scholarly journals Learning Representations of Inorganic Materials from Generative Adversarial Networks

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1889
Author(s):  
Tiantian Hu ◽  
Hui Song ◽  
Tao Jiang ◽  
Shaobo Li
Keyword(s):  
Large Scale ◽  
Inorganic Materials ◽  
Molecular Composition ◽  
Materials Characterization ◽  
Generative Adversarial Networks ◽  
Feature Engineering ◽  
Material Research ◽  
Generative Adversarial Network ◽  
Characterization Of Materials

The two most important aspects of material research using deep learning (DL) or machine learning (ML) are the characteristics of materials data and learning algorithms, where the proper characterization of materials data is essential for generating accurate models. At present, the characterization of materials based on the molecular composition includes some methods based on feature engineering, such as Magpie and One-hot. Although these characterization methods have achieved significant results in materials research, these methods based on feature engineering cannot guarantee the integrity of materials characterization. One possible approach is to learn the materials characterization via neural networks using the chemical knowledge and implicit composition rules shown in large-scale known materials. This article chooses an adversarial method to learn the composition of atoms using the Generative Adversarial Network (GAN), which makes sense for data symmetry. The total loss value of the discriminator on the test set is reduced from 4.1e13 to 0.3194, indicating that the designed GAN network can well capture the combination of atoms in real materials. We then use the trained discriminator weights for material characterization and predict bandgap, formation energy, critical temperature (Tc) of superconductors on the Open Quantum Materials Database (OQMD), Materials Project (MP), and SuperCond datasets. Experiments show that when using the same predictive model, our proposed method performs better than One-hot and Magpie. This article provides an effective method for characterizing materials based on molecular composition in addition to Magpie, One-hot, etc. In addition, the generator learned in this study generates hypothetical materials with the same distribution as known materials, and these hypotheses can be used as a source for new material discovery.

Some applications of electron beam microanalysis techniques in VLSI process evaluation

1983 ◽  
Vol 41 ◽  
pp. 160-161
Author(s):  
Simon Thomas
Keyword(s):  
Integrated Circuits ◽  
Process Evaluation ◽  
Large Scale ◽  
Technology Development ◽  
Analytical Techniques ◽  
Successful Implementation ◽  
Analysis Of Failures ◽  
Characterization Of Materials ◽  
Circuits Vlsi

Trends in the technology development of very large scale integrated circuits (VLSI) have been in the direction of higher density of components with smaller dimensions. The scaling down of device dimensions has been not only laterally but also in depth. Such efforts in miniaturization bring with them new developments in materials and processing. Successful implementation of these efforts is, to a large extent, dependent on the proper understanding of the material properties, process technologies and reliability issues, through adequate analytical studies. The analytical instrumentation technology has, fortunately, kept pace with the basic requirements of devices with lateral dimensions in the micron/ submicron range and depths of the order of nonometers. Often, newer analytical techniques have emerged or the more conventional techniques have been adapted to meet the more stringent requirements. As such, a variety of analytical techniques are available today to aid an analyst in the efforts of VLSI process evaluation. Generally such analytical efforts are divided into the characterization of materials, evaluation of processing steps and the analysis of failures.

scholarly journals COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Han Bao ◽  
Xun Zhou ◽  
Yiqun Xie ◽  
Yingxue Zhang ◽  
Yanhua Li
Keyword(s):  
Spatial Heterogeneity ◽  
Real World ◽  
Large Scale ◽  
Census Data ◽  
Human Mobility ◽  
Training Data ◽  
Estimation Accuracy ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Spatio Temporal

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

scholarly journals AUTOMATIC LARGE-SCALE 3D BUILDING SHAPE REFINEMENT USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS

2018 ◽  
Vol XLII-2 ◽  
pp. 103-108 ◽  
Author(s):  
K. Bittner ◽  
P. d’Angelo ◽  
M. Körner ◽  
P. Reinartz
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Large Scale ◽  
Point Clouds ◽  
Research Area ◽  
Sar Interferometry ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Building Shape ◽  
Object Shapes

