The ONETEP linear-scaling density functional theory program.

We present an overview of the onetep program for linear-scaling density functional theory (DFT) calculations with large basis set (plane-wave) accuracy on parallel computers. The DFT energy is computed from the density matrix, which is constructed from spatially localized orbitals we call Non-orthogonal Generalized Wannier Functions (NGWFs), expressed in terms of periodic sinc (psinc) functions. During the calculation, both the density matrix and the NGWFs are optimized with localization constraints. By taking advantage of localization, onetep is able to perform calculations including thousands of atoms with computational effort, which scales linearly with the number or atoms. The code has a large and diverse range of capabilities, explored in this paper, including different boundary conditions, various exchange-correlation functionals (with and without exact exchange), finite electronic temperature methods for metallic systems, methods for strongly correlated systems, molecular dynamics, vibrational calculations, time-dependent DFT, electronic transport, core loss spectroscopy, implicit solvation, quantum mechanical (QM)/molecular mechanical and QM-in-QM embedding, density of states calculations, distributed multipole analysis, and methods for partitioning charges and interactions between fragments. Calculations with onetep provide unique insights into large and complex systems that require an accurate atomic-level description, ranging from biomolecular to chemical, to materials, and to physical problems, as we show with a small selection of illustrative examples. onetep has always aimed to be at the cutting edge of method and software developments, and it serves as a platform for developing new methods of electronic structure simulation. We therefore conclude by describing some of the challenges and directions for its future developments and applications.

Visual-motor association learning in undergraduate students as a function of the autism-spectrum quotient.

We examined how performance on an associative learning task changes in a sample of undergraduate students as a function of their autism-spectrum quotient (AQ) score. The participants, without any prior knowledge of the Japanese language, learned to associate hiragana characters with button responses. In the novel condition, 50 participants learned visual-motor associations without any prior exposure to the stimuli's visual attributes. In the familiar condition, a different set of 50 participants completed a session in which they first became familiar with the stimuli's visual appearance prior to completing the visual-motor association learning task. Participants with higher AQ scores had a clear advantage in the novel condition; the amount of training required reaching learning criterion correlated negatively with AQ. In contrast, participants with lower AQ scores had a clear advantage in the familiar condition; the amount of training required to reach learning criterion correlated positively with AQ. An examination of how each of the AQ subscales correlated with these learning patterns revealed that abilities in visual discrimination-which is known to depend on the visual ventral-stream system-may have afforded an advantage in the novel condition for the participants with the higher AQ scores, whereas abilities in attention switching-which are known to require mechanisms in the prefrontal cortex-may have afforded an advantage in the familiar condition for the participants with the lower AQ scores.