1,458 research outputs found
Efficient iterative method for solving the Dirac-Kohn-Sham density functional theory
We present for the first time an efficient iterative method to directly solve
the four-component Dirac-Kohn-Sham (DKS) density functional theory. Due to the
existence of the negative energy continuum in the DKS operator, the existing
iterative techniques for solving the Kohn-Sham systems cannot be efficiently
applied to solve the DKS systems. The key component of our method is a novel
filtering step (F) which acts as a preconditioner in the framework of the
locally optimal block preconditioned conjugate gradient (LOBPCG) method. The
resulting method, dubbed the LOBPCG-F method, is able to compute the desired
eigenvalues and eigenvectors in the positive energy band without computing any
state in the negative energy band. The LOBPCG-F method introduces mild extra
cost compared to the standard LOBPCG method and can be easily implemented. We
demonstrate our method in the pseudopotential framework with a planewave basis
set which naturally satisfies the kinetic balance prescription. Numerical
results for Pt, Au, TlF, and BiSe indicate that the
LOBPCG-F method is a robust and efficient method for investigating the
relativistic effect in systems containing heavy elements.Comment: 31 pages, 5 figure
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
In graphs with rich texts, incorporating textual information with structural
information would benefit constructing expressive graph embeddings. Among
various graph embedding models, random walk (RW)-based is one of the most
popular and successful groups. However, it is challenged by two issues when
applied on graphs with rich texts: (i) sampling efficiency: deriving from the
training objective of RW-based models (e.g., DeepWalk and node2vec), we show
that RW-based models are likely to generate large amounts of redundant training
samples due to three main drawbacks. (ii) text utilization: these models have
difficulty in dealing with zero-shot scenarios where graph embedding models
have to infer graph structures directly from texts. To solve these problems, we
propose a novel framework, namely Text-driven Graph Embedding with Pairs
Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling
strategy of RW, being able to reduce ~99% training samples while preserving
competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an
inductive graph embedding approach, to generate node embeddings from texts.
Since each node contains rich texts, TGE is able to generate high-quality
embeddings and provide reasonable predictions on existence of links to unseen
nodes. We evaluate TGE-PS on several real-world datasets, and experiment
results demonstrate that TGE-PS produces state-of-the-art results on both
traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019
Machine-Learned Invertible Coarse Graining for Multiscale Molecular Modeling
Multiscale molecular modeling is widely applied in scientific research of
molecular properties over large time and length scales. Two specific challenges
are commonly present in multiscale modeling, provided that information between
the coarse and fine representations of molecules needs to be properly
exchanged: One is to construct coarse grained (CG) models by passing
information from the fine to coarse levels; the other is to restore finer
molecular details given CG configurations. Although these two problems are
commonly addressed independently, in this work, we present a theory connecting
them, and develop a methodology called Cycle Coarse Graining (CCG) to solve
both problems in a unified manner. In CCG, reconstruction can be achieved via a
tractable optimization process, leading to a general method to retrieve fine
details from CG simulations, which in turn, delivers a new solution to the CG
problem, yielding an efficient way to calculate free energies in a
rare-event-free manner. CCG thus provides a systematic way for multiscale
molecular modeling, where the finer details of CG simulations can be
efficiently retrieved, and the CG models can be improved consistently.Comment: 10 pages, 5 figures, plus S
Influence of oxygen pressure and aging on LaAlO3 films grown by pulsed laser deposition on SrTiO3 substrates
The crystal structures of LaAlO3 films grown by pulsed laser deposition on
SrTiO3 substrates at oxygen pressure of 10-3 mbar or 10-5 mbar, where kinetics
of ablated species hardly depend on oxygen background pressure, are compared.
Our results show that the interface between LaAlO3 and SrTiO3 is sharper when
the oxygen pressure is lower. Over time, the formation of various crystalline
phases is observed while the crystalline thickness of the LaAlO3 layer remains
unchanged. X-ray scattering as well as atomic force microscopy measurements
indicate three-dimensional growth of such phases, which appear to be fed from
an amorphous capping layer present in as-grown samples
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