Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials.

Ternary metal oxides are crucial components in a wide range of applications and have been extensively cataloged in experimental materials databases. However, there still exist cation combinations with unknown stability and structures of their compounds in oxide forms. In this study, we employ extensive crystal structure prediction methods, accelerated by machine-learned potentials, to investigate these untapped chemical spaces. We examine 181 ternary metal oxide systems, encompassing most cations except for partially filled 3d or f shells, and determine their lowest-energy crystal structures with representative stoichiometry derived from prevalent oxidation states or recommender systems. Consequently, we discover 45 ternary oxide systems containing stable compounds against decomposition into binary or elemental phases, the majority of which incorporate noble metals. Comparisons with other theoretical databases highlight the strengths and limitations of informatics-based material searches. With a relatively modest computational resource requirement, we contend that heuristic-based structure searches, as demonstrated in this study, offer a promising approach for future materials discovery endeavors.

Anion Extraction-Induced Polymorph Control of Transition Metal Dichalcogenides.

Controlled phase conversion in polymorphic transition metal dichalcogenides (TMDs) provides a new synthetic route for realizing tunable nanomaterials. Most conversion methods from the stable 2H to metastable 1T phase are limited to kinetically slow cation insertion into atomically thin layered TMDs for charge transfer from intercalated ions. Here, we report that anion extraction by the selective reaction between carbon monoxide (CO) and chalcogen atoms enables predictive and scalable TMD polymorph control. Sulfur vacancy, induced by anion extraction, is a key factor in molybdenum disulfide (MoS2) polymorph conversion without cation insertion. Thermodynamic MoS2-CO-CO2 ternary phase diagram offers a processing window for efficient sulfur vacancy formation with precisely controlled MoS2 structures from single layer to multilayer. To utilize our efficient phase conversion, we synthesize vertically stacked 1T-MoS2 layers in carbon nanofibers, which exhibit highly efficient hydrogen evolution reaction catalytic activity. Anion extraction induces the polymorph conversion of tungsten disulfide (WS2) from 2H to 1T. This reveals that our method can be utilized as a general polymorph control platform. The versatility of the gas-solid reaction-based polymorphic control will enable the engineering of metastable phases in 2D TMDs for further applications.

A molecular dynamics study on the interface morphology of vapor-deposited amorphous organic thin films.

The interfaces between amorphous organic layers play an important role in the efficiency and lifetime of organic light emitting diodes (OLEDs). However, an atomistic understanding of the interface morphology is still poor. In this study, we theoretically investigate the interfacial structure of amorphous organic films using molecular dynamics simulations that mimic vapor-deposition processes. We find that molecularly sharp interfaces are formed by the vapor-deposition process as the interface thickness spans only a mono- or double-layer in terms of lie-down geometry. Interestingly, the interface is more diffusive into the upper layer due to asymmetric interdiffusion during the vapor-deposition process, which is well described by a simple random-walk model. Additionally, we investigate the change in the molecular orientation of interdiffused molecules, which is crucial for device performance.

Identification of ground-state spin ordering in antiferromagnetic transition metal oxides using the Ising model and a genetic algorithm.

We identify ground-state collinear spin ordering in various antiferromagnetic transition metal oxides by constructing the Ising model from first-principles results and applying a genetic algorithm to find its minimum energy state. The present method can correctly reproduce the ground state of well-known antiferromagnetic oxides such as NiO, Fe2O3, Cr2O3 and MnO2. Furthermore, we identify the ground-state spin ordering in more complicated materials such as Mn3O4 and CoCr2O4.

Structure of amorphous GeSe9 by neutron diffraction and first-principles molecular dynamics: Impact of trajectory sampling and size effects.

The structure of glassy GeSe9 was investigated by combining neutron diffraction with density-functional-theory-based first-principles molecular dynamics. In the simulations, three different models of N = 260 atoms were prepared by sampling three independent temporal trajectories, and the glass structures were found to be substantially different from those obtained for models in which smaller numbers of atoms or more rapid quench rates were employed. In particular, the overall network structure is based on Sen chains that are cross-linked by Ge(Se4)1/2 tetrahedra, where the latter are predominantly corner as opposed to edge sharing. The occurrence of a substantial proportion of Ge-Se-Se connections does not support a model in which the material is phase separated into Se-rich and GeSe2-rich domains. The appearance of a first-sharp diffraction peak in the Bhatia-Thornton concentration-concentration partial structure factor does, however, indicate a non-uniform distribution of the Ge-centered structural motifs on an intermediate length scale.

Investigation of field effects in a solid-state nanopore transistor.

