1,720,960 research outputs found
MACHINE LEARNING FOR ELECTRONIC STRUCTURE PREDICTION
Get PDFKohn-Sham density functional theory is the work horse of computational material science research. The core of Kohn-Sham density functional theory, the Kohn-Sham equations, output charge density, energy levels and wavefunctions. In principle, the electron density can be used to obtain several other properties of interest including total potential energy of the system, atomic forces, binding energies and electric constants. In this work we present machine learning models designed to bypass the Kohn-Sham equations by directly predicting electron density. Two distinct models were developed: one tailored to predict electron density for quasi one-dimensional materials under strain, while the other is applicable across a wide array of material systems, with a specific emphasis on metallic and alloy compositions.
The first model applies to important classes of material systems such as nanotubes, for which, tuning the interplay of mechanical deformations and electronic fields --- i.e., strain engineering --- is an active area of investigation. Using armchair single wall carbon nanotubes as a example, we demonstrate the use of the model to predict ground state electron density and the nuclear pseudocharges, when three parameters --- namely, the radius of the nanotube, its axial stretch, and the twist per unit length --- are specified as inputs. Other electronic properties of interest, including the ground state electronic free energy, can be evaluated from these predicted fields with low-overhead post-processing, typically to chemical accuracy. We anticipate that this framework will find utility in the automated discovery of low--dimensional materials, as well as the multi-scale modeling of such systems.
The second model has an emphasis on metallic and alloy systems. One of the fundamental challenge for this model is generation of training data. The computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident --- and when verifiable, accurate --- predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at unprecedented, multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources
DEFORMATION MANIFOLD LEARNING MODEL FOR MULTI WALLED CARBON NANOTUBES
Get PDFTwo-Dimensional (2D) materials are being studied widely by researchers due to their superior material properties over the bulk materials. Since the isolation of graphene in 2004, graphene has gained popularity amongst the 2D materials community. Graphene when rolled into sheets form Carbon Nanotubes (CNTs) which possess excellent mechanical and electrical properties. Concentric stacks of CNTs yield Multi-walled Carbon Nanotubes (MWCNTs) which are superior to CNTs in certain aspects. It has been well established that the deformation of CNTs and MWCNTs change their mechanical and electrical properties significantly. This has opened doors for CNTs into numerous applications and also piqued the need of studying the deformation characteristics of CNTs. Efforts have been made by researchers to develop models that approximate the geometry of CNTs and simulate them under given loading conditions. Atomistic models, Continuum models, and atomistic-continuum models have been used to simulate the deformation of CNTs. These models have been accurate in generating the deformed CNTs and are in good agreement with the experimental results. The models have also been proven to work well for MWCNTs having millions of atoms. Despite being accurate these models require high computation power which is a bottleneck in the wide use of these models. In this work, we present a data-driven model to predict the deformation of MWCNTs under torsional and bending loads.
Million atom MWCNTs are discretized and represented through a proposed dimensionality reduction technique described as constrained-Functional Principal Component Analysis. Further, learning is performed using Deep Neural Networks (DNNs) in the dimensionally reduced space. The proposed framework accurately predicts the deformation of MWCNTs and is in good agreement with the atomistic-physics simulations. The proposed model has an edge over traditional models in regards to the computational time and computational power required. The model yields dominant patterns of deformation which explain the prediction capability of the model. This makes our model comprehensible. The model is currently developed for MWCNTs and is presented here, but the model can be extended to other 2D materials and can form a basis towards the use of data-driven approaches for exploring the mechanics and physics of 2D materials
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
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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Interpretable machine learning model for the deformation of multiwalled carbon nanotubes
No full textWe present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model that comprises a novel dimensionality reduction technique and a deep neural network-based learning in the reduced dimension. The proposed nonlinear dimensionality reduction technique extends the functional principal component analysis to satisfy the constraint of deformation. Its novelty lies in designing a function space that satisfies the constraint exactly, which is crucial for efficient dimensionality reduction. Owing to the dimensionality reduction and several other strategies adopted in the present paper, learning through deep neural networks is remarkably accurate. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials
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