Explore the vast range of titles available.

We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The resulting success rate of 9.72% is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials, and comparable with speed-up factors of machine learning filtering procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our approach, that can be used as a filter on top of any high-throughput scheme, enables us to efficiently extract stable compounds from tremendously large initial sets, without any initial assumption on their crystal structures or chemical compositions.

In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for increased accuracy in the calculations. Here, we present a dataset of calculations for 175k crystalline materials obtained with two functionals: geometry optimizations are performed with PBE for solids (PBEsol) that yields consistently better geometries than the PBE functional, and energies are obtained from PBEsol and from SCAN single-point calculations at the PBEsol geometry. Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBEsol, and SCAN. Measurement(s) optimized geometry (PBESol) • total energy (PBESol, Scan) • bandgap (PBESol, Scan) Technology Type(s) Density functional theory (VASP) Factor Type(s) Exchange correlation functional • Crystal structure

Background The molecular characterization of carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) isolates is not well studied. Our goal was to investigate the molecular epidemiology of CR-hvKP strains that were isolated from a Chinese hospital. Results All clinical carbapenem-resistant K. pneumoniae (CR-KP) isolates were collected and identified from patient samples between 2014 and 2017 from a Chinese hospital. The samples were subjected to screening for CR-hvKP by string test and the detection of the aerobactin gene. CR-hvKP isolates were further confirmed through neutrophil phagocytosis and a mice lethality assay. The CR-hvKP isolates were investigated for their capsular genotyping, virulence gene profiles, and the expression of carbapenemase genes by PCR and DNA sequencing. Multilocus sequence type (MLST) and pulsed-field gel electrophoresis (PFGE) were performed to exclude the homology of these isolates. Twenty strains were identified as CR-hvKP. These strains were resistant to imipenem and several other antibiotics, however, most were susceptible to amikacin. Notably, two isolates were not susceptible to tigecycline. Capsular polysaccharide synthesis genotyping revealed that 17 of the 20 CR-hvKP strains belonged to the K2 serotype, while the others belonged to serotypes other than K1, K2, K5, K20, and K57. The strains were found to be positive for 10 types of virulence genes and a variety of these genes coexisted in the same strain. Two carbapenemase genes were identified: bla KPC-2 (13/20) and bla NDM-1 (1/20). PFGE typing revealed eight clusters comprising isolates that belonged to MLST types ST25, ST11 and ST375, respectively. PFGE cluster A was identified as the main cluster, which included 11 isolates that belong to ST25 and mainly from ICU department. Conclusions Our findings suggest that hospital-acquired infections may contribute in part to the CR-hvKP strains identified in this study. It also suggests that ST25 CR-hvKP strain has a clonal distribution in our hospital. Therefore, effective surveillance and strict infection control strategies should be implemented to prevent outbreak by CR-hvKP strains in hospitals setting.

There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential. This progress has led to increasingly accurate models for predicting energy, forces, and stresses, combining innovative architectures with big data. Here, we benchmark these models on their ability to predict harmonic phonon properties, which are critical for understanding the vibrational and thermal behavior of materials. Using around 10 000 ab initio phonon calculations, we evaluate model performance across various phonon-related parameters to test the universal applicability of these models. The results reveal that some models achieve high accuracy in predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies, even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium. These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.

Background Acinetobacter baumannii has traditionally been considered an opportunistic pathogen with low virulence. In this study, we characterized the carbapenem-resistant hypervirulent A. baumannii (CR-hvAB) stains isolated from our hospital in mid-south region of China. Results Blood samples collected between January 2017 and May 2019 were used for virulence experiments and biofilm assays of individual carbapenem-resistant A. baumannii (CR-AB) strains, performed using a Galleria mellonella infection model and crystal violet staining method, respectively. CR-AB isolates that induced high mortality in the G. mellonella infection model were subjected to genotyping, susceptibility testing, and clinical data analysis, and the genetic characterization of these isolates was performed by whole-genome sequencing (WGS). Among the 109 CR-AB clinical strains, the survival rate of G. mellonella larvae infected with 7 (6.4%) CR-AB isolates (number of strains with mortality of 0, 10 and 20% was 4, 1, and 2, respectively), was significantly lower than that of A. baumannii ATCC 19606 (100.0%) and the remaining CR-AB isolates (> 80.0%). Consistent with these results, patients infected with these seven isolates had an average 7-day mortality rate of 42.9%, suggesting that the isolates were CR-hvAB. These seven isolates belonged to four sequence types (STs): ST457, ST195, ST369, and ST2088 (a new ST), and mainly ST457 ( n  = 4). The results of the biofilm study showed that eight strains had powerful biofilm ability (strong [ n  = 1] and moderate [ n  = 7] biofilm producers) including these seven CR-hvAB isolates. Conclusions CR-hvAB isolates that induced a high mortality rate were cloned in our hospital, most of which belonged to ST457; thus, monitoring of these strains, particularly ST457, should be strengthened in the future. Meanwhile, A. baumannii , which was isolated from blood specimens and found to powerful biofilm-forming ability, is a probable hvAB isolate.

