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Evaluating the Shanghai Typhoon Model against State-of-the-Art Machine-Learning Weather Prediction Models:A Case Study for Typhoon Danas(2025) 认领 引用 被引量：1
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作者 Zeyi NIU Wei HUANG +5 位作者 Yuhua YANG Mengqi YANG Lin DENG Haibo WANG Hong LI Xu ZHANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第4期744-750,共7页
This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and bou... This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models. 展开更多
关键词 Shanghai Typhoon Model(SHTM) machine-learning weather prediction machine learning-physics hybrid model
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Machine-learning models for classification of mudcake thickness in oil-based drilling fluids 认领 引用
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作者 Shadfar Davoodi Grachik Eremyan +2 位作者 Amir H.Mohammedi Mohammed Al-Shargabi Evgeny Burnaev 《Natural Gas Industry B》 2026年第2期206-227,共22页
The performance of oil-based drilling fluids(OBDFs)is critical for maintaining drilling efficiency under conditions in which water-based fluids fail to perform effectively.Mudcake or filtercake thickness(MCT)is a key ... The performance of oil-based drilling fluids(OBDFs)is critical for maintaining drilling efficiency under conditions in which water-based fluids fail to perform effectively.Mudcake or filtercake thickness(MCT)is a key property of OBDFs because it directly influences wellbore stability and the extent of formation damage.Conventional methods for determining MCT rely on manual laboratory measurements that are time-consuming and cannot represent real-time downhole conditions,thereby limiting their value for operational decision-making and motivating the development of reliable predictive methods.This study proposes a novel framework for the near-real-time classification of MCT in OBDF systems.A comprehensive field dataset was compiled from five routinely measured,rapidly acquired laboratory parameters:mud weight,temperature,alkalinity,emulsion stability,and Marsh funnel viscosity.Four machine-learning classifiers-extreme gradient boosting(XGB),support vector machine,extreme learning machine,and multi-layer perceptron-were developed to classify MCT into three operationally meaningful categories:Excellent(≤1.0 mm),Good(1.0-2.2 mm),and Poor(>2.2 mm).The developed models achieved high classification accuracy,with the XGB algorithm demonstrating the most stable and reproducible performance(test accuracy:0.98,F1-score:0.98).Comprehensive evaluation-including receiver operating characteristic analysis,model confidence assessment,learning curves,group K-fold cross-validation,and external validation-confirmed the strong generalization capability of the XGB model,which achieved 94.81% accuracy when applied to completely unseen wells.Interpretability analysis using SHapley Additive exPlanations(SHAP)identified emulsion stability as the dominant predictive feature and revealed that parameter interactions(e.g.,alkalinity-emulsion stability)vary dynamically across different MCT classes.The primary innovation of this study lies in reframing MCT prediction as a multi-class classification problem rather than a traditional regression task,enabling rapid and actionable interpretation of drilling fluid performance.The proposed framework provides a semiautomated,data-driven tool for near-real-time monitoring of MCT in OBDF systems,reducing reliance on slow laboratory measurements and supporting timely operational decisions that improve drilling efficiency and preserve wellbore integrity in complex drilling environments. 展开更多
关键词 Mudcake thickness Oil-based muds Extreme gradient boosting machine-learning modeling Drilling fluids
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DP-EVA:data-efficient fine-tuning framework via maximizing pretrained knowledge of large atomistic models for developing domain-specific machine-learning interatomic potentials 认领 引用
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作者 Zhaoqing Liu Zhe Deng +3 位作者 Huabo Zhao Han Wang Mohan Chen Hong Jiang 《Clean Energy》 EI CSCD 2026年第4期103-116,共14页
Machine-learning interatomic potentials(MLIPs)have greatly extended the temporal and spatial scales of atomistic simulations,enabling the theoretical study of complex processes at affordable computational cost compare... Machine-learning interatomic potentials(MLIPs)have greatly extended the temporal and spatial scales of atomistic simulations,enabling the theoretical study of complex processes at affordable computational cost compared with conventional density functional theory.Recently,large atomistic models(LAMs)have drawn intense interest,as their unified encoders embed extensive chemical knowledge and support fine-tuning methodologies for efficiently adapting models to domain-specific downstream tasks.While many active learning frameworks exist for building MLIP datasets from scratch,dedicated data-generation pipelines for fine-tuning pretrained LAMs remain