scholarly journals Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhao Fan ◽  
Evan Ma
Keyword(s):  
Deep Learning ◽  
Mechanical Response ◽  
Materials Science ◽  
Learning Algorithm ◽  
Amorphous Solids ◽  
Valuable Insight ◽  
Structure Property ◽  
Three Dimensional Model ◽  
Static Structure ◽  
Structure Representation

AbstractIt has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotationally non-invariant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural network (CNN), a powerful deep learning algorithm, leads to unprecedented accuracy for identifying atoms with high propensity for shear transformations (i.e., plastic susceptibility), solely from the static structure in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses.

scholarly journals Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lijian Zhang ◽  
Guangfu Liu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Rate Distortion ◽  
Modeling Method ◽  
Experimental Results ◽  
Image Modeling ◽  
3D Image ◽  
Three Dimensional Model ◽  
Image Shape

Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extraction ceramic image shape edge profile, again, according to the image edge extraction and elliptic rotator ceramics phenomenon. The image distortion effect was optimized by self-application, and then the deep learning modeler was used to model the side edge contour. Finally, the 3D ceramic model of the rotating body was restored according to the intersection and central axis of the extracted contour. By studying the existing segmentation methods based on deep learning, the automatic segmentation of target ceramic image and the effect of target edge refinement and optimization are realized. After extracting and separating the target ceramics from the image, we processed the foreground image of the target into a three-dimensional model. In order to reduce the complexity of the model, a 3D contextual sequencing model is adopted to encode the hidden space features along the channel dimensions, to extract the causal correlation between channels. Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.

scholarly journals A Size-Matching Strategy to Differentiate Flexible Conformers for the Discovery of Novel Cages with Twin Cavities

2021 ◽  
Author(s):  
Zhenyu Yang ◽  
Chunyang Yu ◽  
Lihua Chen ◽  
Pan Li ◽  
Jiaolong Chen ◽  
...  
Keyword(s):  
Model Compound ◽  
Materials Science ◽  
Three Dimensional ◽  
Proof Of Concept ◽  
Structure Property ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
X Ray ◽  
Conformational Isomers ◽  
Matching Strategy

<p>Considerable efforts have been made to characterize the meaningful conformers that a molecule can adopt, which is of great significance for understanding the structure-property correlation in the fileds of molecular biology, drug discovery, catalysis, materials science, etc. It is however challenging to differentiate and make use of the flexible conformers in solution, as they often experience rapid interconversion due to low isomerization barrier. We herein present a novel yet simple size-matching strategy for conformation identification. As a proof of concept, we rationally designed a three-dimensional model compound, namely hexaformyl molecule <b>1 </b>exhibiting two types of conformers, <i>i.e.</i> <b>Conformer-1</b> and -<b>2</b> with different cleft positions and sizes. Aided by DFT calculations, we selected two triamino conformation capturers (denoted CC). Small-sized <b>CC-1</b> selectively captured <b>Conformer-1</b> by matching its cleft size, while large-sized <b>CC-2</b> was able to match and capture both conformers. It therefore allowed facile differentiation of the two conformations by conventional NMR and X-ray analyses. These two erstwhile inverconverting and indistinguishable conformational isomers were made use of, leading to the discovery of two novel configurational isomers, namely two novel cage-like compounds with twin cavities, one exhibiting a sandglass-shaped and another with a dumbbell form, which we coined <i>diphane.</i></p>

scholarly journals A Size-Matching Strategy to Differentiate Flexible Conformers for the Discovery of Novel Cages with Twin Cavities

2021 ◽  
Author(s):  
Zhenyu Yang ◽  
Chunyang Yu ◽  
Lihua Chen ◽  
Pan Li ◽  
Jiaolong Chen ◽  
...  
Keyword(s):  
Model Compound ◽  
Materials Science ◽  
Three Dimensional ◽  
Proof Of Concept ◽  
Structure Property ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
X Ray ◽  
Conformational Isomers ◽  
Matching Strategy

<p>Considerable efforts have been made to characterize the meaningful conformers that a molecule can adopt, which is of great significance for understanding the structure-property correlation in the fileds of molecular biology, drug discovery, catalysis, materials science, etc. It is however challenging to differentiate and make use of the flexible conformers in solution, as they often experience rapid interconversion due to low isomerization barrier. We herein present a novel yet simple size-matching strategy for conformation identification. As a proof of concept, we rationally designed a three-dimensional model compound, namely hexaformyl molecule <b>1 </b>exhibiting two types of conformers, <i>i.e.</i> <b>Conformer-1</b> and -<b>2</b> with different cleft positions and sizes. Aided by DFT calculations, we selected two triamino conformation capturers (denoted CC). Small-sized <b>CC-1</b> selectively captured <b>Conformer-1</b> by matching its cleft size, while large-sized <b>CC-2</b> was able to match and capture both conformers. It therefore allowed facile differentiation of the two conformations by conventional NMR and X-ray analyses. These two erstwhile inverconverting and indistinguishable conformational isomers were made use of, leading to the discovery of two novel configurational isomers, namely two novel cage-like compounds with twin cavities, one exhibiting a sandglass-shaped and another with a dumbbell form, which we coined <i>diphane.</i></p>

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Dan Luo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Supervised Learning ◽  
Image Recognition ◽  
Production Efficiency ◽  
Learning Algorithm ◽  
Electronic Components ◽  
Electron Device ◽  
Actual Recognition ◽  
The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

Sign in / Sign up

Export Citation Format

Share Document