Approaching periodic systems in ensemble density functional theory via finite one-dimensional models

Abstract: Ensemble Density Functional Theory (EDFT) is a generalization of ground-state Density Functional Theory (GS DFT), which is based on an exact formal theory of finite collections of a system's ground and excited states. EDFT in various forms has been shown to improve the accuracy of calculated energy level differences in isolated model systems, atoms, and molecules, but it is not yet clear how EDFT could be used to calculate band gaps for periodic systems. We extend the application of EDFT toward periodic systems by estimating the thermodynamic limit with increasingly large finite one-dimensional "particle in a box" systems, which approach the uniform electron gas (UEG). Using ensemble-generalized Hartree and Local Spin Density Approximation (LSDA) exchange-correlation functionals, we find that corrections go to zero in the infinite limit, as expected for a metallic system. However, there is a correction to the effective mass, with results comparable to other calculations on 1D, 2D, and 3D UEGs, which indicates promise for non-trivial results from EDFT on periodic systems

Excitations and benchmark ensemble density functional theory for two electrons

A new method for extracting ensemble Kohn-Sham potentials from accurate excited state densities is applied to a variety of two electron systems, exploring the behavior of exact ensemble density functional theory. The issue of separating the Hartree energy and the choice of degenerate eigenstates is explored. A new approximation, spin eigenstate Hartree-exchange (SEHX), is derived. Exact conditions that are proven include the signs of the correlation energy components, the virial theorem for both exchange and correlation, and the asymptotic behavior of the potential for small weights of the excited states. Many energy components are given as a function of the weights for two electrons in a one-dimensional flat box, in a box with a large barrier to create charge transfer excitations, in a three-dimensional harmonic well (Hooke's atom), and for the He atom singlet-triplet ensemble, singlet-triplet-singlet ensemble, and triplet bi-ensemble.Comment: 15 pages, supplemental material pd

Warming Up Density Functional Theory

Density functional theory (DFT) has become the most popular approach to electronic structure across disciplines, especially in material and chemical sciences. Last year, at least 30,000 papers used DFT to make useful predictions or give insight into an enormous diversity of scientific problems, ranging from battery development to solar cell efficiency and far beyond. The success of this field has been driven by usefully accurate approximations based on known exact conditions and careful testing and validation. In the last decade, applications of DFT in a new area, warm dense matter, have exploded. DFT is revolutionizing simulations of warm dense matter including applications in controlled fusion, planetary interiors, and other areas of high energy density physics. Over the past decade or so, molecular dynamics calculations driven by modern density functional theory have played a crucial role in bringing chemical realism to these applications, often (but not always) with excellent agreement with experiment. This chapter summarizes recent work from our group on density functional theory at non-zero temperatures, which we call thermal DFT. We explain the relevance of this work in the context of warm dense matter, and the importance of quantum chemistry to this regime. We illustrate many basic concepts on a simple model system, the asymmetric Hubbard dimer

Dissipation and spontaneous symmetry breaking in brain dynamics

We compare the predictions of the dissipative quantum model of brain with neurophysiological data collected from electroencephalograms resulting from high-density arrays fixed on the surfaces of primary sensory and limbic areas of trained rabbits and cats. Functional brain imaging in relation to behavior reveals the formation of coherent domains of synchronized neuronal oscillatory activity and phase transitions predicted by the dissipative model.Comment: Restyled, slight changes in title and abstract, updated bibliography, J. Phys. A: Math. Theor. Vol. 41 (2008) in prin

Synthetic Quantum Systems

So far proposed quantum computers use fragile and environmentally sensitive natural quantum systems. Here we explore the new notion that synthetic quantum systems suitable for quantum computation may be fabricated from smart nanostructures using topological excitations of a stochastic neural-type network that can mimic natural quantum systems. These developments are a technological application of process physics which is an information theory of reality in which space and quantum phenomena are emergent, and so indicates the deep origins of quantum phenomena. Analogous complex stochastic dynamical systems have recently been proposed within neurobiology to deal with the emergent complexity of biosystems, particularly the biodynamics of higher brain function. The reasons for analogous discoveries in fundamental physics and neurobiology are discussed.Comment: 16 pages, Latex, 1 eps figure fil

P300 and uncertainty reduction in a concept identification task.

The relationship between the amplitude of P300, the mean amplitude of the Slow Wave, and uncertainty reduction after (dis)confirmation of hypotheses was studied in a Concept-Identification task. The subjects had to categorize stimuli according to a conceptual rule (joint denial or exclusion) and to rate the confidence that their classification was correct. Three types of feedback were distinguished: confirming (subject's categorization was correct), disconfirming (subject's categorization was incorrect), and non-informative feedback. The EEG was averaged separately according to the three types of feedback and the two confidence ratings (low, high). The data showed the predicted interaction between type of feedback and confidence level. A larger P300 amplitude turned up after confirming feedback when the subject was less confident, than when he was more confident. The reverse was found after disconfirming feedback. The P300 amplitude after non-informative feedback was not influenced by confidence. The mean amplitude of the Slow Wave showed approximately the same interaction pattern. The results were interpreted in terms of changes in the probability of hypotheses which subjects use to categorize stimuli in a Concept-Identification task

Evolution in the Brain, Evolution in the Mind: The Hierarchical Brain and the Interface between Psychoanalysis and Neuroscience

This article first aims to demonstrate the different ways the work of the English neurologist John Hughlings Jackson influenced Freud. It argues that these can be summarized in six points. It is further argued that the framework proposed by Jackson continued to be pursued by twentieth-century neuroscientists such as Papez, MacLean and Panksepp in terms of tripartite hierarchical evolutionary models. Finally, the account presented here aims to shed light on the analogies encountered by psychodynamically oriented neuroscientists, between contemporary accounts of the anatomy and physiology of the nervous system on the one hand, and Freudian models of the mind on the other. These parallels, I will suggest, are not coincidental. They have a historical underpinning, as both accounts most likely originate from a common source: John Hughlings Jackson's tripartite evolutionary hierarchical view of the brain