Recommendation System for Material Scientists Based on Deep Learn Neural Network

Kliuev, Andrei; Klestov, Roman; Bartolomey, Maria; Рогожников, А. Г.

doi:10.1007/978-3-030-02351-5_26

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Recent advances in DNN and specifically in GANs have enabled innovations in creating a new image or composing a symphony . This discovery paradigm can be applied to various materials and provided thoughtful guidance to the synthesis of new materials . GAN is one of the nonparametric approaches for deep generative models initially proposed by Goodfellow et al The generative models can be used to create plausible molecular structures for high‐throughput screening, which is the first step in molecular discovery.…”

Section: Molecular Discoveries Using Gansmentioning

confidence: 99%

“…[154][155][156] This discovery paradigm can be applied to various materials and provided thoughtful guidance to the synthesis of new materials. 51,[157][158][159][160] GAN is one of the nonparametric approaches for deep generative models initially proposed by Goodfellow et al 45 The generative models can be used to create plausible molecular structures for high-throughput screening, which is the first step in molecular discovery. Generative models such as GAN can illuminate property-structure correlations and use them to guide the molecular designs.…”

Section: Molecular Discoveries Using Gansmentioning

confidence: 99%

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Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang

Hou

Jiang

et al. 2019

WIREs Comput Mol Sci

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Artificial intelligence (AI) has been referred to as the "fourth paradigm of science," and as part of a coherent toolbox of data-driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state-of-the-art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML-based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

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Section: Molecular Discoveries Using Gansmentioning

confidence: 99%

Section: Molecular Discoveries Using Gansmentioning

confidence: 99%

Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang

Hou

Jiang

et al. 2019

WIREs Comput Mol Sci

View full text Add to dashboard Cite

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Stability of the Deep Neural Networks Learning Process in the Recognition Problems of the Material Microstructure

Klyuev

Stolbov

Gitman

et al. 2019

Integrated Science in Digital Age

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Deep learning based identification and interpretability research of traditional village heritage value elements: a case study in Hubei Province

Tan,

Zhu,

Chen

2024

Herit Sci

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The preservation and transmission of traditional villages is crucial to the prosperity and development of ethnic cultures. However, current traditional village surveys usually require a large number of experts and scholars to conduct field research, which is an expensive and time-consuming method, especially for large-scale tasks. Therefore, this study proposes an automatic classification method based on deep learning (DL) for the identification of traditional village heritage value elements (TVHVE). The study evaluates four selected convolutional neural network (CNN) frames using traditional villages in Hubei Province as a sample dataset. The results show that Residual Network152 (ResNet152) is the most suitable CNN frame for identifying TVHVE in Hubei. The stability and consistency of various TVHVE present in the ResNet152 model were evaluated using Area Under Curve (AUC) and Precision Recall Curve (PRC), which indicated satisfactory prediction performance for most elements, except for specific elements such as tombstones and stone carvings, which showed lower accuracy. In addition, the study sheds light on the areas of concern of the model with respect to different TVHVE images and elucidates the reasons behind the confusion between elements through semantic clustering based on image classification and interpretability analysis using the Gradient-Weighted Class Activation Mapping (Grad-CAM) heat map. By using an automated classification method based on DL, this study significantly reduces the cost and effort associated with traditional surveys. At the same time, insight into areas of concern and confusion in the model improves guidance for conservation efforts and provides valuable references for subsequent research.

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Recommendation System for Material Scientists Based on Deep Learn Neural Network

Cited by 6 publications

References 12 publications

Machine learning and artificial neural network accelerated computational discoveries in materials science

Machine learning and artificial neural network accelerated computational discoveries in materials science

Stability of the Deep Neural Networks Learning Process in the Recognition Problems of the Material Microstructure

Deep learning based identification and interpretability research of traditional village heritage value elements: a case study in Hubei Province

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