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

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 $60 \%$ with respect the simple circular aligned case with the same porosity.Comment: 4 pages, 4 figures, 1 tabl

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 $10^4$ 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

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