scholarly journals Annotation and Classification of Graphs of Property Values Reported in Material Science Literature

2021 ◽  
Author(s):  
Naoki Iinuma ◽  
Fusataka Kuniyoshi ◽  
Jun Ozawa ◽  
Makoto Miwa
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Line Graph ◽  
Numerical Data ◽  
Inorganic Materials ◽  
Science Literature ◽  
Graph Properties ◽  
Important Research Topic ◽  
Characteristic Values ◽  
Classification Of Graphs

Abstract Building a system for extracting information from the scientific literature is an important research topic in the field of inorganic materials science. However, conventional extraction systems have a limitation in that they do not extract characteristic values from nontextual components, such as charts, diagrams, and tables, which provide key information in many scientific documents. Although there have been several studies on identifying the characteristic values of graphs in the literature, there is no general method that classifies graphs according to the property conditions of the values in the field of materials science. Therefore, in this study, we focus on graphs that are figures representing graphically numerical data, such as a bar graph and line graph, as the first step toward developing a framework for extracting material property information from such noncontextual components. We propose deep-learning-based classification models for identifying the types of graph properties, such as temperature and time, by combining graph images, text in graphs, and captions in neural networks. To train and evaluate the models, we construct a material graph dataset with different types of material properties from a large collection of data from journals in the field of materials science. By using cloud sourcing, we annotate 16,668 images. Our experimental results demonstrate that the best model can achieve high performance with a microaveraged F-score of 0.961.

scholarly journals Annotation and Classification of Graphs of Property Values Reported in Material Science Literature

2021 ◽  
Author(s):  
Naoki Iinuma ◽  
Fusataka Kuniyoshi ◽  
Jun Ozawa ◽  
Makoto Miwa
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Line Graph ◽  
Numerical Data ◽  
Inorganic Materials ◽  
Science Literature ◽  
Graph Properties ◽  
Important Research Topic ◽  
Characteristic Values ◽  
Classification Of Graphs

Abstract Building a system for extracting information from the scientific literature is an important research topic in the field of inorganic materials science. However, conventional extracting systems have a limitation in that they do not extract characteristic values from non-textual components, such as charts, diagrams, and tables, which provide key information in many scientific documents. Although there have been several studies on identifying the characteristic values of graphs in the literature, there is no general method that classifies graphs according to the property conditions of the values in the field of materials science. Therefore, in this study, we focus on the graphs that are figures representing graphically numerical data, such as a bar graph and line graph, as the first step towards developing a framework for extracting material property information from such non-contextual components. We propose deep-learning-based classification models for identifying the types of graph properties, such as temperature and time, by combining graph images, text in graphs, and captions in neural networks. To train and evaluate the models, we construct a material graph dataset with different types of material properties from a large collection of data from journals in the field of materials science. By using cloud sourcing, we annotated 16,668 images in about 3 days. Our experimental results demonstrate that the best model can achieve high performance with a micro-averaged F-score of 0.961.

Development of high-performance objective lens polepiece for ultrahigh resolution Analytical Electron Microscope

1993 ◽  
Vol 51 ◽  
pp. 254-255
Author(s):  
K. Fukushima ◽  
T. Kaneyama ◽  
F. Hosokawa ◽  
H. Tsuno ◽  
T. Honda ◽  
...  
Keyword(s):  
Electron Microscope ◽  
High Performance ◽  
Open Space ◽  
Materials Science ◽  
Objective Lens ◽  
Ultrahigh Resolution ◽  
Science Field ◽  
Specimen Holder ◽  
Analytical Electron ◽  
Analytical Electron Microscope

Recently, in the materials science field, the ultrahigh resolution analytical electron microscope (UHRAEM) has become a very important instrument to study extremely fine areas of the specimen. The requirements related to the performance of the UHRAEM are becoming gradually severer. Some basic characteristic features required of an objective lens are as follows, and the practical performance of the UHRAEM should be judged by totally evaluating them.1) Ultrahigh resolution to resolve ultrafine structure by atomic-level observation.2) Nanometer probe analysis to analyse the constituent elements in nm-areas of the specimen.3) Better performance of x-ray detection for EDS analysis, that is, higher take-off angle and larger detection solid angle.4) Higher specimen tilting angle to adjust the specimen orientation.To attain these requirements simultaneously, the objective lens polepiece must have smaller spherical and chromatic aberration coefficients and must keep enough open space around the specimen holder in it.

scholarly journals Looking through glass: Knowledge discovery from materials science literature using natural language processing

Patterns ◽  
2021 ◽  
pp. 100290
Author(s):  
Vineeth Venugopal ◽  
Sourav Sahoo ◽  
Mohd Zaki ◽  
Manish Agarwal ◽  
Nitya Nand Gosvami ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Materials Science ◽  
Science Literature

scholarly journals Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Volume Fraction ◽  
Training Data ◽  
Phase Fraction ◽  
Phase Volume ◽  
Generative Adversarial Network ◽  
Phase Volume Fraction ◽  
Original Dataset ◽  
Multi Phase

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.

