132 research outputs found
Energetics of the AK13 Semi-Local Kohn-Sham Exchange Energy Functional
The recent non-empirical semi-local exchange functional of Armiento and
K\"ummel, the AK13 [PRL 111, 036402 (2013)] incorporates a number of features
reproduced by higher-order theory. The AK13 potential behaves analogously with
the discontinuous jump associated with the derivative discontinuity at integer
particle numbers. Recent works have established that AK13 gives a qualitatively
improved orbital description compared to other semi-local methods, and
reproduces a band structure closer to higher-order theory. However, its
energies and energetics are inaccurate. The present work further investigates
the deficiency in energetics. In addition to AK13 results, we find that
applying the local-density approximation (LDA) non-self-consistently on the
converged AK13 density gives very reasonable energetics with equilibrium
lattice constants and bulk moduli well described across 14 systems. We also
confirm that the attractive orbital features of AK13 are retained even after
full structural relaxation. Hence, the deficient energetics cannot be a result
of the AK13 orbitals having adversely affected the quality of the electron
density compared to that of usual semi-local functionals; an improved orbital
description and good energetics are not in opposition. We also prove that the
non-self-consistent scheme is equivalent to using a single external-potential
dependent functional in an otherwise consistent KS-DFT scheme. Furthermore, our
results also demonstrate that, while an internally consistent KS functional is
presently missing, non-self-consistent LDA on AK13 orbitals works as a
practical non-empirical computational scheme to predict geometries, bulk
moduli, while retaining the band structure features of AK13 at the
computational cost of semi-local DFT.Comment: 7 pages, 4 figure
Quantum oscillations in the kinetic energy density: Gradient corrections from the Airy gas
We derive a closed form expression for the quantum corrections to the kinetic
energy density (KED) in the Thomas-Fermi (TF) limit of a linear potential model
system in three dimensions (the Airy gas). The universality of the expression
is tested numerically in a number of three dimensional model systems: (i)
jellium surfaces, (ii) hydrogen-like potentials, (iii) systems confined by an
harmonic potential in one and (iv) all three dimensions, and (v) a system with
a cosine potential (the Mathieu gas). Our results confirm that the usual
gradient expansion of extended Thomas-Fermi theory (ETF) does not describe the
quantum oscillations for systems that incorporate surface regions where the
electron density drops off to zero. We find that the correction derived from
the Airy gas is universally applicable to relevant spatial regions of systems
of type (i), (ii), and (iv), but somewhat surprisingly not (iii). We discuss
possible implications of our findings to the development of functionals for the
kinetic energy density.Comment: 15 pages, 9 figure
A Stimuli-Responsive Nanocomposite for 3D Anisotropic Cell-Guidance and Magnetic Soft Robotics
Stimuli-responsive materials have the potential to enable the generation of new bioinspired devices with unique physicochemical properties and cell-instructive ability. Enhancing biocompatibility while simplifying the production methodologies, as well as enabling the creation of complex constructs, i.e., via 3D (bio)printing technologies, remains key challenge in the field. Here, a novel method is presented to biofabricate cellularized anisotropic hybrid hydrogel through a mild and biocompatible process driven by multiple external stimuli: magnetic field, temperature, and light. A low-intensity magnetic field is used to align mosaic iron oxide nanoparticles (IOPs) into filaments with tunable size within a gelatin methacryloyl matrix. Cells seeded on top or embedded within the hydrogel align to the same axes of the IOPs filaments. Furthermore, in 3D, C2C12 skeletal myoblasts differentiate toward myotubes even in the absence of differentiation media. 3D printing of the nanocomposite hydrogel is achieved and creation of complex heterogeneous structures that respond to magnetic field is demonstrated. By combining the advanced, stimuli-responsive hydrogel with the architectural control provided by bioprinting technologies, 3D constructs can also be created that, although inspired by nature, express functionalities beyond those of native tissue, which have important application in soft robotics, bioactuators, and bionic devices
NOGGIN INHIBITS TGF-β1 OR TGF-β3 INDUCED CHONDROGENESIS OF MESENCHYMAL STROMAL CELLS
Noggin (NOG) is an antagonist of bone morphogenetic proteins (BMPs), which regulates development and homeostasis of bone and cartilage. NOG has also been discovered to be an antagonist of transforming growth factor-β1 (TGF-β1). However, the effect of NOG on chondrogenesis induced by TGF-β1 remains unknown. Interestingly, in previous work NOG did not appear to influence TGF-β3-driven chondrogenesis, implying isoform specificity. In our study, the impact of exogenous NOG on TGF-β-induced chondrogenesis of bone marrow derived mesenchymal stromal cells (MSCs) was further investigated. Both TGF-β1 and TGF-β3 supplementation increased NOG expression at day 7, 14, 21 and 28 in MSC pellet culture. Addition of NOG during chondrogenic differentiation in vitro reduced sGAG release into the medium and retention within the pellet induced by TGF-β1 or TGF-β3. This was further confirmed by Safranin O/Fast Green staining. Gene downregulation including ACAN, COL2A1 and SOX9, was also observed downregulated by NOG at day 7. The same inhibitory role of NOG in TGF-β1 or TGF-β3-induced chondrogenesis suggests that the effect is not isoform-specific. We also observed differences mediated by NOG between the TGF-β1 and TGF-β3 groups. NOG suppresses cell proliferation during TGF-β1-induced chondrogenesis, whereas no significant alteration was observed in the TGF-β3 group. The effect of NOG on hypertrophy at day 7 was also investigated. In the TGF-β1 group, NOG resulted in alleviation of hypertrophy by downregulating COL10A1 and IHH expression. In the TGF-β3 group, NOG reduced hypertrophy through downregulation of COL10A1 and RUNX2
Global hybrids from the semiclassical atom theory satisfying the local density linear response
We propose global hybrid approximations of the exchange-correlation (XC)
energy functional which reproduce well the modified fourth-order gradient
expansion of the exchange energy in the semiclassical limit of many-electron
neutral atoms and recover the full local density approximation (LDA) linear
response. These XC functionals represent the hybrid versions of the APBE
functional [Phys. Rev. Lett. 106, 186406, (2011)] yet employing an additional
correlation functional which uses the localization concept of the correlation
energy density to improve the compatibility with the Hartree-Fock exchange as
well as the coupling-constant-resolved XC potential energy. Broad energetical
and structural testings, including thermochemistry and geometry, transition
metal complexes, non-covalent interactions, gold clusters and small
gold-molecule interfaces, as well as an analysis of the hybrid parameters, show
that our construction is quite robust. In particular, our testing shows that
the resulting hybrid, including 20\% of Hartree-Fock exchange and named hAPBE,
performs remarkably well for a broad palette of systems and properties, being
generally better than popular hybrids (PBE0 and B3LYP). Semi-empirical
dispersion corrections are also provided.Comment: 12 pages, 4 figure
Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design
This paper reviews past and ongoing efforts in using high-throughput ab-inito
calculations in combination with machine learning models for materials design.
The primary focus is on bulk materials, i.e., materials with fixed, ordered,
crystal structures, although the methods naturally extend into more complicated
configurations. Efficient and robust computational methods, computational
power, and reliable methods for automated database-driven high-throughput
computation are combined to produce high-quality data sets. This data can be
used to train machine learning models for predicting the stability of bulk
materials and their properties. The underlying computational methods and the
tools for automated calculations are discussed in some detail. Various machine
learning models and, in particular, descriptors for general use in materials
design are also covered.Comment: 19 pages, 2 figure
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