139,590 research outputs found
Machine Learning Accelerated Discovery of Corrosion-resistant High-entropy Alloys
Corrosion has a wide impact on society, causing catastrophic damage to
structurally engineered components. An emerging class of corrosion-resistant
materials are high-entropy alloys. However, high-entropy alloys live in
high-dimensional composition and configuration space, making materials designs
via experimental trial-and-error or brute-force ab initio calculations almost
impossible. Here we develop a physics-informed machine-learning framework to
identify corrosion-resistant high-entropy alloys. Three metrics are used to
evaluate the corrosion resistance, including single-phase formability, surface
energy and Pilling-Bedworth ratios. We used random forest models to predict the
single-phase formability, trained on an experimental dataset. Machine learning
inter-atomic potentials were employed to calculate surface energies and
Pilling-Bedworth ratios, which are trained on first-principles data fast
sampled using embedded atom models. A combination of random forest models and
high-fidelity machine learning potentials represents the first of its kind to
relate chemical compositions to corrosion resistance of high-entropy alloys,
paving the way for automatic design of materials with superior corrosion
protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys
and we identified composition regions with high corrosion resistance. Machine
learning predicted lattice constants and surface energies are consistent with
values by first-principles calculations. The predicted single-phase formability
and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments.
This framework is general in its application and applicable to other materials,
enabling high-throughput screening of material candidates and potentially
reducing the turnaround time for integrated computational materials
engineering
Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data
Knowledge about the culture of a user is especially important for the design
of e-learning applications. In the experiment reported here, questionnaire
data was used to build machine learning models to automatically predict the
culture of a user. This work can be applied to automatic culture detection
and subsequently to the adaptation of user interfaces in e-learning
An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening
BACKGROUND: The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work. OBJECTIVE: In this paper, we present a solution to AATR based on such X-ray CT baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects). METHODS: We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multi-class support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT baggage image datasets specifically collected for the AATR study. RESULTS: Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%. CONCLUSIONS: Our AATR shows the capabilities of adapting to varying types of materials, even the unknown materials which are not available in the training data, adapting to varying required probability of detection and adapting to varying scales of the threat object
Insightful classification of crystal structures using deep learning
Computational methods that automatically extract knowledge from data are
critical for enabling data-driven materials science. A reliable identification
of lattice symmetry is a crucial first step for materials characterization and
analytics. Current methods require a user-specified threshold, and are unable
to detect average symmetries for defective structures. Here, we propose a
machine-learning-based approach to automatically classify structures by crystal
symmetry. First, we represent crystals by calculating a diffraction image, then
construct a deep-learning neural-network model for classification. Our approach
is able to correctly classify a dataset comprising more than 100 000 simulated
crystal structures, including heavily defective ones. The internal operations
of the neural network are unraveled through attentive response maps,
demonstrating that it uses the same landmarks a materials scientist would use,
although never explicitly instructed to do so. Our study paves the way for
crystal-structure recognition of - possibly noisy and incomplete -
three-dimensional structural data in big-data materials science.Comment: Nature Communications, in press (2018
Detecting Family Resemblance: Automated Genre Classification.
This paper presents results in automated genre classification of digital documents in PDF format. It describes genre classification as an important ingredient in contextualising scientific data and in retrieving targetted material for improving research. The current paper compares the role of visual layout, stylistic features and language model features in clustering documents and presents results in retrieving five selected genres (Scientific Article, Thesis, Periodicals, Business Report, and Form) from a pool of materials populated with documents of the nineteen most popular genres found in our experimental data set.
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