriptor exhibits systematically smaller errors in total potential energy than those based on the symmetry function and the Chebyshev polynomials, which are commonly used in ANN potentials. The outputs of the trained descriptor each have flexible functional forms that are difficult to construct using a single separation-of-variable function in terms of the interatomic distance and the bond angle. This indicates that the ANN descriptor can acquire a better representation of diverse atomic environments via the training, which leads to the decreased error. The present study also discusses an efficient way of training the ANN descriptor in a stepwise manner while maintaining its flexibility. Furthermore, the computational time is found to be comparable to that of the symmetry function, which will pave the way for large-scale molecular simulations involving multiple lattice defects with high accuracy.

CrMnFeCoNi, also called the Cantor alloy, is a well-known high-entropy alloy whose magnetic properties have recently become a focus of attention. We present a detailed muon spin relaxation study of the influence of chemical composition and sample processing protocols on the magnetic phase transitions and spin dynamics of several different Cantor alloy samples. Specific samples studied include a pristine equiatomic sample, samples with deficient and excess Mn content, and equiatomic samples magnetized in a field of 9 T or plastically deformed in pressures up to 0.5 GPa. The results confirm the sensitive dependence of the transition temperature on composition and demonstrate that post-synthesis pressure treatments cause the transition to become significantly less homogeneous throughout the sample volume. In addition, we observe critical spin dynamics in the vicinity of the transition in all samples, reminiscent of canonical spin glasses and magnetic materials with ideal continuous phase transitions. Application of an external magnetic field suppresses the critical dynamics in the Mn-deficient sample, while the equiatomic and Mn-rich samples show more robust critical dynamics. The spin-flip thermal activation energy in the paramagnetic phase increases with Mn content, ranging from $3.1\left(3\right)\times {10}^{-21}\mathrm{J}$ for 0% Mn to $1.2\left(2\right)\times {10}^{-20}\mathrm{J}$ for 30% Mn content. These results shed light on critical magnetic behavior in environments of extreme chemical disorder and demonstrate the tunability of spin dynamics in the Cantor alloy via chemical composition and sample processing.

Understanding the interaction between the constituent layers of a functional device under an applied field is important to determine the stability of the device and, hence, its applicability. Here, we report the resistive switching behavior of solution-processed ${\mathrm{CH}}_3{\mathrm{NH}}_3{\mathrm{PbBr}}_3/{\mathrm{TiO}}_2$ heterostructure grown on fluorine-doped tin oxide (FTO-coated glass) substrate and studied the interaction of Ag electrode with ${\mathrm{CH}}_3{\mathrm{NH}}_3{\mathrm{PbBr}}_3$ under cyclic voltage sweep operation. The FTO/${\mathrm{TiO}}_2/{\mathrm{CH}}_3{\mathrm{NH}}_3{\mathrm{PbBr}}_3$/Ag memristor device shows a low SET and RESET voltage of 0.3 V and −1.4 V, respectively, with an initial ON/OFF ratio of >100. However, with the increase in the cycle number, the formation of AgBr leads to a gradual lowering of the RESET voltage and low resistance state (LRS) current, followed by a stable LRS after about 50 cycles, and the current conduction process in negative bias LRS is changed from filamentary conduction to interface dominated Fowler-Nordheim tunneling conduction. On replacing the Ag electrode with Au, the device shows a stable memristor performance with an ON/OFF ratio of ∼300. Our results show the fundamental limitation of Ag electrodes for ${\mathrm{CH}}_3{\mathrm{NH}}_3{\mathrm{PbBr}}_3$-based resistive switching devices, which may be beneficial in understanding the interaction of electrode materials with other memristor devices.