scarce.Here,we introduce the Deep Potential EVolution Accelerator(DP-EVA)—a data-efficient fine-tuning framework that maximizes the utilization of the pretrained knowledge of LAMs during data generation,accelerating the evolution of domain-specific fine-tuned models with minimal datasets.DP-EVA collects highly representative data through a dual-dimensional shallow-ensemble-based uncertainty quantification method based on parallel fine-tuning on the LAM decoder and a DImensionality-Reduced Encoded Clusters with sTratified(DIRECT)sampling strategy based on the LAM encoder.Tests show that DP-EVA delivers optimal chemical space coverage in the task of drastically reducing the size of an existing MLIP dataset for iron-based Fischer–Tropsch synthesis.DP-EVA fills the gap of active learning frameworks suitable for fine-tuning LAMs toward domain-specific MLIPs and it is also open-source,Slurm-native,and agent-ready for the coming era of agentic scientific research. 展开更多
关键词 machine-learning interatomic potentials large atomistic models active learning fine-tuning deep potential Fischer–Tropsch synthesis
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Screening of Key Genes in Pre-eclampsia and Construction of a Risk-Assessment Model Based on Machine-Learning Algorithms 认领 引用
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作者 FAN Li-ping XIE Xiao-hong RAO Cai-li 《Chinese Journal of Biomedical Engineering(English Edition)》 CAS 2025年第3期109-117,共9页
Objective:To identify potential key genes associated with pre-eclampsia through bioinformatics analysis,construct predictive models using machine-learning algorithms,and evaluate the models'performance in predicti... Objective:To identify potential key genes associated with pre-eclampsia through bioinformatics analysis,construct predictive models using machine-learning algorithms,and evaluate the models'performance in predicting pre-eclampsia.Methods:Gene-expression microarray datasets GSE10588,GSE66273,and GSE30186 related to pre-eclampsia were downloaded from the gene expression omnibus(GEO).Data were normalized using R,and differentially expressed genes(DEGs)were identified.LASSO regression was applied to further filter DEGs.Based on the selected DEGs,six machine-learning models-logistic regression(LR),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),neural network(NN),and eXtreme gradient boosting(XGBoost)were built in R,and their performance was validated.Results:From the three datasets,a total of 1,363 genes were extracted.LASSO regression narrowed these to 265 candidate key genes.Multivariate analysis ultimately identified four genes closely associated with pre-eclampsia:EVI5,GCLM,LEP,and SYNPO2L.Using these four key genes,six machine-learning models were constructed.Receiver operating characteristic(ROC)analysis showed that all models achieved AUC>0.9:LR(AUC=0.983,95%CI=0.942-0.998),RF(AUC=0.961,95%CI=0.912-0.987),SVM(AUC=0.936,95%CI=0.879-0.972),KNN(AUC=0.970,95%CI=0.924-0.992),NN(AUC=0.916,95%CI=0.854-0.958),and XGBoost(AUC=0.952,95%CI=0.900-0.982).There was no statistically significant difference among the AUCs of the models(P>0.05).Conclusion:This study identified four key genes linked to preeclampsia through integrated bioinformatics analysis.Predictive models built on these genes can accurately forecast the occurrence of pre-eclampsia,suggesting that the four genes may serve as potential biomarkers for early diagnosis and therapeutic targeting of pre-eclampsia. 展开更多
关键词 pre-eclampsia gene screening bioinformatics machine-learning algorithms
Machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy 认领 引用 被引量：2
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作者 Sheng Lu Min Yan +3 位作者 Chen Li Chao Yan Zhenggang Zhu Wencong Lu 《Chinese Journal of Cancer Research》 SCIE CAS CSCD 2019年第5期797-805,共9页
Objective: Postoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes i... Objective: Postoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes in patients undergoing gastrectomy.Methods: In this study, cancer patients who underwent gastrectomy at Shanghai Rui Jin Hospital in 2017 were randomly assigned to a development or validation cohort in a 9:1 ratio. A support vector classification(SVC) model to predict surgical outcomes in patients undergoing gastrectomy was developed and further validated.Results: A total of 321 patients with 32 features were collected. The positive and negative outcomes of postoperative complication after gastrectomy appeared in 100(31.2%) and 221(68.8%) patients, respectively. The SVC model was constructed to predict surgical outcomes in patients undergoing gastrectomy. The accuracy of 10-fold cross validation and external verification was 78.17% and 78.12%, respectively. Further, an online web server has been developed to share the SVC model for machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy in the future procedures, which is accessible at the web address:http://gffzz58c38b8802434b0chxfuuvffxvkqk6n9f.ffgz.tsg.suse.edu.cn_model_prediction.Conclusions: The SVC model was a useful predictor for measuring the risk of postoperative complications after gastrectomy, which may help stratify patients with different overall status for choice of surgical procedure or other treatments. It can be expected that machine-learning models in cancer informatics research are possibly shareable and accessible via web address all over the world. 展开更多