<p><strong>Abstract.</strong> Three-dimensional building reconstruction from remote sensing imagery is one of the most difficult and important 3D modeling problems for complex urban environments. The main data sources provided the digital representation of the Earths surface and related natural, cultural, and man-made objects of the urban areas in remote sensing are the <i>digital surface models (DSMs)</i>. The DSMs can be obtained either by <i>light detection and ranging (LIDAR)</i>, SAR interferometry or from stereo images. Our approach relies on automatic global 3D building shape refinement from stereo DSMs using deep learning techniques. This refinement is necessary as the DSMs, which are extracted from image matching point clouds, suffer from occlusions, outliers, and noise. Though most previous works have shown promising results for building modeling, this topic remains an open research area. We present a new methodology which not only generates images with continuous values representing the elevation models but, at the same time, enhances the 3D object shapes, buildings in our case. Mainly, we train a <i>conditional generative adversarial network (cGAN)</i> to generate accurate LIDAR-like DSM height images from the noisy stereo DSM input. The obtained results demonstrate the strong potential of creating large areas remote sensing depth images where the buildings exhibit better-quality shapes and roof forms.</p>

scholarly journals GAN-GL: Generative Adversarial Networks for Glacial Lake Mapping

2021 ◽  
Vol 13 (22) ◽  
pp. 4728
Author(s):  
Hang Zhao ◽  
Meimei Zhang ◽  
Fang Chen
Keyword(s):  
Large Scale ◽  
Glacial Lake ◽  
Background Information ◽  
Generative Adversarial Networks ◽  
High Mountain ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Ancillary Data ◽  
Generative Adversarial Network ◽  
Adversarial Network

Remote sensing is a powerful tool that provides flexibility and scalability for monitoring and investigating glacial lakes in High Mountain Asia (HMA). However, existing methods for mapping glacial lakes are designed based on a combination of several spectral features and ancillary data (such as the digital elevation model, DEM) to highlight the lake extent and suppress background information. These methods, however, suffer from either the inevitable requirement of post-processing work or the high costs of additional data acquisition. Signifying a key advancement in the deep learning models, a generative adversarial network (GAN) can capture multi-level features and learn the mapping rules in source and target domains using a minimax game between a generator and discriminator. This provides a new and feasible way to conduct large-scale glacial lake mapping. In this work, a complete glacial lake dataset was first created, containing approximately 4600 patches of Landsat-8 OLI images edited in three ways—random cropping, density cropping, and uniform cropping. Then, a GAN model for glacial lake mapping (GAN-GL) was constructed. The GAN-GL consists of two parts—a generator that incorporates a water attention module and an image segmentation module to produce the glacial lake masks, and a discriminator which employs the ResNet-152 backbone to ascertain whether a given pixel belonged to a glacial lake. The model was evaluated using the created glacial lake dataset, delivering a good performance, with an F1 score of 92.17% and IoU of 86.34%. Moreover, compared to the mapping results derived from the global–local iterative segmentation algorithm and random forest for the entire Eastern Himalayas, our proposed model was superior regarding the segmentation of glacial lakes under complex and diverse environmental conditions, in terms of accuracy (precision = 93.19%) and segmentation efficiency. Our model was also very good at detecting small glacial lakes without assistance from ancillary data or human intervention.

