An explicit approach to conceptual density functional theory descriptors of arbitrary order

We present explicit formulas for arbitrary-order derivatives of the energy, grand potential, electron density, and higher-order response functions with respect to the number of electrons, and the chemical potential for any smooth and differentiable model of the energy versus the number of electrons. The resulting expressions for global reactivity descriptors (hyperhardnesses and hypersoftnesses), local reactivity descriptors (hyperFukui functions and local hypersoftnesses), and nonlocal response functions are easy to evaluate computationally. Specifically, the explicit formulas for global/local/nonlocal hypersoftnesses of arbitrary order are derived using Bell polynomials. Explicit expressions for global and local hypersoftness indicators up to fifth order are presented. (C) 2016 Elsevier B.V. All rights reservedVanier-CGS fellowship Ghent University CONACYT FONDECYT 114031

The HSAB principle from a finite temperature grand‑canonical perspective

We provide a new proof for Pearson’s hard/ soft acid/base (HSAB) principle. Unlike alternative proofs, we do not presuppose a simplified parabolic dependence on the energy of the system with respect to changes in its number of electrons. Instead, we use the more physically grounded finite-temperature formulation of the grand-canonical ensemble. We show that under the usual assumptions regarding the chemical potentials and hardnesses of the involved species, the HSAB rule holds for a wide range of temperatures.FONDECYT (Grant 1140313), Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia-FB0807, and Project RC-130006 CILIS, Granted by the Fondo de Innovación para la Competitividad del Ministerio de Economía, Fomento y Turismo de Chile

Application of anodal tDCS at primary motor cortex immediately after practice of a motor sequence does not improve offline gain

Tecchio et al. (J Neurophysiology 104: 1134–1140, 2010) reported that the application of anodal tDCS at primary motor cortex (M1) immediately after practice of a procedural motor skill enhanced consolidation, which in turn improved offline gain. Tecchio et al. noted, however, that this study did not account for known after-effects associated with this form of non-invasive stimulation. The present study was designed to explicitly reevaluate Tecchio et al.’s claim. As in the original study, individuals experienced either anodal or sham stimulation at M1 after practice of a serial reaction time task (SRTT) followed by test trials 15-min later. Two additional novel conditions experienced the test trials after 120-min rather than 15-min thus allowing potential stimulation after-effects to dissipate. The expectation was that if anodal stimulation influences post-practice consolidation leading to offline gain, this effect would be present not only at 15-min but also after 120-min. In agreement with the working hypothesis, findings revealed offline gain at both 15-min and the longer 2-h time period. Unexpectedly, we found no interaction between real and sham conditions. The lack of difference between Real and Sham effects weakens confidence in the potential of post-practice tDCS for consolidation enhancement, while it is more consistent with other claims that decoupling practice and anodal tDCS stimulation in time can reduce the effectiveness of exogenous stimulation for procedural skill gain

IOData

IOData is a free and open-source Python library for parsing, storing, and converting various file formats commonly used by quantum chemistry, molecular dynamics, and plane-wave density-functional-theory software programs. In addition, IOData supports a flexible framework for generating input files for various software packages. While designed and released for stand-alone use, its original purpose was to facilitate the interoperability of various modules in the HORTON and ChemTools software packages with external (third-party) molecular quantum chemistry and solid-state density-functional-theory packages. IOData is designed to be easy to use, maintain, and extend; this is why we wrote IOData in Python and adopted many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note of the IOData library

IOData: A python library for reading, writing, and converting computational chemistry file formats and generating input files

IOData is a free and open-source Python library for parsing, storing, and converting various file formats commonly used by quantum chemistry, molecular dynamics, and plane-wave density-functional-theory software programs. In addition, IOData supports a flexible framework for generating input files for various software packages. While designed and released for stand-alone use, its original purpose was to facilitate the interoperability of various modules in the HORTON and ChemTools software packages with external (third-party) molecular quantum chemistry and solid-state density-functional-theory packages. IOData is designed to be easy to use, maintain, and extend; this is why we wrote IOData in Python and adopted many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note of the IOData library