Electronic structure, spin state, and magnetism of the square-lattice Mott insulator La2Co2Se2O3 from first principles

Electronic and magnetic structures of the newly synthesized cobalt oxyselenide La2Co2Se2O3 (structurally similar to the superconducting iron pnictides) are studied through density functional calculations. The obtained results show that this material is a Mott insulator, and that it has a very stable Co2+ high-spin ground state with a t2g-like orbital ordering, which is substantiated by the calculated crystal-field excitation energies. The square lattice of the Co2+ spins is found to have a strong antiferro (a weak ferro) magnetic coupling for the second nearest neighbors (2nn) via O (Se2) and an intermediate antiferro one for the 1nn, with the strength ratio about 10:1:3. The present results account for the available experimental data of magnetism, and the prediction of a planar frustrated (2x2) antiferromagnetic structure would motivate a new experiment.Comment: 4 pages, 3 figures, PRB (Rapid Commun.) in pres

Is N-doped SrO magnetic? A first-principles view

N-doped SrO seems to be one of the model systems for d^0 magnetism, in which magnetism (or ideally, ferromagnetism) was ascribed to the localized N 2p spins mediated by delocalized O 2p holes. Here we offer a different view, using density functional calculations. We find that N-doped SrO with solely substitutional N impurities as widely assumed in the literature is unstable, and instead that a pairing state of substitutional and interstitial N impurities is significantly more stable and has a much lower formation energy than the former by 6.7 eV. The stable (N_{sub}-N_{int})^{2-} dimers behave like a charged (N_2)^{2-} molecule and have each a molecular spin=1. However, their spin-polarized molecular levels lie well inside the wide band gap of SrO and thus the exchange interaction is negligibly weak. As a consequence, N-doped SrO could not be ferromagnetic but paramagnetic.Comment: 7 pages, 2 figures, Appl. Phys. Lett., in pres

A Generic Path Algorithm for Regularized Statistical Estimation

Regularization is widely used in statistics and machine learning to prevent overfitting and gear solution towards prior information. In general, a regularized estimation problem minimizes the sum of a loss function and a penalty term. The penalty term is usually weighted by a tuning parameter and encourages certain constraints on the parameters to be estimated. Particular choices of constraints lead to the popular lasso, fused-lasso, and other generalized $l_1$ penalized regression methods. Although there has been a lot of research in this area, developing efficient optimization methods for many nonseparable penalties remains a challenge. In this article we propose an exact path solver based on ordinary differential equations (EPSODE) that works for any convex loss function and can deal with generalized $l_1$ penalties as well as more complicated regularization such as inequality constraints encountered in shape-restricted regressions and nonparametric density estimation. In the path following process, the solution path hits, exits, and slides along the various constraints and vividly illustrates the tradeoffs between goodness of fit and model parsimony. In practice, the EPSODE can be coupled with AIC, BIC, $C_p$ or cross-validation to select an optimal tuning parameter. Our applications to generalized $l_1$ regularized generalized linear models, shape-restricted regressions, Gaussian graphical models, and nonparametric density estimation showcase the potential of the EPSODE algorithm.Comment: 28 pages, 5 figure