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Deep generative learning for exploration in large electrochemical impedance dataset

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 126卷

作者： Doonyapisut, Dulyawat Kim, Byeongkyu Kim, Jung Kyu Lee, Eunseok Chung, Chan-Hwa Sungkyunkwan Univ Sch Chem Engn Suwon 16419 South Korea Sungkyunkwan Univ Coll Comp & Informat Suwon 16419 South Korea

The fields of energy storage, photocatalysis, and sensors have undergone substantial technological advancements, which have led to the generation of vast amounts of data on electrochemical impedance (EIS). The interpretation of large amounts of EIS data is a challenging task since the analysis of EIS data requires multiple steps to get a suitable equivalent circuit. Recently, some progress has been made in the machine learning (ML) model for EIS classification. However, most of the ML models are performed as a "black box" model, which provides only the classification result and lacks physical descriptor representation. Here, we apply variational autoencoders (VAE) to EIS data analysis, which includes classification, parameter prediction, and the visualization of physical descriptors. The VAE model performed well in the classification task, with an accuracy of 82.0%-92.4%. In the prediction task, VAE shows a high R-squared value on the Randles circuit. Additionally, the VAE model can map physical descriptors to the latent space, allowing the latent space to transform into a property space, which plays an important role in the optimization and exploration of novel materials research.

关键词： Electrochemical impedance spectroscopy Equivalent circuit model variational autoencoder Impedance optimization Impedance visualization

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Progressive generation of 3D point clouds with hierarchical consistency

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PATTERN RECOGNITION 2023年 136卷

作者： Li, Peipei Liu, Xiyan Huang, Jizhou Xia, Deguo Yang, Jianzhong Lu, Zhen Beijing Univ Posts & Telecommun Sch Artificial Intelligence 10 Xitucheng Rd Beijing 100876 Peoples R China Baidu Inc Baidu Technol Pk Bldg 210 Xibeiwang East Rd Beijing 100193 Peoples R China

Generating 3D point cloud directly from latent prior (e.g., Gaussian distribution) plays a vital role in the representation learning and data augmentation in 3D vision tasks. Since point cloud is formed by irregu-lar points, the generation process of point cloud requires rich semantic information, yet few studies are devoted to it. In this paper, we recast this generation task as a progressive learning problem to model the two-level hierarchy of distributions and address it by proposing a novel model called hierarchical consis-tency variational autoencoder (HC-VAE). This framework introduces a hierarchical consistent mechanism (HCM) to model the shape consistency and the pointwise representation consistency in a complementary manner. Specifically, we propose a stackable encoder-decoder framework and constrain the generation quality progressively to ensure that the underlying shape and fine-grained parts can be reconstructed with high fidelity. Additionally, given the progressively generated intermediate point cloud instances, a hierarchical-positive contrastive loss is introduced to learn the point-distribution-free instance represen-tations to avoid explicitly parametrizing the distribution of points in a shape. In this way, our model suffices to generate diverse, high-resolution, and uniform point cloud instances. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance in point cloud gen-eration.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： 3D Point cloud generation Point cloud analysis Generative adversarial networks variational autoencoder Hierarchical consistency

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Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第3期4卷 035018-035018页

作者： Templin, Thomas Memarzadeh, Milad Vinci, Walter Lott, P. Aaron Asanjan, Ata Akbari Armenakas, Anthony Alexiades Rieffel, Eleanor NASA Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA NASA Univ Space Res Assoc Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA HP SCDS Leon 24009 Spain NASA Univ Space Res Assoc Quantum Artificial Intelligence Lab Ames Res Ctr Moffett Field CA 94035 USA Harvard Univ Dept Phys Cambridge MA 02138 USA NASA Ames Res Ctr Quantum Artificial Intelligence Lab Moffett Field CA 94035 USA

Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models-variational autoencoders (VAEs) with Gaussian, Bernoulli, and Boltzmann priors-in detecting anomalies in multivariate time series of commercial-flight operations. We created two VAE models with discrete latent variables (DVAEs), one with a factorized Bernoulli prior and one with a restricted Boltzmann machine (RBM) with novel positive-phase architecture as prior, because of the demand for discrete-variable models in machine-learning applications and because the integration of quantum devices based on two-level quantum systems requires such models. To the best of our knowledge, our work is the first that applies DVAE models to anomaly-detection tasks in the aerospace field. The DVAE with RBM prior, using a relatively simple-and classically or quantum-mechanically enhanceable-sampling technique for the evolution of the RBM's negative phase, performed better in detecting anomalies than the Bernoulli DVAE and on par with the Gaussian model, which has a continuous latent space. The transfer of a model to an unseen dataset with the same anomaly but without re-tuning of hyperparameters or re-training noticeably impaired anomaly-detection performance, but performance could be improved by post-training on the new dataset. The RBM model was robust to change of anomaly type and phase of flight during which the anomaly occurred. Our studies demonstrate the competitiveness of a discrete deep generative model with its Gaussian counterpart on anomaly-detection problems. Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum anneale

