364 research outputs found
Minimal Basis Iterative Stockholder: Atoms in Molecules for Force-Field Development
Atomic partial charges appear in the Coulomb term of many force-field models
and can be derived from electronic structure calculations with a myriad of
atoms-in-molecules (AIM) methods. More advanced models have also been proposed,
using the distributed nature of the electron cloud and atomic multipoles. In
this work, an electrostatic force field is defined through a concise
approximation of the electron density, for which the Coulomb interaction is
trivially evaluated. This approximate "pro-density" is expanded in a minimal
basis of atom-centered s-type Slater density functions, whose parameters are
optimized by minimizing the Kullback-Leibler divergence of the pro-density from
a reference electron density, e.g. obtained from an electronic structure
calculation. The proposed method, Minimal Basis Iterative Stockholder (MBIS),
is a variant of the Hirshfeld AIM method but it can also be used as a
density-fitting technique. An iterative algorithm to refine the pro-density is
easily implemented with a linear-scaling computational cost, enabling
applications to supramolecular systems. The benefits of the MBIS method are
demonstrated with systematic applications to molecular databases and extended
models of condensed phases. A comparison to 14 other AIM methods shows its
effectiveness when modeling electrostatic interactions. MBIS is also suitable
for rescaling atomic polarizabilities in the Tkatchenko-Sheffler scheme for
dispersion interactions.Comment: 61 pages, 12 figures, 2 table
Pseudo-symmetry curvature conditions on hypersurfaces of Euclidean spaces and on Kahlerian manifolds
Ideally embedded space-times
Due to the growing interest in embeddings of space-time in higher-dimensional
spaces we consider a specific type of embedding. After proving an inequality
between intrinsically defined curvature invariants and the squared mean
curvature, we extend the notion of ideal embeddings from Riemannian geometry to
the indefinite case. Ideal embeddings are such that the embedded manifold
receives the least amount of tension from the surrounding space. Then it is
shown that the de Sitter spaces, a Robertson-Walker space-time and some
anisotropic perfect fluid metrics can be ideally embedded in a five-dimensional
pseudo-Euclidean space.Comment: layout changed and typos corrected; uses revtex
The ReaxFF reactive force-field : development, applications and future directions
The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method
Accurately Determining the Phase Transition Temperature of CsPbI3 via Random-Phase Approximation Calculations and Phase-Transferable Machine Learning Potentials
While metal halide perovskites (MHPs) have shown great potential for various optoelectronic applications, their widespread adoption in commercial photovoltaic cells or photo-sensors is currently restricted, given that MHPs such as CsPbI3 and FAPbI(3) spontaneously transition to an optically inactive non-perovskite phase at ambient conditions. Herein, we put forward an accurate first-principles procedure to obtain fundamental insight into this phase stability conundrum. To this end, we computationally predict the Helmholtz free energy, composed of the electronic ground state energy and thermal corrections, as this is the fundamental quantity describing the phase stability in polymorphic materials. By adopting the random phase approximation method as a wave function-based method that intrinsically accounts for many-body electron correlation effects as a benchmark for the ground state energy, we validate the performance of different exchange-correlation functionals and dispersion methods. The thermal corrections, accessed through the vibrational density of states, are accessed through molecular dynamics simulations, using a phase-transferable machine learning potential to accurately account for the MHPs' anharmonicity and mitigate size effects. The here proposed procedure is critically validated on CsPbI3, which is a challenging material as its phase stability changes slowly with varying temperature. We demonstrate that our procedure is essential to reproduce the experimental transition temperature, as choosing an inadequate functional can easily miss the transition temperature by more than 100 K. These results demonstrate that the here validated methodology is ideally suited to understand how factors such as strain engineering, surface functionalization, or compositional engineering could help to phase-stabilize MHPs for targeted applications
Sustained synchronized neuronal network activity in a human astrocyte co-culture system
Impaired neuronal network function is a hallmark of neurodevelopmental and neurodegenerative disorders such as autism, schizophrenia, and Alzheimer's disease and is typically studied using genetically modified cellular and animal models. Weak predictive capacity and poor translational value of these models urge for better human derived in vitro models. The implementation of human induced pluripotent stem cells (hiPSCs) allows studying pathologies in differentiated disease-relevant and patient-derived neuronal cells. However, the differentiation process and growth conditions of hiPSC-derived neurons are non-trivial. In order to study neuronal network formation and (mal) function in a fully humanized system, we have established an in vitro co-culture model of hiPSC-derived cortical neurons and human primary astrocytes that recapitulates neuronal network synchronization and connectivity within three to four weeks after final plating. Live cell calcium imaging, electrophysiology and high content image analyses revealed an increased maturation of network functionality and synchronicity over time for co-cultures compared to neuronal monocultures. The cells express GABAergic and glutamatergic markers and respond to inhibitors of both neurotransmitter pathways in a functional assay. The combination of this co-culture model with quantitative imaging of network morphofunction is amenable to high throughput screening for lead discovery and drug optimization for neurological diseases
A Computational Model of Visual Anisotropy
Visual anisotropy has been demonstrated in multiple tasks where performance differs between vertical, horizontal, and oblique orientations of the stimuli. We explain some principles of visual anisotropy by anisotropic smoothing, which is based on a variation on Koenderink's approach in [1]. We tested the theory by presenting Gaussian elongated luminance profiles and measuring the perceived orientations by means of an adjustment task. Our framework is based on the smoothing of the image with elliptical Gaussian kernels and it correctly predicted an illusory orientation bias towards the vertical axis. We discuss the scope of the theory in the context of other anisotropies in perception
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