Structural and Correlation Effects in the Itinerant Insulating Antiferromagnetic Perovskite NaOsO3

The orthorhombic perovskite NaOsO3 undergoes a continuous metal-insulator transition (MIT), accompanied by antiferromagnetic (AFM) order at T_N=410 K, suggested to be an example of the rare Slater (itinerant) MIT. We study this system using ab initio and related methods, focusing on the origin and nature of magnetic ordering and the MIT. The rotation and tilting of OsO6 octahedra in the GdFeO3 structure result in moderate narrowing the band width of the t_{2g} manifold, but sufficient to induce flattening of bands and AFM order within the local spin density approximation (LSDA), where it remains metallic but with a deep pseudogap. Including on-site Coulomb repulsion U, at U_c ~2 eV a MIT occurs only in the AFM state. Effects of spin-orbit coupling (SOC) on the band structure seem minor as expected for a half-filled $t_{2g}^{3}$ shell, but SOC doubles the critical value U_c necessary to open a gap and also leads to large magnetocrystalline energy differences in spite of normal orbital moments no greater than 0.1$\mu_B$. Our results are consistent with a Slater MIT driven by magnetic order, induced by a combination of structurally-induced band narrowing and moderate Coulomb repulsion, with SOC necessary for a full picture. Strong p-d hybridization reduces the moment, and when bootstrapped by the reduced Hund's rule coupling (proportional to the moment) gives a calculated moment of ~1 $\mu_B$, consistent with the observed moment and only a third of the formal $d^3$ value. We raise and discuss one important question: since this AFM ordering is at q=0 (in the 20 atom cell) where nesting is a moot issue, what is the microscopic driving force for ordering and the accompanying MIT?Comment: 9 page

Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data

Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients – basic parameters required for implementing the MIIA – are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite

Reply to "Comment on `First-principles calculation of the superconducting transition in MgB2 within the anisotropic Eliashberg formalism'"

The recent preprint by Mazin et al. [cond-mat/0212417] contains many inappropriate evaluations and/or criticisms on our published work [Phys. Rev. B 66, 020513 (2002) and Nature 418, 758 (2002)]. The preprint [cond-mat/0212417v1] was submitted to Physical Review B as a comment on one of our papers [Phys. Rev. B 66, 020513 (2002)]. In the reviewing process, Mazin et al. have withdrawn many of the statements contained in cond-mat/0212417v1, however two claims remain in their revised manuscript [cond-mat/0212417v3]: (1) the calculated variations of the superconducting energy gap within the sigma- or the pi-bands are not observable in real samples due to scatterings, and (2) the Coulomb repulsion mu(k,k') is negligibly small between sigma- and pi-states and thus should be approximated by a diagonal 2 x 2 matrix in the sigma and pi channels. Here, we point out that the former does not affect the validity of our theoretical work which is for the clean limit, and that the latter is not correct

Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities

An intriguing aspect in microbial communities is that pairwise interactions can be influenced by neighboring species. This creates context dependencies for microbial interactions that are based on the functional composition of the community. Context dependent interactions are ecologically important and clearly present in nature, yet firmly established theoretical methods are lacking from many modern computational investigations. Here, we propose a novel network inference method that enables predictions for interspecies interactions affected by shifts in community composition and species populations. Our approach first identifies interspecies interactions in binary communities, which is subsequently used as a basis to infer modulation in more complex multi-species communities based on the assumption that microbes minimize adjustments of pairwise interactions in response to neighbor species. We termed this rule-based inference minimal interspecies interaction adjustment (MIIA). Our critical assessment of MIIA has produced reliable predictions of shifting interspecies interactions that are dependent on the functional role of neighbor organisms. We also show how MIIA has been applied to a microbial community composed of competing soil bacteria to elucidate a new finding that – in many cases – adding fewer competitors could impose more significant impact on binary interactions. The ability to predict membership-dependent community behavior is expected to help deepen our understanding of how microbiomes are organized in nature and how they may be designed and/or controlled in the future