Understanding the evolution of the space charge layer (SCL) at the electrode-solid electrolyte (SE) interface is crucial for elucidating the failure mechanism and addressing the rate performance bottleneck of all-solid-state batteries (ASSBs). However, discrepancies between current theories and experimental reports necessitate the urgent development of a comprehensive theoretical prediction. Herein, we present a comprehensive study of the defect formation energies, electronic band alignments, and potential profiles in $\mathrm{Li}/\gamma \text{−}{\mathrm{Li}}_3{\mathrm{PO}}_4/{\mathrm{Li}}_x{\mathrm{CoO}}_2$ system to probe the SCL formation and its impact on Li-ion transport using a first-principles-informed thermodynamic model. We reveal the formation of Li defects in $\gamma \text{−}{\mathrm{Li}}_3{\mathrm{PO}}_4$ and at the SE/electrode interfaces, and find that the dominant Li carrier in $\gamma \text{−}{\mathrm{Li}}_3{\mathrm{PO}}_4$ changes with Li chemical potential within the battery. Through the analysis of defect formation energies and band alignments, we predict that the SCL, arising from the Li-ion and electron transfer, is responsible for the interface potential drops, influencing the Li-ion transport. In addition, we find that the largest potential drop occurs at the $\mathrm{Li}/\gamma \text{−}{\mathrm{Li}}_3{\mathrm{PO}}_4$ interface, which makes it a rate-limiting interface, hindering the discharge process. Furthermore, it is found that the interfacial potential drops depend strongly on both the battery's state of charge (SOC) and the crystal orientation. Interestingly, different $\gamma \text{−}{\mathrm{Li}}_3{\mathrm{PO}}_4$ crystal orientations determine initial interfacial potential drops with ${\mathrm{Li}}_x{\mathrm{CoO}}_2$ cathode, with (100) orientation showing a lower drop than (010). These insights enhance our understanding of the SCL's impact on Li-ion transport during cycling and suggest strategies for improving ASSBs' power output by tuning crystal orientation or the electrolyte valence band.

Solute segregation along grain boundaries (GBs) profoundly affects their thermodynamic and kinetic behaviors in polycrystalline materials. Recently, the spectral approach has emerged as a powerful tool to predict GB segregation. However, previous GB segregation predictions using this method relied heavily on the single-solute (SS) segregation energy spectrum without solute-solute interactions, which were often incorporated through a fitting parameter. In this paper, we develop a dual-solute (DS) model whose segregation energy spectrum intrinsically incorporates solute-solute interactions. It was first validated for GB segregation prediction in the Al-Mg system and then extended to several other binary alloy systems. The DS model shows significant improvement over the SS model and can accurately predict the real segregation states obtained by hybrid molecular dynamics/Monte Carlo simulations within a broad temperature range with different solute concentrations before forming secondary phases. This DS model provides an effective method for accurately predicting GB segregation in nanocrystalline metals.

In this paper we used ab initio random structure searching (AIRSS) to carry out a systematic search for crystalline Na-Ge materials at both 0 and 10 GPa. The high-throughput structural relaxations were accelerated using a machine-learned interatomic potential (MLIP) fit to density-functional theory (DFT) reference data, allowing $\sim 1.5$ million structures to be relaxed. At ambient conditions we predict three new Zintl phases, ${\mathrm{Na}}_3{\mathrm{Ge}}_2,{\mathrm{Na}}_2\mathrm{Ge}$, and ${\mathrm{Na}}_9{\mathrm{Ge}}_4$, to be stable and a number of Ge-rich layered structures to lie in close proximity to the convex hull. The known ${\mathrm{Na}}_{\delta }{\mathrm{Ge}}_{34}$ clathrate and ${\mathrm{Na}}_4{\mathrm{Ge}}_{13}$ host-guest structures are found to be relatively stabilized at higher temperature by vibrational contributions to the free energy. Overall, the low-energy phases exhibit exceptional structural diversity, with the expected mixture of covalent and ionic bonding confirmed using the electron-localization function (ELF). The local Ge structural motifs present at each composition were determined using smooth overlap of atomic positions (SOAP) descriptors and the Ge-K edge was simulated for representatives of each motif, providing a direct link to experimental x-ray absorption spectroscopy (XAS). Two Ge-rich phases are predicted to be stable at 10 GPa; ${\mathrm{NaGe}}_3$ and ${\mathrm{NaGe}}_2$ have simple kagome and simple hexagonal Ge lattices respectively with Na contained in the pores. ${\mathrm{NaGe}}_3$ is isostructural with the ${\mathrm{MgB}}_3$ and ${\mathrm{MgSi}}_3$ family of kagome superconductors and remains dynamically stable at 0 GPa. Removing the Na from ${\mathrm{NaGe}}_2$ results in the hexagonal lonsdalite Ge allotrope, which has a direct band gap.