In order to calculate ion currents through solid-state nanopore transistors realistically, we propose a computational model based on the Poisson-Nernst-Plank equation. In the present model, we determine the surface charge density locally on the nanopore by imposing consistency between the ion distribution and the chemical reaction at the surface. The model can consider a non-uniform influence by the gate voltage on the inner surface of the nanopore membrane, which enables us to investigate ion currents depending on the gate geometry such as the thickness and vertical position within the nanopore. We verify the validity of the model by comparing the pH dependence of simulation results with the extant experimental results. We also investigate the transistor behaviour depending on the surface material, pore geometry and gate position. In particular, we propose an optimized system to enhance the on/off ratio of the nanopore transistor.

Hydrated manganese(II) phosphate (Mn3(PO4)2·3H2O) as a water oxidation catalyst.

The development of a water oxidation catalyst has been a demanding challenge in realizing water splitting systems. The asymmetric geometry and flexible ligation of the biological Mn4CaO5 cluster are important properties for the function of photosystem II, and these properties can be applied to the design of new inorganic water oxidation catalysts. We identified a new crystal structure, Mn3(PO4)2·3H2O, that precipitates spontaneously in aqueous solution at room temperature and demonstrated its high catalytic performance under neutral conditions. The bulky phosphate polyhedron induces a less-ordered Mn geometry in Mn3(PO4)2·3H2O. Computational analysis indicated that the structural flexibility in Mn3(PO4)2·3H2O could stabilize the Jahn-Teller-distorted Mn(III) and thus facilitate Mn(II) oxidation. This study provides valuable insights into the interplay between atomic structure and catalytic activity.

Modified Activation-Relaxation Technique (ARTn) Method Tuned for Efficient Identification of Transition States in Surface Reactions.

Exploring potential energy surfaces (PES) is essential for unraveling the underlying mechanisms of chemical reactions and material properties. While the activation-relaxation technique (ARTn) is a state-of-the-art method for identifying saddle points on PES, it often faces challenges in complex energy landscapes, especially on surfaces. In this study, we introduce iso-ARTn, an enhanced ARTn method that incorporates constraints on an orthogonal hyperplane and employs an adaptive active volume. By leveraging a neural network potential (NNP) to conduct an exhaustive saddle point search on the Pt(111) surface with 0.3 monolayers of surface oxygen coverage, iso-ARTn achieves a success rate that is 8.2% higher than the original ARTn, with 40% fewer force calls. Moreover, this method effectively finds various saddle points without compromising the success rate. Combined with kinetic Monte Carlo simulations for event table construction, iso-ARTn with NNP demonstrates the capability to reveal structures consistent with experimental observations. This work signifies a substantial advancement in the investigation of PES, enhancing both the efficiency and breadth of saddle point searches.

Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations.

Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due to their data efficiency and high accuracy. However, parallelizing GNN-IPs poses challenges because multiple message-passing layers complicate data communication within the spatial decomposition method, which is preferred by many molecular dynamics (MD) packages. In this article, we propose an efficient parallelization scheme compatible with GNN-IPs and develop a package, SevenNet (Scalable EquiVariance-Enabled Neural NETwork), based on the NequIP architecture. For MD simulations, SevenNet interfaces with the LAMMPS package. Through benchmark tests on a 32-GPU cluster with examples of SiO2, SevenNet achieves over 80% parallel efficiency in weak-scaling scenarios and exhibits nearly ideal strong-scaling performance as long as GPUs are fully utilized. However, the strong-scaling performance significantly declines with suboptimal GPU utilization, particularly affecting parallel efficiency in cases involving lightweight models or simulations with small numbers of atoms. We also pretrain SevenNet with a vast data set from the Materials Project (dubbed "SevenNet-0") and assess its performance on generating amorphous Si3N4 containing more than 100,000 atoms. By developing scalable GNN-IPs, this work aims to bridge the gap between advanced machine-learning models and large-scale MD simulations, offering researchers a powerful tool to explore complex material systems with high accuracy and efficiency.

Disorder-Dependent Li Diffusion in Li6PS5Cl Investigated by Machine-Learning Potential.

Solid-state electrolytes with argyrodite structures, such as Li6PS5Cl, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio molecular dynamics (MD) simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in Li6PS5Cl at 300 K using large-scale, long-term MD simulations empowered by machine-learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a 5 × 5 × 5 supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites rather than at 50% where the disorder is maximized. In addition, Li-ion diffusion shows non-Arrhenius behavior, leading to different activation energies at high temperatures (>400 K). These phenomena are explained by the interplay between inter- and intracage jumps. By elucidation of the key factors affecting Li-ion diffusion in Li6PS5Cl, this work paves the way for optimizing ionic conductivity in the argyrodite family.

Atomistic Simulation of HF Etching Process of Amorphous Si3N4 Using Machine Learning Potential.

