71,746 research outputs found
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
The spin-polarized state of graphene: a spin superconductor
We study the spin-polarized Landau-level state of graphene. Due to
the electron-hole attractive interaction, electrons and holes can bound into
pairs. These pairs can then condense into a spin-triplet superfluid ground
state: a spin superconductor state. In this state, a gap opens up in the edge
bands as well as in the bulk bands, thus it is a charge insulator, but it can
carry the spin current without dissipation. These results can well explain the
insulating behavior of the spin-polarized state in the recent
experiments.Comment: 6 pages, 4 figure
Spin-current diode with a ferromagnetic semiconductor
Diode is a key device in electronics: the charge current can flow through the
device under a forward bias, while almost no current flows under a reverse
bias. Here we propose a corresponding device in spintronics: the spin-current
diode, in which the forward spin current is large but the reversed one is
negligible. We show that the lead/ferromagnetic quantum dot/lead system and the
lead/ferromagnetic semiconductor/lead junction can work as spin-current diodes.
The spin-current diode, a low dissipation device, may have important
applications in spintronics, as the conventional charge-current diode does in
electronics.Comment: 5 pages, 3 figure
Mathematical Modeling of Product Rating: Sufficiency, Misbehavior and Aggregation Rules
Many web services like eBay, Tripadvisor, Epinions, etc, provide historical
product ratings so that users can evaluate the quality of products. Product
ratings are important since they affect how well a product will be adopted by
the market. The challenge is that we only have {\em "partial information"} on
these ratings: Each user provides ratings to only a "{\em small subset of
products}". Under this partial information setting, we explore a number of
fundamental questions: What is the "{\em minimum number of ratings}" a product
needs so one can make a reliable evaluation of its quality? How users' {\em
misbehavior} (such as {\em cheating}) in product rating may affect the
evaluation result? To answer these questions, we present a formal mathematical
model of product evaluation based on partial information. We derive theoretical
bounds on the minimum number of ratings needed to produce a reliable indicator
of a product's quality. We also extend our model to accommodate users'
misbehavior in product rating. We carry out experiments using both synthetic
and real-world data (from TripAdvisor, Amazon and eBay) to validate our model,
and also show that using the "majority rating rule" to aggregate product
ratings, it produces more reliable and robust product evaluation results than
the "average rating rule".Comment: 33 page
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