Halide double perovskites are a chemically diverse and growing class of compound semiconductors that are promising for optoelectronic applications. However, the prediction of their fundamental gaps and optical properties with density functional theory (DFT) and ab initio many-body perturbation theory has been a significant challenge. Recently, a nonempirical Wannier-localized optimally tuned screened range-separated hybrid (WOT-SRSH) functional has been shown to accurately produce the fundamental band gaps of a wide set of semiconductors and insulators, including lead halide perovskites. Here, we apply the WOT-SRSH functional to five halide double perovskites and compare the results with those obtained from other known functionals and previous $GW$ calculations. We also use the approach as a starting point for $GW$ calculations and we compute the band structures and optical absorption spectrum for ${\mathrm{Cs}}_2{\mathrm{AgBiBr}}_6$, using both time-dependent DFT and the $GW$-Bethe-Salpeter equation approach. We show that the WOT-SRSH functional leads to accurate fundamental and optical band gaps, as well as optical absorption spectra, consistent with spectroscopic measurements, thereby establishing WOT-SRSH as a viable method for the accurate prediction of optoelectronic properties of halide double perovskites.

Using high-throughput automation of ab initio impurity embedding simulations, we create a database of $3d$ and $4d$ transition metal defects embedded into the prototypical topological insulators (TIs) ${\mathrm{Bi}}_2{\mathrm{Te}}_3$ and ${\mathrm{Bi}}_2{\mathrm{Se}}_3$. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of nonmagnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian. We uncover chemical trends in the exchange coupling constants and discuss the impurities' abilities with respect to magnetic order in the fields of quantum anomalous Hall insulators and topological quantum computing. In particular, we confirm that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs.

Alternative magnetic materials, or “altermagnets,” characterized by their unconventional nonrelativistic, momentum-dependent spin-split states, represent a cutting-edge advancement in the field of collinear magnetism, offering promising avenues for spintronic applications. Among these materials, hexagonal MnTe has emerged as a standout material candidate for its substantial spin-splitting. In this study, employing first-principles electronic structure calculations and spin group symmetry analysis, we delve into the interplay of altermagnetism and pressure in two main phases of MnTe. Our relativistic calculations demonstrate the presence of the tunable anomalous Hall effect (AHE) in hexagonal MnTe. In addition, our results underscore the pivotal role of pressure as a tuning parameter for the alternative magnetic traits in the system. Furthermore, we identify another phase of MnTe with orthorhombic structure, namely $\gamma \text{-MnTe}$, hosting altermagnetic characteristics. We study, in detail, its response in AHE and spin-spliting due to magnetization and pressure variations, respectively. Our study highlights the substantial impact of pressure on the properties of alternative magnetic materials, particularly emphasizing the pronounced tuning effect observed in the hexagonal and orthorhombic MnTe.

