scholarly journals Generative Design of Stable Semiconductor Materials Using Deep Learning And DFT

2022 ◽  
Author(s):  
Edirisuriya Siriwardane ◽  
Yong Zhao ◽  
Indika Perera ◽  
Jianjun Hu
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Density Functional ◽  
Semiconductor Device ◽  
Wide Bandgap ◽  
Generative Adversarial Network ◽  
Functional Theory ◽  
Adversarial Network ◽  
Bandgap Semiconductors ◽  
Learning Data

Semiconductor device technology has exceptionally developed in complexity since discovering the bipolar transistor. With the rapid advancement of various technologies, semiconductors with distinct properties are essential. Recently, deep-learning, data-mining, and density functional theory (DFT)- based high-throughput calculations were widely performed to discover potential semiconductors for diverse applications. CubicGAN is a generative adversarial network where high-throughput analyses were done to uncover mechanically and dynamically stable materials with the assistance of DFT. In our work, we screened the semiconductors using a binary classifier from materials found from the CubicGAN. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. According to our studies, 12 stable semiconductors were found with a particular class of materials, which we label as AA′MH6. Those are BaNaRhH6, BaSrZnH6, BaCsAlH6, SrTlIrH6, KNaNiH6, NaYRuH6, CsKSiH6, CaScMnH6, YZnMnH6, NaZrMnH6, AgZrMnH6, AgZrMnH6, and ScZnMnH6. It could be shown that AA′MH6 with M=Mn and NaYRuH6 semiconductors have considerably different structural, mechanical, and thermodynamic properties compared to the rest of the AA′MH6 semiconductors. In this study, The maximum bandgap found was approximately 3.3 eV from KNaNiH6, while the minimum bandgap was about 1.3 eV from CaScMnH6. BaNaRhH6, BaCsAlH6, CsKSiH6, KNaNiH6, and NaYRuH6 were identified as wide-bandgap semiconductors, where bandgaps are greater than 2 eV. Furthermore, BaSrZnH6 and KNaNiH6 are a direct bandgap semiconductors, whereas other AA′MH6 semiconductors exhibit indirect bandgaps.

scholarly journals High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network

2019 ◽  
Vol 10 (3) ◽  
pp. 1044 ◽  
Author(s):  
Hao Zhang ◽  
Chunyu Fang ◽  
Xinlin Xie ◽  
Yicong Yang ◽  
Wei Mei ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
High Throughput ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Stability and molecular pathways to the formation of spin defects in silicon carbide

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Elizabeth M. Y. Lee ◽  
Alvin Yu ◽  
Juan J. de Pablo ◽  
Giulia Galli
Keyword(s):  
Silicon Carbide ◽  
Density Functional ◽  
Defect Formation ◽  
Density Functional Theory Calculations ◽  
Wide Bandgap ◽  
Enhanced Sampling ◽  
Functional Theory ◽  
Thermally Activated ◽  
Atomistic Models ◽  
Bandgap Semiconductors

AbstractSpin defects in wide-bandgap semiconductors provide a promising platform to create qubits for quantum technologies. Their synthesis, however, presents considerable challenges, and the mechanisms responsible for their generation or annihilation are poorly understood. Here, we elucidate spin defect formation processes in a binary crystal for a key qubit candidate—the divacancy complex (VV) in silicon carbide (SiC). Using atomistic models, enhanced sampling simulations, and density functional theory calculations, we find that VV formation is a thermally activated process that competes with the conversion of silicon (VSi) to carbon monovacancies (VC), and that VV reorientation can occur without dissociation. We also find that increasing the concentration of VSi relative to VC favors the formation of divacancies. Moreover, we identify pathways to create spin defects consisting of antisite-double vacancy complexes and determine their electronic properties. The detailed view of the mechanisms that underpin the formation and dynamics of spin defects presented here may facilitate the realization of qubits in an industrially relevant material.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals Designing of magnetic MAB phases for energy applications

2021 ◽  
Vol 9 (13) ◽  
pp. 8805-8813
Author(s):  
Chen Shen ◽  
Qiang Gao ◽  
Nuno M. Fortunato ◽  
Harish K. Singh ◽  
Ingo Opahle ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Permanent Magnet ◽  
High Throughput ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Functional Theory ◽  
Energy Applications

Based on high-throughput density functional theory calculations, we performed screening for stable magnetic MAB compounds and predicted potential strong magnets for permanent magnet and magnetocaloric applications.

