1,720,979 research outputs found
Data supporting Holey Substrate-Directed Strain Pattering in Bilayer MoS2
No full textAll TEM images are in .dm3 or .tif file formats, which can be accessed by Gatan DigitalMicrograph software. The AFM data showing topographic mapping were provided in .gwy and .txt file formats. Detailed description of the simulation data is provided in a readme file.This data set contains transmission electron microscopy (TEM), atomic force microscopy (AFM), and atomistic simulation data supporting "Holey Substrate-Directed Strain Pattering in Bilayer MoS2" manuscript cited in referenced by.National Science Foundation through the University of Minnesota MRSEC under Award Number DMR-2011401National Science Foundation under Grant No. DMR-1654318the Army Research Office (W911NF-14-1-0247) under the MURI programNSF through the UMN MRSEC program under Award Number DMR-2011401Louise T. Dosdall FellowshipZhang, Yichao; Choi, Moon-Ki; Haugstad, Greg; Tadmor, Ellad B; Flannigan, David J. (2021). Data supporting Holey Substrate-Directed Strain Pattering in Bilayer MoS2. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/14jz-pj24
Supporting data for Atomistically-informed continuum modeling and isogeometric analysis of 2D materials over holey substrates
No full textfigure* directories include data files and MATLAB files for generating figures in the paper.
*_test directories include LAMMPS input script and atomistic structure files to simulate uniaxial and bending tests. Detailed description is in the paper.
*_new fix directories has .cpp and .h files. These files can be implemented in LAMMPS as a new fix command.Data includes LAMMPS input script for MoS2 test problems and MATLAB data for generating figures in the paper.Start-up funding from the University of California San DiegoThe University of Minnesota MRSEC under Award Number DMR-2011401Choi, Moon-ki; Pasetto, Marco; Shen, Zhaoxiang; Tadmor, Ellad; Kamensky, David. (2022). Supporting data for Atomistically-informed continuum modeling and isogeometric analysis of 2D materials over holey substrates. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/gfms-wy93
Designing efficient, interpretable, and generalizable machine learning interatomic potentials
Full text linkSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo termsThe student, Joshua Vita, accepted the attached license on 2023-06-29 at 09:42.The student, Joshua Vita, submitted this Dissertation for approval on 2023-06-29 at 09:49.This Dissertation was approved for publication on 2023-07-05 at 16:13.DSpace SAF Submission Ingestion Package generated from Vireo submission #19481 on 2024-03-01 at 13:12:59Interatomic potentials (IPs) are invaluable tools in the fields of computational materials science and chemistry for their ability to accelerate atomic-scale simulations beyond the length- and time-scales that are accessible using first-principles techniques. In recent years, the application of machine learning (ML) and deep learning (DL) models and algorithms towards IP development has been a major area of interest, where machine learning interatomic potentials (MLIPs) are seen as being more flexible and accurate than their classical potential counterparts. The resounding success of MLIPs has led to a major shift away from the physical foundations characteristic of classical models, and towards more data-driven methods. While a data-centric fitting approach guided by error-based training metrics can be expected to correlate well with accuracies on higher-level property predictions, it has also resulted in a pattern of developing slower, more complex, and less general models. This work aims to address these issues by rectifying the false dichotomy between classical and ML IPs, and developing models and techniques that show how IP design can be improved with a focus on speed, interpretability, and generalizability. By first performing an in-depth comparison of the performance of a classical spline-based MEAM (s-MEAM) IP relative to a collection of MLIPs, I demonstrate the competitive nature of s-MEAM on a variety of common benchmarking tests. s-MEAM is shown to be capable of achieving errors comparable to those of the benchmarked MLIPs while maintaining its high speeds and interpretability, establishing its position on the accuracy-speed pareto front. These results demonstrate that high model complexities may not be strictly necessary in order to achieve near-DFT accuracy for certain benchmarking tasks and suggest an alternative route towards sampling the high accuracy, low complexity region of model space by starting with forms that promote simpler and more interpretable interatomic potentials I then build upon these results by leveraging the strengths of both s-MEAM and modern neural network (NN) architectures to propose a novel MLIP framework. The proposed framework, which I call the spline-based neural network potential (s-NNP), is a simplified version of the traditional NNP that can be used to describe complex datasets in a computationally efficient manner. I demonstrate how this framework can be used to probe the boundary between classical and ML IPs, highlighting the benefits of key architectural changes for improving model accuracy and interpretability. Finally, I present a metric using the entropy of the loss landscape, and show how it can be used to predict model performance on out-of-domain data and provide insights regarding model design and optimization. Using this metric, I demonstrate how architectural and optimization choices influence the generalization capacity of neural network (NN) IPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. With a large-scale study on two state-of-the-art MLIPs, and their optimizers, I show that the metric of loss entropy predicts out-of-distribution error and data efficiency despite being computed only on the training set
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
Potential transferability and the Knowledgebase of Interatomic Models
Full text linkEmpirical (fitted) interatomic potentials are widely used to predict the response of materials and structures in atomistic simulations. The ability of a potential to predict behavior that it was not fitted to reproduce is referred to as its “transferability.” Despite the importance of the notion of transferability in selecting an empirical potential for a specific application, it has not yet been rigorously addressed by the materials simulation community. This is now possible due to the forthcoming Knowledgebase of Interatomic Models (https://openkim.org) which promises to serve as an abundant source of predictions of potentials and the corresponding first principles and experimental data for various material properties. Making use of this novel data resource in a cumulative manner, we compare representations of atomic environments as well as nonparametric supervised learning algorithms which can be used to systematically define and predict the transferability of empirical potentials
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