关键词 Gastric cancer postoperative complications machine-learning models support vector classification
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Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms 认领 引用 被引量：2
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作者 Huiting Chen Zhaozhong Zhu +3 位作者 Ye Qiu Xingyi Ge Heping Zheng Yousong Peng 《Virologica Sinica》 SCIE CAS CSCD 2022年第3期437-444,共8页
The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL prot... The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL protease.This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and Gammacoronavirus.Strong residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in cross-validations.The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts.It achieved an AUC of 0.95 and predicted correctly 80%of the cleavage sites.Then,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model.These proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and tubulin.Finally,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model.Overall,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses. 展开更多
关键词 Coronavirus 3C-like protease Cleavage sites Machine-learning algorithms 3CLP webserver
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A Machine-Learning Approach for the Prediction of Fly-Ash Concrete Strength 认领 引用 被引量：2
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作者 Shanqing Shao Aimin Gong +4 位作者 Ran Wang Xiaoshuang Chen Jing Xu Fulai Wang Feipeng Liu 《Fluid Dynamics & Materials Processing》 EI 2023年第12期3007-3019,共13页
The composite exciter and the CaO to Na2SO4 dosing ratios are known to have a strong impact on the mechanical strength offly-ash concrete.In the present study a hybrid approach relying on experiments and a machi... The composite exciter and the CaO to Na2SO4 dosing ratios are known to have a strong impact on the mechanical strength offly-ash concrete.In the present study a hybrid approach relying on experiments and a machine-learn-ing technique has been used to tackle this problem.The tests have shown that the optimal admixture of CaO and Na2SO4 alone is 8%.The best 3D mechanical strength offly-ash concrete is achieved at 8%of the compound activator;If the 28-day mechanical strength is considered,then,the best performances are obtained at 4%of the compound activator.Moreover,the 3D mechanical strength offly-ash concrete is better when the dosing ratio of CaO to Na2SO4 in the compound activator is 1:1;the maximum strength offly-ash concrete at 28-day can be achieved for a 1:1 ratio of CaO to Na2SO4 by considering a 4%compound activator.In this case,the compressive andflexural strengths are 260 MPa and 53.6 MPa,respectively;the mechanical strength offly-ash concrete at 28-day can be improved by a 4:1 ratio of CaO to Na2SO4 by considering 8%and 12%compound excitants.It is shown that the predictions based on the aforementioned machine-learning approach are accurate and reliable. 展开更多
关键词 Fly ash compound activator machine-learning approach
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Different types of drug abusers prefrontal cortex activation patterns and based on machine-learning classification 认领 引用 被引量：1
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作者 Banghua Yang Xuelin Gu +3 位作者 Shouwei Gao Lin Feng Yan Ding Xu Wen Wang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2022年第2期83-92,共10页
Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activ... Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activation.A functional near-infrared spectroscopy(fNIRS)device was used for the research.This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings.We collected the fNIRS data of 30 drug users,including 10 who used heroin,10 who used Methamphetamine,and 10 who used mixed drugs.First,using Statistical Analysis,the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin,Methamphetamine,and mixed drugs,including Left/Right-Dorsolateral prefrontal cortex(L/R-DLPFC),Left/Right-Ventrolateral prefrontal cortex(L/R-VLPFC),Left/Right-Fronto-polar prefrontal cortex(L/R-FPC),and Left/Right Orbitofrontal Cortex(L/R-OFC).Second,referencing the degrees of activation of oxyhaemoglobin concentration(HbO2),the study made an analysis and got the specific activation patterns of each group of the addicts.Finally,after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts,and which had the same channel numbers,the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks(CNNs).The average three-class accuracy is 67.13%.It is of great significance for the analysis of brain function errors and personalized rehabilitation. 展开更多
关键词 Drug addiction fNIRS machine-learning di®erent drug users brain regions activation
