1,721,066 research outputs found
Study on atomic and electronic structures of ceramic materials using spectroscopy, microscopy, and first principles calculation
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks
No full textThe discovery of
molecules with specific properties is crucial
to developing effective materials and useful drugs. Recently, to accelerate
such discoveries with machine learning, deep neural networks (DNNs)
have been applied to quantum chemistry calculations based on the density
functional theory (DFT). While various DNNs for quantum chemistry
have been proposed, these networks require various chemical descriptors
as inputs and a large number of learning parameters to model atomic
interactions. In this paper, we propose a new DNN-based molecular
property prediction that (i) does not depend on descriptors, (ii)
is more compact, and (iii) involves additional neural networks to
model the interactions between all the atoms in a molecular structure.
In the consideration of the molecular structure, we also model the
potentials between all the atoms; this allows the neural networks
to simultaneously learn the atomic interactions and potentials. We
emphasize that these atomic “pair” interactions and
potentials are characterized using the global molecular structure,
a function of the depth of the neural networks; this leads to the
implicit or indirect consideration of atomic “many-body”
interactions and potentials within the DNNs. In the evaluation of
our model with the benchmark QM9 data set, we achieved fast and accurate
prediction performances for various quantum chemical properties. In
addition, we analyzed the effects of learning the interactions and
potentials on each property. Furthermore, we demonstrated an extrapolation
evaluation, i.e., we trained a model with small molecules and tested
it with large molecules. We believe that insights into the extrapolation
evaluation will be useful for developing more practical applications
in DNN-based molecular property predictions
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Correction to “Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks”
No full textQuantum Deep Descriptor: Physically Informed Transfer Learning from Small Molecules to Polymers
No full textIn this study, we
propose a physically informed transfer learning
approach for materials informatics (MI) using a quantum deep descriptor
(QDD) obtained from the quantum deep field (QDF). The QDF is a machine
learning model based on density functional theory (DFT) and can be
trained with a large database of molecular properties. The pre-trained
QDF model can provide an effective molecular descriptor that encodes
the fundamental quantum-chemical characteristics (i.e., the wave function
or orbital, electron density, and energies of a molecule) learned
from the large database; we refer to this descriptor as a QDD. We
show that a QDD pre-trained with certain properties of small molecules
can predict different properties (e.g., the band gap and dielectric
constant) of polymers compared with some existing descriptors. We
believe that our DFT-based, physically informed transfer learning
approach will not only be useful for practical applications in MI
but will also provide quantum-chemical insights into materials in
the future. All codes used in this study are available at https://github.com/masashitsubaki
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