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Enhanced Simulation of Complicated MXene Materials with graph convolutional neural networks

CHEMPHYSCHEM 2025年第6期26卷 e202400749页

作者： Chen, Xin Wan, Zicheng Lao, Sisi Tian, Ziqi UCLA Dept Phys & Astron Los Angeles CA 90095 USA Chinese Acad Sci Ningbo Inst Mat Technol & Engn Ningbo 315201 Peoples R China Ningbo Univ Sch Mat Sci & Chem Engn Ningbo 313001 Peoples R China

MXene, a notable two-dimensional transition metal carbide, has attracted increasing attention in materials science due to its unique attributes, driving innovations in energy storage, sensors, catalysts, and electromagnetic shielding. The property and application performance are determined by the electronic structure, which can be described based on the density of states (DOS). The conventional density functional theory (DFT) calculation is able to provide the DOS spectrum of a specific atomic structure. However, for complicated composition, such as the recently reported high entropy MXene, the DFT calculations in exhaustive structure space are resource-intensive. In this study, machine learning (ML) technique, specifically the crystal graph convolutional neural networks (CGCNN) model, is applied to generate DOS of these MXene models with complex compositions. By using calculations on M3C2 and M4C3 structures as training sets, the DOS of the complex high entropy MXene is well reproduced according to the atomic structure. Moreover, the adsorption energy of lithium is precisely predicted based on the DOS, which can be further employed to screen the potential electrode materials for lithium batteries. Herein, ML method not only streamlines predictions but also enhances the understanding of MXene's intrinsic properties.

关键词： MXene Machine Learning graph convolutional neural networks Density of States Li-ion Battery

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Application of graph convolutional neural networks and multi-sources data on urban functional zones identification, A case study of Changchun, China

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SUSTAINABLE CITIES AND SOCIETY 2025年 119卷

作者： Wang, Siyu Zhao, Chunhong Jiang, Qunou Zhu, Di Ma, Jun Sun, Yunxiao Tianjin Chengjian Univ Sch Geol & Geomat Tianjin Peoples R China Northeast Normal Univ Sch Geog Sci Key Lab Geog Proc & Ecol Secur Changbai Mt Minist Educ Changchun Peoples R China Beijing Forestry Univ Sch Soil & Water Conservat Beijing Peoples R China Univ Minnesota Dept Geog Environm & Soc St Paul MN USA Shanxi Agr Univ Coll Forestry Taigu Shanxi Peoples R China

Urban functional zones (UFZs) identification is integral to comprehensive city management. However, current methods often fail to effectively leverage multiple data sources to enhance identification accuracy and overlook the spatial interconnections between neighboring units. In this study, we employed a graph convolutional neural networks (GCNNs) model to consolidate information from adjacent units and enhance the accuracy of UFZs identification. We specifically integrated street view imagery into our methodology to extract features from ground scenes. Furthermore, we conducted a co-occurrence analysis, correlating these visual features with socioeconomic characteristics. With Changchun as a case study, the results indicate that 1) our proposed framework exhibits robust performance, achieving an accuracy of 96 % on the test set and a visual interpretation accuracy of 78 %;2) the integration of street view imagery effectively addresses gaps in social sensing data features. Notably, the inclusion of ground scene features bolster the identification accuracy of residential and industrial areas by approximately 8 % and 16 %, respectively;3) relative to other frequently utilized classification models, the graph convolutional model enhances the accuracy of UFZs identification by 11.2 %-16.6 %. Consequently, our framework effectively identifies UFZs, offering innovative methods and substantial data support for governmental bodies and urban planning authorities.

关键词： Urban functional zones graph convolutional neural networks (GCNNs) Multi-sources data fusion Deep learning

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Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks

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COMPUTATIONAL MECHANICS 2025年第3期75卷 1115-1135页

作者： Kneifl, Jonas Fehr, Joerg Brunton, Steven L. Kutz, J. Nathan Univ Stuttgart Inst Engn & Computat Mech D-70569 Stuttgart Germany Univ Washington Dept Mech Engn Seattle WA 98195 USA Univ Washington Dept Appl Math & Elect & Comp Engn Seattle WA 98195 USA

Highly nonlinear dynamic finite element simulations using explicit time integration are particularly valuable tools for structural analysis in fields like automotive, aerospace, and civil engineering, or in the study of injury biomechanics. However, such state-of-the-art simulation models demand significant computational resources. Conventional data-driven surrogate modeling approaches address this by evolving the dynamics on low-dimensional embeddings, yet the majority of them operate directly on high-resolution data obtained from numerical discretizations, making them costly and unsuitable for adaptive resolutions or for handling information flow over large spatial distances. We therefore propose a multi-hierarchical framework for the structured creation of a series of surrogate models at different resolutions. Macroscale features are captured on coarse surrogates, while microscale effects are resolved on finer ones, while leveraging transfer learning to pass information between scales. The objective of this study is to develop efficient surrogates for a kart frame model in a frontal impact scenario. To achieve this, its mesh is simplified to obtain multi-resolution representations of the kart. Subsequently, a graph-convolutional neural network-based surrogate learns parameter-dependent low-dimensional latent dynamics on the coarsest representation. Following surrogates are trained on residuals using finer resolutions, allowing for multiple surrogates with varying hardware requirements and increasing accuracy.