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Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature 认领 引用 被引量：1
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作者 Ming Wan Quanliang Li +3 位作者 Jiangyuan Yao Yan Song Yang Liu Yuxin Wan 《Computers, Materials & Continua》 SCIE EI 2022年第11期4033-4049,共17页
Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to succes... Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks.However,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples.As a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm.Based on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities.Furthermore,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming time.In the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed.All experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases. 展开更多
关键词 Anomaly detection machine-learning algorithm process control feature qualitative and quantitative comparisons
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Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease 认领 引用
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作者 Yao-Hua Gu Yu Chen +10 位作者 Qing Li Neng-Bin Xie Xue Xing Jun Xiong Min Hu Tian-Zhou Li Ke-Yu Yuan Yu Liu Tang Tang Fan He Bi-Feng Yuan 《Chinese Chemical Letters》 SCIE CAS CSCD 2024年第11期266-272,共7页
Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic dis... Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and progression.Herein,we proposed a large-scale plasma metabolite identification and quantification system that combines the strengths of targeted and untargeted metabolomics technologies,i.e.,widely-targeted metabolomics(WT-Met)approach.WT-Met method enables large-scale identification and accurate quantification of thousands of metabolites.We collected plasma samples from 21 healthy controls and 62CKD patients,categorized into different stages(22 in stages 1-3,20 in stage 4,and 20 in stage 5).Using LC-MS-based WT-Met approach,we were able to effectively annotate and quantify a total of 1431metabolites from the plasma samples.Focusing on the 539 endogenous metabolites,we identified 399significantly altered metabolites and depicted their changing patterns from healthy controls to end-stage CKD.Furthermore,we employed machine-learning to identify the optimal combination of metabolites for predicting different stages of CKD.We generated a multiclass classifier consisting of 7 metabolites by machine-learning,which exhibited an average AUC of 0.99 for the test set.In general,amino acids,nucleotides,organic acids,and their metabolites emerged as the most significantly altered metabolites.However,their patterns of change varied across different stages of CKD.The 7-metabolite panel demonstrates promising potential as biomarker candidates for CKD.Further exploration of these metabolites can provide valuable insights into their roles in the etiology and progression of CKD. 展开更多
关键词 Widely-targeted metabolomics Machine-learning Chronic kidney disease Biomarker Mass spectrometry
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Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis 认领 引用 被引量：17
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作者 A.L.Achu C.D.Aju +4 位作者 Mariano Di Napoli Pranav Prakash Girish Gopinath E.Shaji Vinod Chandra 《Geoscience Frontiers》 SCIE CAS CSCD 2023年第6期327-340,共14页
Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation strategies for future calamities.In this context,research on landslide susceptibility modelling has become a topic of relevance... Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation strategies for future calamities.In this context,research on landslide susceptibility modelling has become a topic of relevance and is in constant evolution.Though various machine-learning techniques(MLTs)have been identified for landslide susceptibility modelling,the uncertainty inherent in the models is rarely considered.The present study attempts to quantify the uncertainty associated with landslide prediction models by developing a new methodological framework based on the ensembles of the eight MLTs.This methodology has been tested at the highlands of the southern Western Ghats region(Kerala,India),where landslides have frequently been occurring.Fourteen landslide conditioning factors have been identified as part of this study,and their association was correlated with 671 historic landslides in the study area.The study used four ensemble models such as the mean of probabilities,the median of probabilities,the weighted mean of probabilities,and the committee average.The weighted mean of probability was proved to be the best model based on the average of 800 standalone MLTs,viz.,receiver operating characteristics,true skill statistics,and area under curve with corresponding validation scores.Thereafter,an uncertainty analysis was carried out on the coefficient of variation.A confident map was generated to represent the distinct zonation of landslide susceptibility areas with definite uncertainty scales.Nearly 74%of the past landslides fall in the higher susceptibility-low uncertainty category.It is also inferred that such micro-level zonation based on MLTs may improve the efficiency of landslide susceptibility maps and may help in accurately identifying landslide-prone areas in the future.The confident maps thus generated can be used as a ready reference to the planners for the formulation of landslide adaptation strategies at micro-scales. 展开更多