The Mn chalcogenides show a remarkable variation in structural and physical properties with density. In ${\mathrm{MnS}}_{\text{2}}$ we observe a pronounced difference with the effects of varying hydrostaticity with pressure inducing a structural transition at 300 K. Within the pyrite phase, a colossal electrical resistance drop of ${10}^8\mathrm{Ohms}$ is observed by 12 GPa of nonhydrostatic compression at 300 K. DFT simulations reveal the metallization to be unexpectedly driven by previously unoccupied ${\mathrm{S}}_2^{2-}{\sigma }_{\text{3p}}^{*}$ antibonding states crossing the Fermi level. This is a seldom seen variant of the charge transfer insulator to metal transition for negative charge transfer insulators which have anions with an unsaturated valence. By 36 GPa of nonhydrostatic compression, the single presence of the low-spin insulating arsenopyrite ($P{2}_1/c$) phase is confirmed, and the bulk metallicity is broken with the system returning to an insulative electronic state.

Using first principles–based effective Hamiltonian, electric field–controlled (reversible and repeatable) motion of spontaneously created topological charge particle across the thickness of a (001)-oriented epitaxially strained $\mathrm{BiFe}{\mathrm{O}}_3$ thin film is studied. It is shown that the electric field below a critical value moves this topological particle reversibly and gradually up and down across the film thickness, while the field higher than the critical value moves it from the bottom of the thin film straight to the top and back under alternating positive and negative fields. These field-controlled mobile topological charge particles are shown to be chiral and have topological charge corresponding to a meron. These electric-field-controlled meron motions can provide inspiration for the future of topological entity–based ferroelectric memory devices.

The discovery of 2D materials opens up unprecedented opportunities to design new materials with specified properties. In many cases, the design guiding principle is based on one or another proximity effect, i.e., the nanoscale-penetration of electronic correlations from one material to another. In few-layer van der Waals (vdW) heterostructures the proximity regions occupy the entire system. Here, we demonstrate that the physics of magnetic and superconducting proximity effects in 2D superconductor/ferromagnet vdW heterostructures is determined by the effects of interface hybridization of the electronic spectra of both materials. The degree of hybridization can be adjusted by gating, which makes it possible to achieve a high degree of controllability of the proximity effect. In particular, we show that this allows for electrical switching of superconductivity in such structures on and off, as well as for control of the amplitude and sign of the Zeeman splitting of superconducting spectra, opening interesting opportunities for spintronics and spin caloritronics.

Delicate competition between comparable energy scales of spin-orbit coupling, electron correlation, and electronic bandwidth in iridates drives the emergence of exotic quantum states. Here, we report the epitaxial growth of monoclinic ${\mathrm{SrIrO}}_3$ (001) films on ${\mathrm{SrTiO}}_3$ (111) substrates and the observation of a transition from a semimetallic to a correlated insulating state upon reducing thickness. The insulating state is associated with robust and long-range ordered magnetism with a saturated moment of $\sim 0.6{\mu }_{\mathrm{B}}/\mathrm{Ir}$. We reveal that the semimetal to insulator transition and emergent magnetism is triggered by the enhanced electron correlation as a consequence of spatial confinement. Our work indicates monoclinic ${\mathrm{SrIrO}}_3$ can be a platform for tailoring nontrivial electronic and magnetic ground state.

The intermetallic compound ZnPd has been found to have desirable characteristics as a catalyst for the steam reforming of methanol. The understanding of the surface structure of ZnPd is important to optimize its catalytic behavior. However, due to the lack of bulk single-crystal samples and the complexity of characterizing surface properties in the available polycrystalline samples using common experimental techniques, all previous surface science studies of this compound have been performed on surface alloy samples formed through thin-film deposition. In this study, we present findings on the chemical and atomic structure of the surfaces of bulk polycrystalline ZnPd studied by a variety of complementary experimental techniques, including scanning tunneling microscopy (STM), x-ray photoelectron spectroscopy (XPS), low energy electron microscopy (LEEM), photoemission electron microscopy (PEEM), and microspot low-energy electron diffraction ($\mu$-LEED). These experimental techniques, combined with density functional theory (DFT)-based thermodynamic calculations of surface free energy and detachment kinetics at the step edges, confirm that surfaces terminated by atomic layers composed of both Zn and Pd atoms are more stable than those terminated by only Zn or Pd layers. DFT calculations also demonstrate that the primary contribution to the tunneling current arises from Pd atoms, in agreement with the STM results. The formation of intermetallics at surfaces may contribute to the superior catalyst properties of ZnPd over Zn or Pd elemental counterparts.