关键词： generative modeling deep learning variational autoencoder anomaly detection restricted Boltzmann machine Gibbs sampling quantum-assisted machine learning

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Slice-to-voxel stochastic reconstructions on porous media with hybrid deep generative model

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COMPUTATIONAL MATERIALS SCIENCE 2021年 186卷

作者： Zhang, Fan Teng, Qizhi Chen, Honggang He, Xiaohai Dong, Xiucheng Sichuan Univ Coll Elect & Informat Engn Chengdu Peoples R China Xihua Univ Sch Elect Engn & Elect Informat Chengdu Peoples R China Minist Educ Key Lab Wireless Power Transmiss Chengdu Peoples R China

Three-dimensional (3D) microstructures are useful for studying the spatial structures and physical properties of porous media. A number of stochastic reconstructions are essential to discover the geometry and topology of the porous media and the its flow behavior. While several deep-learning based generative models have been proposed to deal with the issue, the obstacles about stable training and difficulty in convergence limit the application of these models. To address these problems, a hybrid deep generative model for 3D porous media reconstruction is proposed. The hybrid model is composed of a variant autoencoder (VAE) and a generative adversarial network (GAN). It receives a two-dimensional image as input and generates 3D porous media. The encoder from VAE characterizes the statistical and morphological information of input image and generates a low-dimensional feature vector for generator. Benefiting from the hybrid model, the training becomes more stable and the generative capability is enhanced as well. Furthermore, a simple but useful loss function is used to help improve accuracy. The proposed model is tested on both isotropic and anisotropic porous media. The results show the synthetic realizations have good agreement to the targets on visual inspection, statistical functions and two-phase flow simulation.

关键词： Porous media 3D reconstruction Generative adversarial networks variational autoencoder Hybrid deep generative model

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Robust errant beam prognostics with conditional modeling for particle accelerators

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第1期5卷 015044-015044页

作者： Rajput, Kishansingh Schram, Malachi Blokland, Willem Alanazi, Yasir Ramuhalli, Pradeep Zhukov, Alexander Peters, Charles Vilalta, Ricardo Thomas Jefferson Natl Accelerator Facil Newport News VA 23606 USA Oak Ridge Natl Lab Oak Ridge 37830 TN USA Univ Houston Dept Comp Sci Houston TX 77204 USA

Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data's variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

关键词： anomaly prediction siamese model variational autoencoder conditional SNS spallational neutron source particle accelerators

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Data-Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases

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ADVANCED ENERGY MATERIALS 2023年第40期13卷

作者： Rajagopal, Deepalaxmi Koeppe, Arnd Esmaeilpour, Meysam Selzer, Michael Wenzel, Wolfgang Stein, Helge Nestler, Britta Karlsruhe Inst Technol KIT Inst Appl Mat IAM MMS Str Forum 7 D-76131 Karlsruhe Germany Karlsruhe Inst Technol KIT Inst Nanotechnol INT Hermann Von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Helmholtz Inst Ulm Lise Meitner Str 16 D-89081 Ulm Germany

Solid electrolyte interphases (SEIs) form as reduction products at the electrodes and strongly affect battery performance and safety. Because SEI formation poses a highly nonlinear, complex multi-physics problem over various lengths and time scales, traditional modeling approaches struggle to characterize SEI evolution solely with existing physical properties. To improve the characterization of SEIs, it proposes a data-driven strategy for a virtual material design that learns to represent and characterize SEI formation with physical and data-driven properties from kinetic Monte Carlo simulations. A variational autoencoder with a property regressor learns data-driven properties, which represent SEI configurations and correlate with physical target properties. This new neural network design encodes the high-dimensional structural and reaction spaces into a lower-dimensional latent space, while the property regressor orders the latent space by physical target properties. The model achieves high correlation scores between target and predicted properties from latent representations, thereby proving that the data-driven properties enrich the expressiveness of SEI characterizations. A recent study has developed a parallel regressor variational autoencoder tool that can automate the characterization of solid electrolyte interphase (SEI) in batteries. This tool can synthesize and explain SEI configurations by analyzing data-driven properties encoded with physical properties. The study also investigates the impact of reaction barriers on SEI ***

关键词： kinetic Monte Carlo simulations solid electrolyte interphases variational autoencoder virtual material design

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Deep generative fuel design in low data regimes via multi-objective imitation

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CHEMICAL ENGINEERING SCIENCE 2023年 274卷