关键词： Model order reduction Crash simulations Multiscale modeling Surrogate modeling graph convolutional neural networks

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graph convolutional neural networks for Nuclei Segmentation from Histopathology Images 4th

Graph Convolutional Neural Networks for Nuclei Segmentation ...

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4th International Conference on Soft Computing and its Engineering Applications (icSoftComp)

作者： Damania, Karishma Jothi, J. Angel Arul Birla Inst Technol & Sci Dept Comp Sci Pilani Rajasthan India Dubai Int Acad City Dubai Campus Dubai U Arab Emirates

ISBN: (纸本)9783031276088;9783031276095

The analysis of hematoxylin and eosin (H&E) stained images obtained from breast tissue biopsies is one of the most dependable ways to obtain an accurate diagnosis for breast cancer. Several deep learning methods have been explored to perform the analysis of breast histopathology images to automate and improve the efficiency of computer aided diagnoses (CAD) of breast cancer. Cell nuclei segmentation is one of the predominant tasks of a CAD system. Traditional convolutional neural networks (CNNs) have seen remarkable success when it comes to this task. However, there has been recent success suggested by several studies that made use of graph convolutional neural networks (GCNN) with computer vision tasks. This paper aimed to design an architecture called graphSegNet, a GCNN that follows the encoder-decoder structure of the well-known network for image segmentation, SegNet to perform nuclei segmentation. The proposed model achieved an accuracy, Dice coefficient, Intersection over Union (IoU), precision and recall scores of 90.75%, 83.74%, 83.2%, 75.40% and 79.93% respectively on a UCSB bio-segmentation benchmark breast histopathology dataset.

关键词： Deep learning graph convolutional neural networks Image segmentation Cell nuclei segmentation graphSegNet

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graph convolutional neural networks with node transition probability-based message passing and DropNode regularization

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 174卷 114711-114711页

作者： Do, Tien Huu Nguyen, Duc Minh Bekoulis, Giannis Munteanu, Adrian Deligiannis, Nikos Vrije Univ Brussel Pl Laan 2 B-1050 Brussels Belgium IMEC Kapeldreef 75 B-3001 Leuven Belgium

graph convolutional neural networks (GCNNs) have received much attention recently, owing to their capability in handling graph-structured data. Among the existing GCNNs, many methods can be viewed as instances of a neural message passing motif;features of nodes are passed around their neighbors, aggregated and transformed to produce better nodes' representations. Nevertheless, these methods seldom use node transition probabilities, a measure that has been found useful in exploring graphs. Furthermore, when the transition probabilities are used, their transition direction is often improperly considered in the feature aggregation step, resulting in an inefficient weighting scheme. In addition, although a great number of GCNN models with increasing level of complexity have been introduced, the GCNNs often suffer from over-fitting when being trained on small graphs. Another issue of the GCNNs is over-smoothing, which tends to make nodes' representations indistinguishable. This work presents a new method to improve the message passing process based on node transition probabilities by properly considering the transition direction, leading to a better weighting scheme in nodes' features aggregation compared to the existing counterpart. Moreover, we propose a novel regularization method termed DropNode to address the over-fitting and over-smoothing issues simultaneously. DropNode randomly discards part of a graph, thus it creates multiple deformed versions of the graph, leading to data augmentation regularization effect. Additionally, DropNode lessens the connectivity of the graph, mitigating the effect of over-smoothing in deep GCNNs. Extensive experiments on eight benchmark datasets for node and graph classification tasks demonstrate the effectiveness of the proposed methods in comparison with the state of the art.

关键词： graph convolutional neural networks graph classification Node classification Geometric deep learning

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Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions

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GEOINFORMATICA 2022年第4期26卷 645-676页

作者： Zhu, Di Liu, Yu Yao, Xin Fischer, Manfred M. Univ Minnesota Dept Geog Environm & Soc Minneapolis MN 55455 USA Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing Peoples R China Alibaba Grp Beijing Peoples R China Vienna Univ Econ & Business Dept Socioecon Vienna Austria

Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution - commonly known as filters or kernels - in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front

关键词： Spatial regression graph convolutional neural networks Deep learning GeoAI Social sensing

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NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 257卷 108435-108435页

作者： Deng, Boyang Tian, Yu Zhang, Qi Wang, Yangyang Chai, Zhenxin Ye, Qiancheng Yao, Shang Liang, Tingbo Li, Jingsong Zhejiang Univ Coll Biomed Engn & Instrument Sci Engn Res Ctr EMR & Intelligent Expert Syst Minist Educ 38 Zheda Rd Hangzhou 310027 Peoples R China Zhejiang Lab Res Ctr Data Hub & Secur Hangzhou 311100 Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Sch Med Hangzhou 310003 Peoples R China MOE Joint Int Res Lab Pancreat Dis Hangzhou Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Sch Med Hangzhou Peoples R China Zhejiang Canc Hosp Zhejiang Clin Res Ctr Hepatobiliary & Pancreat Dis Hangzhou 310022 Zhejiang Peoples R China