An atomistic understanding of dry-etching processes with reactive molecules is crucial for achieving geometric integrity in highly scaled semiconductor devices. Molecular dynamics (MD) simulations are instrumental, but the lack of reliable force fields hinders the widespread use of MD in etching simulations. In this work, we develop an accurate neural network potential (NNP) for simulating the etching process of amorphous Si3N4 with HF molecules. The surface reactions in diverse local environments are considered by incorporating several types of training sets: baseline structures, reaction-specific data, and general-purpose training sets. Furthermore, the NNP is refined through iterative comparisons with the density functional theory results. Using the trained NNP, we carry out etching simulations, which allow for detailed observation and analysis of key processes such as preferential sputtering, surface modification, etching yield, threshold energy, and the distribution of etching products. Additionally, we develop a simple continuum model, built from the MD simulation results, which effectively reproduces the surface composition obtained with MD simulations. By establishing a computational framework for atomistic etching simulation and scale bridging, this work will pave the way for more accurate and efficient design of etching processes in the semiconductor industry, enhancing device performance and manufacturing precision.

Supramolecular Cl⋠⋠⋠H and O⋠⋠⋠H interactions in self-assembled 1,5-dichloroanthraquinone layers on Au(111).

The role of halogen bonds in self-assembled networks for systems with Br and I ligands has recently been studied with scanning tunneling microscopy (STM), which provides physical insight at the atomic scale. Here, we study the supramolecular interactions of 1,5-dichloroanthraquinone molecules on Au(111), including Cl ligands, by using STM. Two different molecular structures of chevron and square networks are observed, and their molecular models are proposed. Both molecular structures are stabilized by intermolecular Cl⋅⋅⋅H and O⋅⋅⋅H hydrogen bonds with marginal contributions from Cl-related halogen bonds, as revealed by density functional theory calculations. Our study shows that, in contrast to Br- and I-related halogen bonds, Cl-related halogen bonds weakly contribute to the molecular structure due to a modest positive potential (σ hole) of the Cl ligands.

Thermodynamics of native point defects in α-Fe2O3: an ab initio study.

The native point defects in Fe2O3 are theoretically investigated using ab initio methods based on the GGA + U formalism. We consider vacancies and interstitials of Fe and O atoms as well as the electron polaron as Fe(II) defects at the host Fe(III) site. The formation energies and charge transition levels are computed for each defect type with careful elimination of size effects of the supercell. It is found that the Fe interstitial and vacancy form donor and acceptor levels close to band edges, respectively, thereby allowing for charge carriers at room temperature. We determine the oxygen deficiency under high-temperature equilibrium conditions and find an excellent agreement with experiment. In the quenched condition, it is found that the Fermi level is pinned at ~0.5 eV below the conduction band minimum, which may limit the performance of Fe2O3 as a photoanode in solar water-splitting cells. Furthermore, the oxygen vacancy is mostly neutral and the Fe interstitial is responsible for electron carriers.

Role of preferential weak hybridization between the surface-state of a metal and the oxygen atom in the chemical adsorption mechanism.

We report on the chemical adsorption mechanism of atomic oxygen on the Pt(111) surface using angle-resolved-photoemission spectroscopy (ARPES) and density functional calculations. The detailed band structure of Pt(111) from ARPES reveals that most of the bands near the Fermi level are surface-states. By comparing band maps of Pt and O/Pt, we identify that dxz (dyz) and dz(2) orbitals are strongly correlated in the surface-states around the symmetry point M and K, respectively. Additionally, we demonstrate that the s- or p-orbital of oxygen atoms hybridizes preferentially with the dxz (dyz) orbital near the M symmetry point. This weak hybridization occurs with minimal charge transfer.

Reconfigurable Manipulation of Oxygen Content on Metal Oxide Surfaces and Applications to Gas Sensing.

Oxygen vacancies and adsorbed oxygen species on metal oxide surfaces play important roles in various fields. However, existing methods for manipulating surface oxygen require severe settings and are ineffective for repetitive manipulation. We present a method to manipulate the amount of surface oxygen by modifying the oxygen adsorption energy by electrically controlling the electron concentration of the metal oxide. The surface oxygen control ability of the method is verified using first-principles calculations based on density functional theory (DFT), X-ray photoelectron spectroscopy (XPS), and electrical resistance analysis. The presented method is implemented by fabricating oxide thin film transistors with embedded microheaters. The method can reconfigure the oxygen vacancies on the In2O3, SnO2, and IGZO surfaces so that specific chemisorption dominates. The method can selectively increase oxidizing (e.g., NO and NO) and reducing gas (e.g., H2S, NH3, and CO) reactions by electrically controlling the metal oxide surface to be oxygen vacancy-rich or adsorbed oxygen species-rich. The proposed method is applied to gas sensors and overcomes their existing limitations. The method makes the sensor insensitive to one gas (e.g., H2S) in mixed-gas environments (e.g., NO2+H2S) and provides a linear response (R2 = 0.998) to the target gas (e.g., NO2) concentration within 3 s. We believe that the proposed method is applicable to applications utilizing metal oxide surfaces.