This study presents a computational investigation of X4H15 compounds (where X represents a metal) as potential superconductors at ambient conditions or under pressure. Through systematic density functional theory calculations and electron–phonon coupling analysis, it is demonstrated that electronic structure engineering via hole doping dramatically enhances the superconducting properties of these materials. While electron‐doped compounds with X4 + cations (Ti, Zr, Hf, Th) exhibit modest transition temperatures of 1–9 K, hole‐doped systems with X3 + cations (Y, Tb, Dy, Ho, Er, Tm, Lu) show remarkably higher values of ≈50 K at ambient pressure. Superconductivity in hole‐doped compounds originates from stronger coupling between electrons and both cation and hydrogen phonon modes. Although pristine X3 +4H15 compounds are thermodynamically unstable, a viable synthesis route via controlled hole doping of the charge‐compensated YZr3H15 compound is proposed. The calculations predict that even minimal concentrations of excess Y can induce high‐temperature superconductivity while preserving structural integrity. This work reveals how strategic electronic structure modulation can optimize superconducting properties in hydride systems, establishing a promising pathway toward practical high‐temperature conventional superconductors at ambient pressure. Hole doping in X4H15 hydrides significantly enhances superconductivity, with X3 + compounds reaching Tc values near 50 K due to strong electron–phonon coupling. A viable route is proposed by doping thermodynamically unstable X43+H ${\\rm X}^{3+}_4{\\rm H}$ 15 to control the charge‐compensated YZr3H15 compound. This work demonstrates how electronic structure modulation can enable high‐Tc superconductivity under ambient pressure.

Enhancer of zeste homolog 2 (EZH2) is a histone methyltransferase involved in cell proliferation, invasion, angiogenesis, and metastasis in various cancers, including hepatocellular carcinoma (HCC). However, the role and molecular mechanisms of EZH2 in HCC radiosensitivity remain unclear. Here, we show that EZH2 is upregulated in HCC cells and the aberrantly overexpressed EZH2 is associated with the poor prognosis of HCC patients. Using miRNA databases, we identified miR-138-5p as a regulator of EZH2. We also found that miR-138-5p was suppressed by EZH2-induced H3K27me3 in HCC cell lines. MiR-138-5p overexpression and EZH2 knockdown enhanced cellular radiosensitivity while inhibiting cell migration, invasion, and epithelial-mesenchymal transition (EMT). Analysis of RNA-seq datasets revealed that the hypoxia-inducible factor-1 (HIF-1) signaling pathway was the main enrichment pathway for differential genes after miR-138-5p overexpression or EZH2 knockdown. Expression level of HIF-1α was significantly suppressed after miR-138-5p overexpression or silencing of EZH2. HIF-1α silencing mitigated resistance of HCC cells and inhibited EMT. This study establishes the EZH2/miR-138-5p/HIF-1α as a potential therapeutic target for sensitizing HCC to radiotherapy.

One of the major challenges in the development of universal machine learning interatomic potentials is accurately reproducing phonon properties. This issue appears to arise from the limitations of available datasets rather than the models themselves. To address this, we develop an extensive dataset of phonon calculations using density-functional perturbation theory (DFPT). We then show how this dataset can be used to train neural-network force fields, by implementing the training and the prediction of force constants in periodic crystals. This approach improves the quality of phonon properties prediction while reducing the number of structures needed for neural network training. We demonstrate the efficiency of this method using two examples of ternary phase diagrams: Ti–Nb–Ta and Li–B–C. In both cases, neural network predictions for the energy and forces show a considerable improvement, while phonon properties are predicted with high precision for all structures across the entire phase diagrams.

Garnets have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc. The overwhelming majority of experimentally known garnets are oxides, while explorations (experimental or theoretical) for the rest of the chemical space have been limited in scope. A key issue is that the garnet structure has a large primitive unit cell, requiring a substantial amount of computational resources. To perform a comprehensive search of the complete chemical space for new garnets, we combine recent progress in graph neural networks with high-throughput calculations. We apply the machine learning model to identify the potentially (meta-)stable garnet systems before performing systematic density-functional calculations to validate the predictions. We discover more than 600 ternary garnets with distances to the convex hull below 100 meV ⋅ atom −1 . This includes sulfide, nitride, and halide garnets. We analyze their electronic structure and discuss the connection between the value of the electronic band gap and charge balance.

Background： Klebsiellapneumoniae carbapenemase （KPC）-producing K. pneumoniae bacteria, which cause serious disease outbreaks worldwide, was rarely detected in Xiangya Hospital, prior to an outbreak that occurred from August 4, 2014, to March 17, 2015. The aim of this study was to analyze the epidemiology and molecular characteristics of the K. pneumoniae strains isolated during the outbreak. Methods： Nonduplicate carbapenem-resistant K. pneumoniae isolates were screened for blanc,＂ and multiple other resistance determinants using polymerase chain reaction. Subsequent studies included pulsed-field gel electrophoresis （PFGE）, multilocus sequence typing, analysis of plasmids, and genetic organization ofblaKPC-2 locus. Results： Seventeen blaKPC-2-positive K. pneumoniae were identified. A wide range of resistant determinants was detected. Most isolates （88.2%） coharbored blaKPC-2 and rmtB in addition to other resistance genes, including biaSHV-1, blaTEM-1, and aac（3）-lla. The blaKPC-2 and rmtB genes were located on the conjugative IncFIB-type plasmid. Genetic organization of blaKPC-2 locus in most strains was consistent with that of the plasmid pKP048. Four types （A l, A2, A3, and B） were detected by PFGE, and Type A1, an ST11, was the predominant PFGE type. A novel K. pneumoniae sequence type （ST1883） related to STI 1 was discovered. Conclusions： These isolates in our study appeared to be clonal and STI 1 K. pneumoniae was the predominant clone attributed to the outbreak. Coharbing of blaKPC-2 and rmtB, which were located on a transferable plasmid, in clinical K. pneumoniae isolates may lead to the emergence of a new pattern of drug resistance.