Guided Waves in Plates and Their Use in Materials Characterization

1997 ◽  
Vol 50 (5) ◽  
pp. 247-284 ◽  
Author(s):  
D. E. Chimenti
Keyword(s):  
Guided Waves ◽  
Large Body ◽  
Review Article ◽  
Guided Wave ◽  
Materials Characterization ◽  
Current State ◽  
Ultrasonic Characterization ◽  
Characterization Of Materials ◽  
Wave Methods

In this review article, the ultrasonic characterization of materials using guided plate waves and their usage to elucidate mechanical properties of plate-like structures is reviewed. The purpose here is to summarize and explain the large body of theoretical and experimental work in this developing field. It is also to gain a perspective on recent salient contributions and to analyze the current state of knowledge and practice in guided wave ultrasonics. Models of waves in plates are examined, as are the means to generate and detect them. Their application to several problems of current interest in materials characterization is treated in detail. In particular, composite materials and their inspection and characterization have been a major impetus in the development of guided wave methods. Techniques to inspect composites sensitively and reliably for defects and to probe their micromechanical behavior are a major focus of this article. Also considered are the characterization of adhesive bonds, the measurement of stress and texture, and the detection of defects using guided waves. This review article contains 362 references.

scholarly journals USING GENERATIVE ADVERSARIAL NETWORKS FOR EXTRACTION OF INSAR SIGNALS FROM LARGE-SCALE SENTINEL-1 INTERFEROGRAMS BY IMPROVING TROPOSPHERIC NOISE CORRECTION

2021 ◽  
Vol V-3-2021 ◽  
pp. 57-64
Author(s):  
B. Ghosh ◽  
M. Haghshenas Haghighi ◽  
M. Motagh ◽  
S. Maghsudi
Keyword(s):  
Large Scale ◽  
Ground Deformation ◽  
Noise Model ◽  
Generative Adversarial Networks ◽  
Tropospheric Delay ◽  
Generative Adversarial Network ◽  
Water Vapour Content ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Noise Correction

Abstract. Spatiotemporal variations of pressure, temperature, water vapour content in the atmosphere lead to significant delays in interferometric synthetic aperture radar (InSAR) measurements of deformations in the ground. One of the key challenges in increasing the accuracy of ground deformation measurements using InSAR is to produce robust estimates of the tropospheric delay. Tropospheric models like ERA-Interim can be used to estimate the total tropospheric delay in interferograms in remote areas. The problem with using ERA-Interim model for interferogram correction is that after the tropospheric correction, there are still some residuals left in the interferograms, which can be mainly attributed to turbulent troposphere. In this study, we propose a Generative Adversarial Network (GAN) based approach to mitigate the phase delay caused by troposphere. In this method, we implement a noise to noise model, where the network is trained only with the interferograms corrupted by tropospheric noise. We applied the technique over 116 large scale 800 km long interfergrams formed from Sentinel-1 acquisitions covering a period from 25th October, 2014 to 2nd November, 2017 from descending track numbered 108 over Iran. Our approach reduces the root mean square of the phase values of the interferogram 64% compared to those of the original interferogram and by 55% in comparison to the corresponding ERA-Interim corrected version.

scholarly journals Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization

2021 ◽  
Author(s):  
Mengting Liu ◽  
Piyush Maiti ◽  
Sophia Thomopoulos ◽  
Alyssa Zhu ◽  
Yaqiong Chai ◽  
...  
Keyword(s):  
Large Scale ◽  
Transfer Problem ◽  
Large Data ◽  
Generative Adversarial Networks ◽  
Reference Image ◽  
Demographic Information ◽  
Mr Images ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Cross Site

AbstractLarge data initiatives and high-powered brain imaging analyses require the pooling of MR images acquired across multiple scanners, often using different protocols. Prospective cross-site harmonization often involves the use of a phantom or traveling subjects. However, as more datasets are becoming publicly available, there is a growing need for retrospective harmonization, pooling data from sites not originally coordinated together. Several retrospective harmonization techniques have shown promise in removing cross-site image variation. However, most unsupervised methods cannot distinguish between image-acquisition based variability and cross-site population variability, so they require that datasets contain subjects or patient groups with similar clinical or demographic information. To overcome this limitation, we consider cross-site MRI image harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a reference image directly, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multi-site datasets with varied demographics. Results demonstrated that our styleencoding model can harmonize MR images, and match intensity profiles, successfully, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. Moreover, we further demonstrated that if we included diverse enough images into the training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising novel tool for ongoing collaborative studies.