Background and Objective: Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to have high prognostic value in HCC, its application in clinical prognosis prediction requires specialized knowledge and complex calculations, which poses challenges for clinicians. It would be of interest to develop a model to help clinicians make full use of micronecrosis to assess patient survival. Methods: To address these challenges, we propose a HCC prognosis prediction model that integrates pathological micronecrosis information through graph convolutional neural networks (GCN). This approach enables GCN to utilize micronecrosis, which has been shown to be highly correlated with prognosis, thereby significantly enhancing prognostic stratification quality. We developed our model using 3622 slides from 752 patients with primary HCC from the FAH-ZJUMS dataset and conducted internal and external validations on the FAH-ZJUMS and TCGA-LIHC datasets, respectively. Results: Our method outperformed the baseline by 8.18% in internal validation and 9.02% in external validations. Overall, this paper presents a deep learning research paradigm that integrates HCC micronecrosis, enhancing both the accuracy and interpretability of prognostic predictions, with potential applicability to other pathological prognostic markers. Conclusions: This study proposes a composite GCN prognostic model that integrates information on HCC micronecrosis, collecting large dataset of HCC histopathological images. This approach could assist clinicians in analyzing HCC patient survival and precisely locating and visualizing necrotic tissues that affect prognosis. Following the research paradigm outlined in this paper, other prognostic biomarker integration models with GCN could be developed, significantly enhancing the predictive performance and interpre

关键词： Survival analysis graph convolutional neural networks Hepatocellular carcinoma Prognosis Biomarkers Micronecrosis

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Phase-Locking Value Based graph convolutional neural networks for Emotion Recognition

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IEEE ACCESS 2019年 7卷 93711-93722页

作者： Wang, Zhongmin Tong, Yue Heng, Xia Xian Univ Posts & Telecommun Sch Comp Sci & T Imol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China

Recognition of discriminative neural signatures and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Electroencephalogram (EEG) is a spatial discrete signal. In this paper, in order to extract the spatio-temporal characteristics and the inherent information implied by functional connections, a multichannel EEG emotion recognition method based on phase-locking value (PLV) graph convolutional neural networks (P-GCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is using PLV-based brain network to model multi-channel EEG features as graph signals and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed P-GCNN method uses the PLV connectivity of EEG signals to determine the mode of emotional-related functional connectivity, which is used to represent the intrinsic relationship between EEG channels in different emotional states. On this basis, the neural network is trained to extract effective EEG emotional features. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DEAP dataset. The experimental results demonstrate that novel framework can improve the classification accuracy on both datasets, but not so effective on DEAP as on SEED, in which with 84.35% classification accuracy for SEED, and the average accuracies of 73.31%, 77.03% and 79.20% are, respectively, obtained for valence, arousal, and dominance classifications on the DEAP database.

关键词： EEG emotion recognition phase-locking value graph convolutional neural networks brain network functional connectivity

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graph convolutional neural networks-based assessment of students' collaboration ability

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第28期34卷 e7395-e7395页

作者： Lin, Jinjiao Gao, Tianqi Wen, Yuhua Yu, Xianmiao You, Bizhen Yin, Yanfang Zhao, Yanze Pu, Haitao Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan Peoples R China Shandong Police Coll Legal Teaching & Res Dept Jinan Peoples R China Shandong Univ Sci & Technol Dept Elect Informat Jinan Peoples R China

As 21st-century skills have become increasingly important, collaboration ability is now considered essential in many areas of life. Different theoretical frameworks and assessment tools have emerged to measure this skill. However, more applied studies on its implementation and assessment in current educational settings are required. This research accordingly uses graph convolutional neural networks (GCNs) to assess students' collaboration ability from students' assignments. The Pearson correlation coefficient is used to measure the similarity of the level of students' collaboration ability, and similar students are linked together to establish an adjacency matrix. By sorting through relevant literature and selecting the feature words that represent the strength of collaboration ability, calculating the similarity between the preprocessed student data and each selected feature word, after which the highest value of the similarity as the feature value of the student for this feature and establish the student feature matrix. Finally, the GCNs are jointly trained by the adjacency matrix and the feature matrix. The results show that this method can effectively assess students' collaboration ability. Moreover, compared with other text classification methods, the GCNs selected in this paper has higher accuracy.

关键词： collaboration ability graph convolutional neural networks self-analysis text classification

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Classification of Cancer Types Using graph convolutional neural networks

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FRONTIERS IN PHYSICS 2020年 8卷 203-203页

作者： Ramirez, Ricardo Chiu, Yu-Chiao Hererra, Allen Mostavi, Milad Ramirez, Joshua Chen, Yidong Huang, Yufei Jin, Yu-Fang Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA Univ Texas Hlth San Antonio Greehey Childrens Canc Res Inst San Antonio TX USA Univ Texas Hlth San Antonio Dept Populat Hlth Sci San Antonio TX USA

Background:Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently. Results:In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group).In silicogene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 marker genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific. Conclusion:Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from t

关键词： graph convolutional neural networks cancer classification model TCGA deep learning

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