You Only Traverse Twice: A YOTT Placement, Routing, and Timing Approach for CGRAs

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Michael Canesche ◽  
Westerley Carvalho ◽  
Lucas Reis ◽  
Matheus Oliveira ◽  
Salles Magalhães ◽  
...  
Keyword(s):  
Execution Time ◽  
High Performance ◽  
Coarse Grained ◽  
Optimal Placement ◽  
Greedy Heuristics ◽  
High Quality ◽  
Solution Quality ◽  
Graph Traversal ◽  
Trade Offs ◽  
Graph Properties

Coarse-grained reconfigurable architecture (CGRA) mapping involves three main steps: placement, routing, and timing. The mapping is an NP-complete problem, and a common strategy is to decouple this process into its independent steps. This work focuses on the placement step, and its aim is to propose a technique that is both reasonably fast and leads to high-performance solutions. Furthermore, a near-optimal placement simplifies the following routing and timing steps. Exact solutions cannot find placements in a reasonable execution time as input designs increase in size. Heuristic solutions include meta-heuristics, such as Simulated Annealing (SA) and fast and straightforward greedy heuristics based on graph traversal. However, as these approaches are probabilistic and have a large design space, it is not easy to provide both run-time efficiency and good solution quality. We propose a graph traversal heuristic that provides the best of both: high-quality placements similar to SA and the execution time of graph traversal approaches. Our placement introduces novel ideas based on “you only traverse twice” (YOTT) approach that performs a two-step graph traversal. The first traversal generates annotated data to guide the second step, which greedily performs the placement, node per node, aided by the annotated data and target architecture constraints. We introduce three new concepts to implement this technique: I/O and reconvergence annotation, degree matching, and look-ahead placement. Our analysis of this approach explores the placement execution time/quality trade-offs. We point out insights on how to analyze graph properties during dataflow mapping. Our results show that YOTT is 60.6 , 9.7 , and 2.3 faster than a high-quality SA, bounding box SA VPR, and multi-single traversal placements, respectively. Furthermore, YOTT reduces the average wire length and the maximal FIFO size (additional timing requirement on CGRAs) to avoid delay mismatches in fully pipelined architectures.

Thermoelectrics by Computational Design: Progress and Opportunities

2021 ◽  
Vol 51 (1) ◽  
Author(s):  
Boris Kozinsky ◽  
David J. Singh
Keyword(s):  
First Principles ◽  
High Performance ◽  
Materials Science ◽  
Phonon Scattering ◽  
Transport Coefficients ◽  
Computational Design ◽  
Science Volume ◽  
Annual Review ◽  
Publication Date ◽  
Boltzmann Transport

The performance of thermoelectric materials is determined by their electrical and thermal transport properties that are very sensitive to small modifications of composition and microstructure. Discovery and design of next-generation materials are starting to be accelerated by computational guidance. We review progress and challenges in the development of accurate and efficient first-principles methods for computing transport coefficients and illustrate approaches for both rapid materials screening and focused optimization. Particularly important and challenging are computations of electron and phonon scattering rates that enter the Boltzmann transport equations, and this is where there are many opportunities for improving computational methods. We highlight the first successful examples of computation-driven discoveries of high-performance materials and discuss avenues for tightening the interaction between theoretical and experimental materials discovery and optimization. Expected final online publication date for the Annual Review of Materials Science, Volume 51 is August 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Analysis for understanding and standardization of additive manufacturing processes

2021 ◽  
Author(s):  
Alexander Matschinski ◽  
Tim Osswald ◽  
Klaus Drechsler
Keyword(s):  
Additive Manufacturing ◽  
High Performance ◽  
Manufacturing Systems ◽  
Polymer Melt ◽  
Thermal Shrinkage ◽  
Target Component ◽  
Associated Functions ◽  
Cooling Mechanism ◽  
Characteristic Values ◽  
As Stress