作者： Liu, Yifan Liu, Runze Duan, Jinyu Wang, Li Zhang, Xiangwen Li, Guozhu Tianjin Univ Sch Chem Engn & Technol Key Lab Green Chem Technol Minist Educ Tianjin 300072 Peoples R China Collaborat Innovat Ctr Chem Sci & Engn Tianjin Tianjin 300072 Peoples R China Haihe Lab Sustainable Chem Transformat Tianjin 300192 Peoples R China

Commercial fuel discovery faces a constantly decreasing return of investment due to due to increasingly tight environmental criteria and reducing potential uses for each new fuel. In this paper, a deep generative model, termed Latent Interspace Generative Adversarial Network with a Domain of Stacking (LIGANDS), has been established to screen desired fuel molecules in the large chemical space without setting design rules manually. A variational autoencoder, a generative adversarial network and a stacking model are well integrated in LIGANDS through model convergence. Given only the structures of 255 typical highenergy???density fuels in low data regimes, LIGANDS generated 3461 new fuel molecules with similar property distribution and improved energy performance as the qualified candidates of next-generation fuels. To expand and enrich the fuel-relevant chemical space with innovative molecular entities on demand, in-depth multi-objective imitation on the key properties of target fuel is realized by LIGANDS through optimizing generative molecular structures and their distribution. ?? 2023 Elsevier Ltd. All rights reserved.

关键词： Deep learning Generative model Fuel variational autoencoder Generative adversarial network Ensemble learning

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Data-Driven Discovery of 2D Materials for Solar Water Splitting

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FRONTIERS IN MATERIALS 2021年 8卷

作者： Agarwal, Abhishek Goverapet Srinivasan, Sriram Rai, Beena Tata Consulatncy Serv Ltd TCS Res Pune Maharashtra India

Hydrogen economy, wherein hydrogen is used as the fuel in the transport and energy sectors, holds significant promise in mitigating the deleterious effects of global warming. Photocatalytic water splitting using sunlight is perhaps the cleanest way of producing the hydrogen fuel. Among various other factors, widespread adoption of this technology has mainly been stymied by the lack of a catalyst material with high efficiency. 2D materials have shown significant promise as efficient photocatalysts for water splitting. The availability of open databases containing the "computed" properties of 2D materials and advancements in deep learning now enable us to do "inverse" design of these 2D photocatalysts for water splitting. We use one such database (Jain et al., ACS Energ. Lett. 2019, 4, 6, 1410-1411) to build a generative model for the discovery of novel 2D photocatalysts. The structures of the materials were converted into a 3D image-based representation that was used to train a cell, a basis autoencoder and a segmentation network to ascertain the lattice parameters as well as position of atoms from the images. Subsequently, the cell and basis encodings were used to train a conditional variational autoencoder (CVAE) to learn a continuous representation of the materials in a latent space. The latent space of the CVAE was then sampled to generate several new 2D materials that were likely to be efficient photocatalysts for water splitting. The bandgap of the generated materials was predicted using a graph neural network model while the band edge positions were obtained via empirical correlations. Although our generative modeling framework was used to discover novel 2D photocatalysts for water splitting reaction, it is generic in nature and can be used directly to discover novel materials for other applications as well.

关键词： generative modeling variational autoencoder inverse design photocatalysts water splitting reaction

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Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing

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APPLIED SCIENCES-BASEL 2024年第17期14卷

作者： Pan, Jianguo Dong, Zhengyang Yan, Lijun Cai, Xia Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China

Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students' historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students' knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using variational autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets.

关键词： knowledge graph programming learning variational autoencoder knowledge tracing LSTM

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Incremental learning model for dynamical identification and classification of abnormal vibration in operational underground facilities

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年 140卷

作者： Chai, Fu Zhou, Biao Xie, Xiongyao Zhang, Zixin Wang, Chen Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China Tongji Univ Dept Geotech Engn Shanghai 200092 Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai 200072 Peoples R China

Underground infrastructures are rapidly growing in size and complexity. However, their operations are affected by several hazards, including hidden structural deterioration and effects of random external constructions. Dynamic monitoring of these hazards is essential to provide early warning. We propose a vibration-based self-supervised incremental learning model for dynamic monitoring of emerging operational threats by recognizing abnormal responses. The model comprises teacher and student models based on a variational autoencoder (VAE) with a metric function. When the teacher model detects a new category of abnormal vibration, the student model is trained to recognize this abnormality through sample rehearsals and knowledge distillations. Subsequently, it becomes the teacher model for the next round of incremental learning. We demonstrate through a case study that catastrophic forgetting can be avoided and memory consumption can be reduced during dynamic network updates. Moreover, the use of a metric function in the VAE increases the vibration identification accuracy.

关键词： Underground infrastructure Monitoring Incremental learning variational autoencoder Metric function

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