It is common knowledge that noble metals are excellent conductors but do not exhibit superconductivity. On the other hand, quantum confinement in thin films has been consistently shown to induce a significant enhancement of the superconducting critical temperature in several superconductors. It is, therefore, an important fundamental question whether ultrathin film confinement may induce observable superconductivity in nonsuperconducting metals. We present a generalization, in the Eliashberg framework, of a BCS theory of superconductivity in good metals under thinfilm confinement. By numerically solving these new Eliashberg-type equations, we find the dependence of the superconducting critical temperature on the film thickness $L$. This parameter-free theory predicts a maximum increase in the critical temperature for a specific value of the film thickness, which is a function of the number of free carriers in the material. Exploiting this fact, we predict that ultrathin films of gold, silver, and copper of suitable thickness could be superconductors at low but experimentally accessible temperatures. We demonstrate that this is a fine-tuning problem where the thickness must assume a very precise value, close to half a nanometer.

This work demonstrates how first-principles statistical mechanics approaches within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of 0 K ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the fcc zirconium nitride system, including confidence intervals on order-disorder transition temperatures.

Due to their excellent abilities, machine learning potential models have shown significant promise in accurately constructing potential energy surfaces for various materials. As a data-driven approach, however, the training of these models necessitates carefully curated datasets to be effective. In this paper, we propose an active learning scheme for message passing neural networks (MPNNs). This method leverages the latent distance in the descriptor space, inherently generated by MPNNs, to quantify model uncertainty. Additionally, we employ CUR matrix decomposition for sample selection to reduce data redundancy. We demonstrate the application of this method on ${\mathrm{HfO}}_2$, a complex ferroelectric material with many phases, by investigating its static properties, phonon spectrum, and notably, structural phase transitions. We also demonstrate the reliability of latent distance as a model uncertainty metric. Utilizing latent space distance, the proposed method enriches active learning frameworks for machine learning potentials, providing an efficient alternative to traditional methods, such as the “query by committee” approach.

The discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy ($K_1$). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization ($J_s>$ 1 T). The structures with desirable magnetic properties center on ${\left(\mathrm{Fe},\mathrm{Co}\right)}_2\mathrm{P}$. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: ${\mathrm{Fe}}_2\mathrm{P}$ and ${\mathrm{Co}}_2\mathrm{P}$. Our work provides guidance for synthesis. We find ${\mathrm{Fe}}_7{\mathrm{CoP}}_4$ shows the most promise ($J_s=1.03\mathrm{T}$ and $K_1=0.83\mathrm{MJ}/{\mathrm{m}}^3$).

We investigate the structural and electronic origin of antiferromagnetic (AFM) coupling in the yttrium iron garnet (YIG) and permalloy (${\text{Ni}}_{80}{\text{Fe}}_{20}$, Py) bilayer system at the atomic level. Ferromagnetic resonance (FMR) spectra reveal unique hybrid modes in samples prepared with surface ion milling, indicative of antiferromagnetic exchange coupling at the YIG/Py interface. Using atomic resolution scanning transmission electron microscopy (STEM), we found that AFM coupling appears at the YIG/Py interface of the tetrahedral YIG surface formed with ion milling. The STEM measurements suggest that the interfacial AFM coupling is predominantly driven by an oxygen-mediated superexchange coupling mechanism, which is confirmed by the density-functional theory (DFT) calculations to be energetically favorable. Thus, the combined experimental and theoretical results reveal the critical role of interfacial atomic structure in determining the type of magnetic coupling in a YIG/ferromagnet heterostructure, and prove that the interfacial structure can be experimentally tuned by surface ion milling.

We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.