The market segment of additive manufacturing is showing an annual growth of more than ten percent, with extrusion-based processes being the larger segment of the market. The scope of use is limited to secondary structures. Equipment manufacturers try to guarantee constant material characteristics by closed systems. The characteristic values are up to 50% below the ones from injection molding. The processing of high-performance polymers with reinforcing fibers is an additional challenge. Further development requires an opening of the material and manufacturing systems. The guidelines and standardization for this are still missing. For this reason, a functional analysis (FA) according to TRIZ ("theory of the resolution of invention-related tasks") is performed within this study. This identifies the undesired functions and quantifies their coupling with process components and parameters. In the FA, the manufactured part is the target component in order to address its quality. This way the FA identifies five undesirable functions in the process. These are: deform, cool, weaken, swell and shape. For hightemperature thermoplastics, thermal shrinkage is the primary cause of geometric tolerance. Therefore, the deformation is largely dependent on the cooling mechanism. For a detailed analysis, the polymer melt is further disassembled. The results are six sub-components. The weakening is mainly due to the physical phase of the voids, which exists during the entire processing. The breakdown comprises physical fields such as stress, temperature and flow. These determine the output properties as well as the bonding between the layers. The associated functions are the swelling and shaping. In order to generate broadly applicable standardizations, research questions for further investigation are derived from this study.

Soft Chemical Routes to Heterostructured High-Tc Superconducting Materials

MRS Bulletin ◽  
2000 ◽  
Vol 25 (9) ◽  
pp. 32-39 ◽  
Author(s):  
Jin-Ho Choy ◽  
Soon-Jae Kwon ◽  
Seong-Ju Hwang ◽  
Eue-Soon Jang
Keyword(s):  
Solid State ◽  
High Performance ◽  
Inorganic Compounds ◽  
Rechargeable Batteries ◽  
Host Lattice ◽  
Inorganic Materials ◽  
Solid State Reactions ◽  
Metal Chalcogenides ◽  
Structural Anisotropy ◽  
Intercalation Reaction

Recently, inorganic/inorganic and organic/inorganic heterostructured materials have attracted considerable research interest, due to their unusual physicochemical properties, which cannot be achieved by conventional solid-state reactions. In order to develop new hybrid materials, various synthetic approaches, such as vacuum deposition, Langmuir–Blodgett films, selfassembly, and intercalation techniques, have been explored. Among them, the intercalation reaction technique—that is, the reversible insertion of guest species into the two-dimensional host lattice—is expected to be one of the most effective tools for preparing new layered heterostructures because this process can provide a soft chemical way of hybridizing inorganic/inorganic, organic/inorganic, or biological/inorganic compounds. In fact, the intercalation/deintercalation process allows us to design high-performance materials in a solution at ambient temperature and pressure, just as “soft solution processing” provides a simple and economical route for advanced inorganic materials by means of an environmentally benign, lowenergy method. These unique advantages of the intercalation technique have led to its wide application to diverse fields of the solid-state sciences, namely, secondary (rechargeable) batteries, electrochromic systems, oxidation–reduction catalysts, separating agents, sorbents, and so on. Through these extensive studies, many kinds of low-dimensional compounds have been developed as host materials for the intercalation reaction, including graphite, transition-metal chalcogenides, transitionmetal oxides, aluminosilicates, metal phosphates, metal chalcogenohalides, and so on. Recently, the area of intercalation chemistry has been extended to high-Tc superconducting copper oxides, resulting in remarkable structural anisotropy.

scholarly journals Crafting Efficient Neural Graph of Large Entropy

2019 ◽  
Author(s):  
Minjing Dong ◽  
Hanting Chen ◽  
Yunhe Wang ◽  
Chang Xu
Keyword(s):  
Information Flow ◽  
Network Architecture ◽  
High Performance ◽  
Global Information ◽  
High Quality ◽  
Network Pruning ◽  
Initial Network ◽  
Trade Offs ◽  
Graph Properties ◽  
Deep Cnn

Network pruning is widely applied to deep CNN models due to their heavy computation costs and achieves high performance by keeping important weights while removing the redundancy. Pruning redundant weights directly may hurt global information flow, which suggests that an efficient sparse network should take graph properties into account. Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture. Our algorithm can be easily implemented and deployed to different popular CNN models and achieve better trade-offs.

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