AN AI-BASED APPROACH TO ENHANCED FRACTURE RESOLUTION IN IMAGE LOGS

2021 ◽  
Author(s):  
James Howard ◽  
◽  
Joe Tracey ◽  
Mike Shen ◽  
Shawn Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Rock Fracture ◽  
Short Interval ◽  
Acoustic Properties ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Learning Neural Network ◽  
Borehole Image

Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.

scholarly journals Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3913 ◽  
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Supervised Learning ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

scholarly journals INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 573-582 ◽  
Author(s):  
S. M. Tilon ◽  
F. Nex ◽  
D. Duarte ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Essential Information ◽  
Urban Scenes ◽  
Adversarial Network ◽  
Collapsed Buildings ◽  
Post Disaster

Abstract. Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.

scholarly journals Improving CBCT Quality to CT Level using Deep‐Learning with Generative Adversarial Network

2020 ◽  
Author(s):  
Yang Zhang ◽  
Ning Yue ◽  
Min‐Ying Su ◽  
Bo Liu ◽  
Yi Ding ◽  
...  
Keyword(s):  
Deep Learning ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer

2020 ◽  
Vol 245 (7) ◽  
pp. 597-605 ◽  
Author(s):  
Tri Vu ◽  
Mucong Li ◽  
Hannah Humayun ◽  
Yuan Zhou ◽  
Junjie Yao
Keyword(s):  
Deep Learning ◽  
Linear Array ◽  
Artifact Removal ◽  
Generative Adversarial Network ◽  
Transducer Array ◽  
Limited Bandwidth ◽  
Adversarial Network ◽  
Array Transducer ◽  
Imaging Speed

With balanced spatial resolution, penetration depth, and imaging speed, photoacoustic computed tomography (PACT) is promising for clinical translation such as in breast cancer screening, functional brain imaging, and surgical guidance. Typically using a linear ultrasound (US) transducer array, PACT has great flexibility for hand-held applications. However, the linear US transducer array has a limited detection angle range and frequency bandwidth, resulting in limited-view and limited-bandwidth artifacts in the reconstructed PACT images. These artifacts significantly reduce the imaging quality. To address these issues, existing solutions often have to pay the price of system complexity, cost, and/or imaging speed. Here, we propose a deep-learning-based method that explores the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to reduce the limited-view and limited-bandwidth artifacts in PACT. Compared with existing reconstruction and convolutional neural network approach, our model has shown improvement in imaging quality and resolution. Our results on simulation, phantom, and in vivo data have collectively demonstrated the feasibility of applying WGAN-GP to improve PACT’s image quality without any modification to the current imaging set-up. Impact statement This study has the following main impacts. It offers a promising solution for removing limited-view and limited-bandwidth artifact in PACT using a linear-array transducer and conventional image reconstruction, which have long hindered its clinical translation. Our solution shows unprecedented artifact removal ability for in vivo image, which may enable important applications such as imaging tumor angiogenesis and hypoxia. The study reports, for the first time, the use of an advanced deep-learning model based on stabilized generative adversarial network. Our results have demonstrated its superiority over other state-of-the-art deep-learning methods.

scholarly journals Deep learning and density-functional theory

2019 ◽  
Vol 100 (2) ◽  
Author(s):  
Kevin Ryczko ◽  
David A. Strubbe ◽  
Isaac Tamblyn
Keyword(s):  
Density Functional Theory ◽  
Deep Learning ◽  
Density Functional ◽  
Functional Theory
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