Investigation of the mechanism of the anomalous Hall effects in Cr2Te3/(BiSb)2(TeSe)3 heterostructure.

The interplay between ferromagnetism and the non-trivial topology has unveiled intriguing phases in the transport of charges and spins. For example, it is consistently observed the so-called topological Hall effect (THE) featuring a hump structure in the curve of the Hall resistance (Rxy) vs. a magnetic field (H) of a heterostructure consisting of a ferromagnet (FM) and a topological insulator (TI). The origin of the hump structure is still controversial between the topological Hall effect model and the multi-component anomalous Hall effect (AHE) model. In this work, we have investigated a heterostructure consisting of BixSb2-xTeySe3-y (BSTS) and Cr2Te3 (CT), which are well-known TI and two-dimensional FM, respectively. By using the so-called "minor-loop measurement", we have found that the hump structure observed in the CT/BSTS is more likely to originate from two AHE channels. Moreover, by analyzing the scaling behavior of each amplitude of two AHE with the longitudinal resistivities of CT and BSTS, we have found that one AHE is attributed to the extrinsic contribution of CT while the other is due to the intrinsic contribution of BSTS. It implies that the proximity-induced ferromagnetic layer inside BSTS serves as a source of the intrinsic AHE, resulting in the hump structure explained by the two AHE model.

In-Memory-Computed Low-Frequency Noise Spectroscopy for Selective Gas Detection Using a Reducible Metal Oxide.

Concerns about indoor and outdoor air quality, industrial gas leaks, and medical diagnostics are driving the demand for high-performance gas sensors. Owing to their structural variety and large surface area, reducible metal oxides hold great promise for constructing a gas-sensing system. While many earlier reports have successfully obtained a sufficient response to various types of target gases, the selective detection of target gases remains challenging. In this work, a novel method, low-frequency noise (LFN) spectroscopy is presented, to achieve selective detection using a single FET-type gas sensor. The LFN of the sensor is accurately modeled by considering the charge fluctuation in both the sensing material and the FET channel. Exposure to different target gases produces distinct corner frequencies of the power spectral density that can be used to achieve selective detection. In addition, a 3D vertical-NAND flash array is used with the fast Fourier transform method via in-memory-computing, significantly improving the area and power efficiency rate. The proposed system provides a novel and efficient method capable of selectively detecting a target gas using in-memory-computed LFN spectroscopy and thus paving the way for the further development in gas sensing systems.

Microscopic origin of universal quasilinear band structures of transparent conducting oxides.

A tight-binding-based microscopic theory is developed that accounts for quasilinear conduction bands appearing commonly in transparent conducting oxides. It is found that the interaction between oxygen p and metal s orbtials plays a critical role in determining the band structure around the conduction-band minimum. Under certain types of short-range orders, the tight-binding model universally leads to a dispersion relation which corresponds to that of the massive Dirac particle. The impact of the graphenelike band structure is demonstrated by evaluating the electron mobility of highly doped n-type ZnO.

Electronic bipolar resistance switching in an anti-serially connected Pt/TiO2/Pt structure for improved reliability.

Electronic bipolar resistive switching and its degradation in the Pt/TiO2/Pt structure were studied. The electronic bipolar switching was induced from the asymmetric trap distribution of the structure under its unipolar reset state. The imbalanced migration of oxygen accompanied by electronic switching significantly degrades switching endurance. Instead, the anti-serial connection of Pt/TiO2/Pt cells resulted in substantial improvements in endurance, underscoring the importance of vacancy migration in device reliability. In addition, the independent control of resistance states of the two connected cells provides the freedom to control resistance ratio, switching direction, and reliability.

First-Principles Calculations of Luminescence Spectra of Real-Scale Quantum Dots.

The luminescence line shape is an important feature of semiconductor quantum dots (QDs) and affects performance in various optical applications. Here, we report a first-principles method to predict the luminescence spectrum of thousands of atom QDs. In our approach, neural network potential calculations are combined with density functional theory calculations to describe exciton-phonon coupling (EPC). Using the calculated EPC, the luminescence spectrum is evaluated within the Franck-Condon approximation. Our approach results in the luminescence line shape for an InP/ZnSe core/shell QD (3406 atoms) that exhibits excellent agreement with the experiments. From a detailed analysis of EPC, we reveal that the coupling of both acoustic and optical phonons to an exciton are important in determining the spectral line shapes of core/shell QDs, which is in contrast with previous studies. On the basis of the present simulation results, we provide guidelines for designing high-performance core/shell QDs with ultrasharp emission spectra.