762 research outputs found
Phonon Bottleneck Identification in Disordered Nanoporous Materials
Nanoporous materials are a promising platform for thermoelectrics in that
they offer high thermal conductivity tunability while preserving good
electrical properties, a crucial requirement for high- effciency thermal energy
conversion. Understanding the impact of the pore arrangement on thermal
transport is pivotal to engineering realistic materials, where pore disorder is
unavoidable. Although there has been considerable progress in modeling thermal
size effects in nanostructures, it has remained a challenge to screen such
materials over a large phase space due to the slow simulation time required for
accurate results. We use density functional theory in connection with the
Boltzmann transport equation, to perform calculations of thermal conductivity
in disordered porous materials. By leveraging graph theory and regressive
analysis, we identify the set of pores representing the phonon bottleneck and
obtain a descriptor for thermal transport, based on the sum of the pore-pore
distances between such pores. This approach provides a simple tool to estimate
phonon suppression in realistic porous materials for thermoelectric
applications and enhance our understanding of heat transport in disordered
materials
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
The use of machine learning methods for accelerating the design of
crystalline materials usually requires manually constructed feature vectors or
complex transformation of atom coordinates to input the crystal structure,
which either constrains the model to certain crystal types or makes it
difficult to provide chemical insights. Here, we develop a crystal graph
convolutional neural networks framework to directly learn material properties
from the connection of atoms in the crystal, providing a universal and
interpretable representation of crystalline materials. Our method provides a
highly accurate prediction of density functional theory calculated properties
for eight different properties of crystals with various structure types and
compositions after being trained with data points. Further, our
framework is interpretable because one can extract the contributions from local
chemical environments to global properties. Using an example of perovskites, we
show how this information can be utilized to discover empirical rules for
materials design.Comment: 6+9 pages, 3+6 figure
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
The combination of high throughput computation and machine learning has led
to a new paradigm in materials design by allowing for the direct screening of
vast portions of structural, chemical, and property space. The use of these
powerful techniques leads to the generation of enormous amounts of data, which
in turn calls for new techniques to efficiently explore and visualize the
materials space to help identify underlying patterns. In this work, we develop
a unified framework to hierarchically visualize the compositional and
structural similarities between materials in an arbitrary material space with
representations learned from different layers of graph convolutional neural
networks. We demonstrate the potential for such a visualization approach by
showing that patterns emerge automatically that reflect similarities at
different scales in three representative classes of materials: perovskites,
elemental boron, and general inorganic crystals, covering material spaces of
different compositions, structures, and both. For perovskites, elemental
similarities are learned that reflects multiple aspects of atom properties. For
elemental boron, structural motifs emerge automatically showing characteristic
boron local environments. For inorganic crystals, the similarity and stability
of local coordination environments are shown combining different center and
neighbor atoms. The method could help transition to a data-centered exploration
of materials space in automated materials design.Comment: 22 + 7 pages, 6 + 5 figure
Toward phonon-boundary engineering in nanoporous materials
Tuning thermal transport in nanostructured materials is a powerful approach
to develop high-efficiency thermoelectric materials. Using a recently developed
approach based on the phonon mean free path dependent Boltzmann transport
equation, we compute the effective thermal conductivity of nanoporous materials
with pores of various shapes and arrangements. We assess the importance of
pore-pore distance in suppressing thermal transport, and identify the pore
arrangement that minimizes the thermal conductivity, composed of a periodic
arrangement of two misaligned rows of triangular pores. Such a configuration
yields a reduction in the thermal conductivity of more than with
respect the simple circular aligned case with the same porosity.Comment: 4 pages, 4 figures, 1 tabl
Correlations from ion-pairing and the Nernst-Einstein equation
We present a new approximation to ionic conductivity well suited to dynamical
atomic-scale simulations, based on the Nernst-Einstein equation. In our
approximation, ionic aggregates constitute the elementary charge carriers, and
are considered as non-interacting species. This approach conveniently captures
the dominant effect of ion-ion correlations on conductivity, short range
interactions in the form of clustering. In addition to providing better
estimates to the conductivity at a lower computational cost than exact
approaches, this new method allows to understand the physical mechanisms
driving ion conduction in concentrated electrolytes. As an example, we consider
Li conduction in poly(ethylene oxide), a standard solid-state polymer
electrolyte. Using our newly developed approach, we are able to reproduce
recent experimental results reporting negative cation transference numbers at
high salt concentrations, and to confirm that this effect can be caused by a
large population of negatively charged clusters involving cations
Phonon Diodes and Transistors from Magneto-acoustics
By sculpting the magnetic field applied to magneto-acoustic materials,
phonons can be used for information processing. Using a combination of analytic
and numerical techniques, we demonstrate designs for diodes (isolators) and
transistors that are independent of their conventional, electronic formulation.
We analyze the experimental feasibility of these systems, including the
sensitivity of the circuits to likely systematic and random errors.Comment: 5 pages, 4 figure
Optoelectronic Properties and Excitons in Hybridized Boron Nitride and Graphene Hexagonal Monolayers
We explain the nature of the electronic band gap and optical absorption
spectrum of Carbon - Boron Nitride (CBN) hybridized monolayers using density
functional theory (DFT), GW and Bethe-Salpeter equation calculations. The CBN
optoelectronic properties result from the overall monolayer bandstructure,
whose quasiparticle states are controlled by the C domain size and lie at
separate energy for C and BN without significant mixing at the band edge, as
confirmed by the presence of strongly bound bright exciton states localized
within the C domains. The resulting absorption spectra show two marked peaks
whose energy and relative intensity vary with composition in agreement with the
experiment, with large compensating quasiparticle and excitonic corrections
compared to DFT calculations. The band gap and the optical absorption are not
regulated by the monolayer composition as customary for bulk semiconductor
alloys and cannot be understood as a superposition of the properties of
bulk-like C and BN domains as recent experiments suggested
The electronic structure of liquid water within density functional theory
In the last decade, computational studies of liquid water have mostly
concentrated on ground state properties. However recent spectroscopic
measurements have been used to infer the structure of water, and the
interpretation of optical and x-ray spectra requires accurate theoretical
models of excited electronic states, not only of the ground state. To this end,
we investigate the electronic properties of water at ambient conditions using
ab initio density functional theory within the generalized gradient
approximation (DFT/GGA), focussing on the unoccupied subspace of Kohn-Sham
eigenstates. We generate long (250 ps) classical trajectories for large
supercells, up to 256 molecules, from which uncorrelated configurations of
water molecules are extracted for use in DFT/GGA calculations of the electronic
structure. We find that the density of occupied states of this molecular liquid
is well described with 32 molecule supercells using a single k-point (k = 0) to
approximate integration over the first Brillouin zone. However, the description
of the density of unoccupied states (u-EDOS) is sensitive to finite size
effects. Small, 32 molecule supercell calculations, using Gamma-the point
approximation, yield a spuriously isolated state above the Fermi level.
Nevertheless, the more accurate u-EDOS of large, 256 molecule supercells may be
reproduced using smaller supercells and increased k-point sampling. This
indicates that the electronic structure of molecular liquids like water is
relatively insensitive to the long-range disorder in the molecular structure.
These results have important implications for efficiently increasing the
accuracy of spectral calculations for water and other molecular liquids.Comment: 12 pages, 11 figures (low quality) Submitted to JChemPhy
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