scholarly journals Improving Multi-Agent Generative Adversarial Nets with Variational Latent Representation

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1055
Author(s):  
Huan Zhao ◽  
Tingting Li ◽  
Yufeng Xiao ◽  
Yu Wang
Keyword(s):  
Large Scale ◽  
Random Noise ◽  
Qualitative Evaluation ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Proposed Model ◽  
Multi Agent ◽  
Latent Representations ◽  
Real World Datasets

Generative adversarial networks (GANs), which are a promising type of deep generative network, have recently drawn considerable attention and made impressive progress. However, GAN models suffer from the well-known problem of mode collapse. This study focuses on this challenge and introduces a new model design, called the encoded multi-agent generative adversarial network (E-MGAN), which tackles the mode collapse problem by introducing the variational latent representations learned from a variable auto-encoder (VAE) to a multi-agent GAN. The variational latent representations are extracted from training data to replace the random noise input of the general multi-agent GANs. The generator in E-MGAN employs multiple generators and is penalized by a classifier. This integration guarantees that the proposed model not only enhances the quality of generated samples but also improves the diversity of generated samples to avoid the mode collapse problem. Moreover, extensive experiments are conducted on both a synthetic dataset and two large-scale real-world datasets. The generated samples are visualized for qualitative evaluation. The inception score (IS) and Fréchet inception distance (FID) are adopted to measure the performance of the model for quantitative assessment. The results confirmed that the proposed model achieves outstanding performances compared to other state-of-the-art GAN variants.

The Role of Structure-to-Property-Relationships in Materials Characterization

1999 ◽  
Vol 591 ◽  
Author(s):  
W. Morgner
Keyword(s):  
Cast Iron ◽  
Material Characterization ◽  
Ball Bearing ◽  
Bearing Steel ◽  
Materials Characterization ◽  
Characterization Of Materials ◽  
Nondestructive Characterization ◽  
Ball Bearing Steel

ABSTRACTThe paper deals with questions concerning the material characterization for steels in the field of engineering and metallurgy. Based on the structure-to-property-relationships, a procedure is proposed to strengthen the systematical development of methods for nondestructive characterization of materials. The state of the nondestructive characterization of metals is reviewed and applications are described in which adequate macroscopic physical properties are measured in order to characterize the materials state and properties nondestructively. The materials characterization of ball bearing steel and cast iron using multiparametrical approaches is discussed in detail.

Micro-Raman Spectroscopy for the Characterization of Materials in Electronic and Photonic Devices

1997 ◽  
Vol 3 (S2) ◽  
pp. 843-844
Author(s):  
David D.Tuschel
Keyword(s):  
Raman Spectroscopy ◽  
Photonic Devices ◽  
Materials Characterization ◽  
Selection Rules ◽  
Micro Raman Spectroscopy ◽  
Optical And Electronic Properties ◽  
Oriented Polycrystalline ◽  
Characterization Of Materials ◽  
Non Destructive

Materials characterization is the primary application of macro- and micro-Raman spectroscopy in our laboratory. Specifically, we wish to correlate chemical bonding and short to long range translational symmetry (including amorphous, highly oriented, polycrystalline, and single crystal materials) to physical, optical and electronic properties of materials and devices. Raman spectroscopy is particularly useful in this capacity because of its origin in the vibrational motions of chemically bonded atoms and its dependence upon crystal symmetry through the polarization selection rules. Furthermore, the high spatial resolution and non-destructive nature of micro-Raman spectroscopy make it ideal for in situcharacterization of electronic and photonic devices. We will present results of materials characterization studies, performed using macro- and micro-Raman spectroscopy, of electronic and photonic devices. In addition, we will discuss how the Raman polarization selection rules can be advantageously applied to device characterization.A primary area of investigation involves the study of ion-implanted and annealed Si by Raman spectroscopy.

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