The random phase approximation applied to ice

Standard density functionals without van der Waals interactions yield an unsatisfactory description of ice phases, specifically, high density phases occurring under pressure are too unstable compared to the common low density phase I$_h$ observed at ambient conditions. Although the description is improved by using functionals that include van der Waals interactions, the errors in relative volumes remain sizable. Here we assess the random phase approximation (RPA) for the correlation energy and compare our results to experimental data as well as diffusion Monte Carlo data for ice. The RPA yields a very balanced description for all considered phases, approaching the accuracy of diffusion Monte Carlo in relative energies and volumes. This opens a route towards a concise description of molecular water phases on surfaces and in cavities

Ritz-like values in steplength selections for stochastic gradient methods

The steplength selection is a crucial issue for the effectiveness of the stochastic gradient methods for large-scale optimization problems arising in machine learning. In a recent paper, Bollapragada et al. (SIAM J Optim 28(4):3312–3343, 2018) propose to include an adaptive subsampling strategy into a stochastic gradient scheme, with the aim to assure the descent feature in expectation of the stochastic gradient directions. In this approach, theoretical convergence properties are preserved under the assumption that the positive steplength satisfies at any iteration a suitable bound depending on the inverse of the Lipschitz constant of the objective function gradient. In this paper, we propose to tailor for the stochastic gradient scheme the steplength selection adopted in the full-gradient method knows as limited memory steepest descent method. This strategy, based on the Ritz-like values of a suitable matrix, enables to give a local estimate of the inverse of the local Lipschitz parameter, without introducing line search techniques, while the possible increase in the size of the subsample used to compute the stochastic gradient enables to control the variance of this direction. An extensive numerical experimentation highlights that the new rule makes the tuning of the parameters less expensive than the trial procedure for the efficient selection of a constant step in standard and mini-batch stochastic gradient methods

Screened hybrid functional applied to 3d^0-->3d^8 transition-metal perovskites LaMO3 (M=Sc-Cu): influence of the exchange mixing parameter on the structural, electronic and magnetic properties

We assess the performance of the Heyd-Scuseria-Ernzerhof (HSE) screened hybrid density functional scheme applied to the perovskite family LaMO3 (M=Sc-Cu) and discuss the role of the mixing parameter alpha (which determines the fraction of exact Hartree-Fock exchange included in the density functional theory (DFT) exchange-correlation functional) on the structural, electronic, and magnetic properties. The physical complexity of this class of compounds, manifested by the largely varying electronic characters (band/Mott-Hubbard/charge-transfer insulators and metals), magnetic orderings, structural distortions (cooperative Jahn-Teller like instabilities), as well as by the strong competition between localization/delocalization effects associated with the gradual filling of the t_2g and e_g orbitals, symbolize a critical and challenging case for theory. Our results indicates that HSE is able to provide a consistent picture of the complex physical scenario encountered across the LaMO3 series and significantly improve the standard DFT description. The only exceptions are the correlated paramagnetic metals LaNiO3 and LaCuO3, which are found to be treated better within DFT. By fitting the ground state properties with respect to alpha we have constructed a set of 'optimum' values of alpha from LaScO3 to LaCuO3: it is found that the 'optimum' mixing parameter decreases with increasing filling of the d manifold (LaScO3: 0.25; LaTiO3 & LaVO3: 0.10-0.15; LaCrO3, LaMnO3, and LaFeO3: 0.15; LaCoO3: 0.05; LaNiO3 & LaCuO3: 0). This trend can be nicely correlated with the modulation of the screening and dielectric properties across the LaMO3 series, thus providing a physical justification to the empirical fitting procedure.Comment: 32 pages, 29 figure

