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The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the HubbardUandJparameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore theUandJparameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largestUcorrection. PBE has the smallest standard deviation and itsUandJparameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.

 

Multiferroics are a unique class of materials where magnetic and ferroelectric orders coexist. The research on multiferroics contributes significantly to the fundamental understanding of the strong correlations between different material degrees of freedom and provides an energy‐efficient route toward the electrical control of magnetism. While multiple ABO₃oxide perovskites are identified as being multiferroic, their magnetoelectric coupling strength is often weak, necessitating the material search in different compounds. Here, the observation of room‐temperature multiferroic orders in multi‐anion SrNbO_3−xN_xthin films is reported. In these samples, the multi‐anion state enables the room‐temperature ferromagnetic ordering of the Nb d‐electrons. Simultaneously, ferroelectric responses that originate from the structural symmetry breaking associated are found with both the off‐center displacements of Nb and the geometric displacements of Sr, depending on the relative O‐N arrangements within the Nb‐centered octahedra. The findings not only diversify the available multiferroic material pool but also demonstrate a new multiferroism design strategy via multi‐anion engineering.

 

We search NANOGrav’s 12.5 yr data set for evidence of a gravitational-wave background (GWB) with all the spatial correlations allowed by general metric theories of gravity. We find no substantial evidence in favor of the existence of such correlations in our data. We find that scalar-transverse (ST) correlations yield signal-to-noise ratios and Bayes factors that are higher than quadrupolar (tensor-transverse, TT) correlations. Specifically, we find ST correlations with a signal-to-noise ratio of 2.8 that are preferred over TT correlations (Hellings and Downs correlations) with Bayesian odds of about 20:1. However, the significance of ST correlations is reduced dramatically when we include modeling of the solar system ephemeris systematics and/or remove pulsar J0030+0451 entirely from consideration. Even taking the nominal signal-to-noise ratios at face value, analyses of simulated data sets show that such values are not extremely unlikely to be observed in cases where only the usual TT modes are present in the GWB. In the absence of a detection of any polarization mode of gravity, we place upper limits on their amplitudes for a spectral index ofγ= 5 and a reference frequency off_yr= 1 yr⁻¹. Among the upper limits for eight general families of metric theories of gravity, we find the values of$A_{\mathrm{TT}}^{95\%}=(9.7\pm 0.4)\times {10}^{-16}$and$A_{\mathrm{ST}}^{95\%}=(1.4\pm 0.03)\times {10}^{-15}$for the family of metric spacetime theories that contain both TT and ST modes.

 

Abstract We present an analysis of a densely repeating sample of bursts from the first repeating fast radio burst, FRB 121102. We reanalyzed the data used by Gourdji et al. and detected 93 additional bursts using our single-pulse search pipeline. In total, we detected 133 bursts in three hours of data at a center frequency of 1.4 GHz using the Arecibo telescope, and develop robust modeling strategies to constrain the spectro-temporal properties of all of the bursts in the sample. Most of the burst profiles show a scattering tail, and burst spectra are well modeled by a Gaussian with a median width of 230 MHz. We find a lack of emission below 1300 MHz, consistent with previous studies of FRB 121102. We also find that the peak of the log-normal distribution of wait times decreases from 207 to 75 s using our larger sample of bursts, as compared to that of Gourdji et al. Our observations do not favor either Poissonian or Weibull distributions for the burst rate distribution. We searched for periodicity in the bursts using multiple techniques, but did not detect any significant period. The cumulative burst energy distribution exhibits a broken power-law shape, with the lower- and higher-energy slopes of −0.4 ± 0.1 and −1.8 ± 0.2, with the break at (2.3 ± 0.2) × 10 37 erg. We provide our burst fitting routines as a Python package burstfit 4 4 https://github.com/thepetabyteproject/burstfit that can be used to model the spectrogram of any complex fast radio burst or pulsar pulse using robust fitting techniques. All of the other analysis scripts and results are publicly available. 5 5 https://github.com/thepetabyteproject/FRB121102 

We introduce NRPyElliptic, an elliptic solver for numerical relativity (NR) built within the NRPy+ framework. As its first application, NRPyElliptic sets up conformally flat, binary black hole (BBH) puncture initial data (ID) on a single numerical domain, similar to the widely used TwoPunctures code. Unlike TwoPunctures, NRPyElliptic employs a hyperbolic relaxation scheme, whereby arbitrary elliptic PDEs are trivially transformed into a hyperbolic system of PDEs. As consumers of NR ID generally already possess expertise in solving hyperbolic PDEs, they will generally find NRPyElliptic easier to tweak and extend than other NR elliptic solvers. When evolved forward in (pseudo)time, the hyperbolic system exponentially reaches a steady state that solves the elliptic PDEs. Notably NRPyElliptic accelerates the relaxation waves, which makes it many orders of magnitude faster than the usual constant-wavespeed approach. While it is still ∼12x slower than TwoPunctures at setting up full-3D BBH ID, NRPyElliptic requires only ≈0.3% of the runtime for a full BBH simulation in the Einstein Toolkit. Future work will focus on improving performance and generating other types of ID, such as binary neutron star. 

Abstract The invasive Japanese stiltgrass (Microstegium vimineum) affects a wide range of ecosystems and threatens biodiversity across the eastern USA. However, the mechanisms underlying rapid adaptation, plasticity, and epigenetics in the invasive range are largely unknown. We present a chromosome-level assembly for M. vimineum to investigate genome dynamics, evolution, adaptation, and the genomics of phenotypic plasticity. We generated a 1.12-Gb genome with scaffold N50 length of 53.44 Mb respectively, taking a de novo assembly approach that combined PacBio and Dovetail Genomics Omni-C sequencing. The assembly contains 23 pseudochromosomes, representing 99.96% of the genome. BUSCO assessment indicated that 80.3% of Poales gene groups are present in the assembly. The genome is predicted to contain 39,604 protein-coding genes, of which 26,288 are functionally annotated. Furthermore, 66.68% of the genome is repetitive, of which unclassified (35.63%) and long-terminal repeat (LTR) retrotransposons (26.90%) are predominant. Similar to other grasses, Gypsy (41.07%) and Copia (32%) are the most abundant LTR-retrotransposon families. The majority of LTR-retrotransposons are derived from a significant expansion in the past 1–2 Myr, suggesting the presence of relatively young LTR-retrotransposon lineages. We find corroborating evidence from Ks plots for a stiltgrass-specific duplication event, distinct from the more ancient grass-specific duplication event. The assembly and annotation of M. vimineum will serve as an essential genomic resource facilitating studies of the invasion process, the history and consequences of polyploidy in grasses, and provides a crucial tool for natural resource managers.