Modeling magnetic multipolar phases in density functional theory

Multipolar magnetic phases in correlated insulators represent a great challenge for density functional theory (DFT) due to the coexistence of intermingled interactions, typically spin-orbit coupling, crystal field and com-plex noncollinear and high-rank intersite exchange, creating a complected configurational space with multiple minima. Although the +U correction to DFT allows, in principle, the modeling of such magnetic ground states, its results strongly depend on the initially symmetry breaking, constraining the nature of order parameter in the converged DFT + U solution. As a rule, DFT + U calculations starting from a set of initial on-site magnetic moments result in a conventional dipolar order. A more sophisticated approach is clearly needed in the case of magnetic multipolar ordering, which is revealed by a null integral of the magnetization density over spheres centered on magnetic atoms, but with nonzero local contributions. Here we show how such phases can be efficiently captured using an educated constrained initialization of the on-site density matrix, which is derived from the multipolar-ordered ground state of an ab initio effective Hamiltonian. Various properties of such exotic ground states, like their one-electron spectra, become therefore accessible by all-electron DFT + U methods. We assess the reliability of this procedure on the ferro-octupolar ground state recently predicted in Ba2MOsO6 (M = Ca, Mg, Zn) [Phys. Rev. Lett. 127, 237201 (2021)]

Self-consistent theory of intrinsic localized modes: application to monatomic chain

A theory of intrinsic localized modes (ILMs) in anharmonic lattices is developed, which allows one to reduce the original nonlinear problem to a linear problem of small variations of the mode. This enables us to apply the Lifshitz method of the perturbed phonon dynamics for the calculations of ILMs. In order to check the theory, the ILMs in monatomic chain are considered. A comparison of the results with the corresponding molecular dynamics calculations shows an excellent agreement.Comment: 9 pages, 1 figure, 1 tabl

Two-spin entanglement distribution near factorized states

We study the two-spin entanglement distribution along the infinite $S=1/2$ chain described by the XY model in a transverse field; closed analytical expressions are derived for the one-tangle and the concurrences $C_r$, $r$ being the distance between the two possibly entangled spins, for values of the Hamiltonian parameters close to those corresponding to factorized ground states. The total amount of entanglement, the fraction of such entanglement which is stored in pairwise entanglement, and the way such fraction distributes along the chain is discussed, with attention focused on the dependence on the anisotropy of the exchange interaction. Near factorization a characteristic length-scale naturally emerges in the system, which is specifically related with entanglement properties and diverges at the critical point of the fully isotropic model. In general, we find that anisotropy rule a complex behavior of the entanglement properties, which results in the fact that more isotropic models, despite being characterized by a larger amount of total entanglement, present a smaller fraction of pairwise entanglement: the latter, in turn, is more evenly distributed along the chain, to the extent that, in the fully isotropic model at the critical field, the concurrences do not depend on $r$.Comment: 14 pages, 6 figures. Final versio

Intensidade dos danos do nematoide das lesões radiculares na soja, em função da variabilidade espacial de atributos químicos do solo.

Objetivo deste trabalho: entender melhor o comportamento da espécie em relação à variabilidade de atributos químicos do solo, foi estabelecido, na safra 2010/2011, um estudo de campo na região médio norte do Mato Grosso

A stochastic gradient method with variance control and variable learning rate for Deep Learning

In this paper we study a stochastic gradient algorithm which rules the increase of the minibatch size in a predefined fashion and automatically adjusts the learning rate by means of a monotone or non -monotone line search procedure. The mini -batch size is incremented at a suitable a priori rate throughout the iterative process in order that the variance of the stochastic gradients is progressively reduced. The a priori rate is not subject to restrictive assumptions, allowing for the possibility of a slow increase in the mini -batch size. On the other hand, the learning rate can vary non -monotonically throughout the iterations, as long as it is appropriately bounded. Convergence results for the proposed method are provided for both convex and non -convex objective functions. Moreover it can be proved that the algorithm enjoys a global linear rate of convergence on strongly convex functions. The low per -iteration cost, the limited memory requirements and the robustness against the hyperparameters setting make the suggested approach well -suited for implementation within the deep learning framework, also for GPGPU-equipped architectures. Numerical results on training deep neural networks for multiclass image classification show a promising behaviour of the proposed scheme with respect to similar state of the art competitors