关键词 Landslides Machine-learning Ensemble model Kerala India
Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning 认领 引用
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作者 Jieni Lv Shuguang Han Jueliang Hu 《Journal of Textile Science and Technology》 2023年第1期1-19,共19页
Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of t... Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information. 展开更多
关键词 Clothing Retail Sales Forecasting Weather Machine-Learning Stacking
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Machine-learning guided design of non-precious-metal high-entropy electrocatalysts for alkaline hydrogen evolution 认领 引用
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作者 Shiyu Zhen Yong Wang +8 位作者 Yuhan Wang Jianhao Wang Xiangfu Niu Kailang Li Dong Su Haohong Duan Baorui Jia Mingli Qin Liang Zhang 《eScience》 SCIE CSCD 2026年第3期242-251,共10页
High-entropy alloys(HEAs)have emerged as a promising class of electrocatalysts for sustainable hydrogen production driven by renewable electricity,owing to their compositional diversity and flexible local atomic confi... High-entropy alloys(HEAs)have emerged as a promising class of electrocatalysts for sustainable hydrogen production driven by renewable electricity,owing to their compositional diversity and flexible local atomic configurations.However,the vast design space and complex local environments of HEAs pose major challenges for pinpointing active sites and enabling rational catalyst design.In this work,we develop an integrated,datadriven framework to accelerate the discovery of non-precious-metal HEA catalysts for the alkaline hydrogen evolution reaction(HER).By combining high-throughput density functional theory(DFT)calculations with machine learning models enhanced by transfer learning,we screened over 25,000 surface sites to rapidly identify catalytically promising FeCoNi-based HEAs.Among them,FeCoNiCuW emerges as a leading candidate,featuring two distinct active site motifs,NiCoW and NiCuW,that are identified computationally and validated through both DFT analysis and experimental measurements.This study establishes a generalizable approach for active site discovery in compositionally complex materials and provides actionable insights for the design of efficient HER electrocatalysts. 展开更多
关键词 High entropy alloys Hydrogen electrocatalysis Machine-learning Data-driven High-throughput calculation
Clinical-radiomics models with machine-learning algorithms to distinguish uncomplicated from complicated acute appendicitis in adults:a multiphase multicenter cohort study 认领 引用 被引量：1
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作者 Li Li Yangyang Sun +10 位作者 Yang Sun Yunhe Gao Benlong Zhang Ruizhao Qi Fugeng Sheng Xiaodong Yang Xu Liu Lin Liu Canrong Lu Lin Chen Kecheng Zhang 《Gastroenterology Report》 SCIE CSCD 2025年第1期137-144,共8页
Increasing evidence suggests that non-operative management(NOM)with antibiotics could serve as a safe alternative to surgery for the treatment of uncomplicated acute appendicitis(AA).However,accurately differentiating... Increasing evidence suggests that non-operative management(NOM)with antibiotics could serve as a safe alternative to surgery for the treatment of uncomplicated acute appendicitis(AA).However,accurately differentiating between uncomplicated and complicated AA remains challenging.Our aim was to develop and validate machine-learning-based diagnostic models to differentiate uncomplicated from complicated AA.This was a multicenter cohort trial conducted from January 2021 and December 2022 across five tertiary hospitals.Three distinct diagnostic models were created,namely,the clinical-parameter-based model,the CT-radiomicsbased model,and the clinical-radiomics-fused model.These models were developed using a comprehensive set of eight machinelearning algorithms,which included logistic regression(LR),support vector machine(SVM),random forest(RF),decision tree(DT),gradient boosting(GB),K-nearest neighbors(KNN),Gaussian Naïve Bayes(GNB),and multi-layer perceptron(MLP).The performance and accuracy of these diverse models were compared.All models exhibited excellent diagnostic performance in the training cohort,achieving a maximal AUC of 1.00.For the clinical-parameter model,the GB classifier yielded the optimal AUC of 0.77(95%confidence interval[CI]:0.64-0.90)in the testing cohort,while the LR classifier yielded the optimal AUC of 0.76(95%CI:0.66-0.86)in the validation cohort.For the CT-radiomics-based model,GB classifier achieved the best AUC of 0.74(95%CI:0.60-0.88)in the testing cohort,and SVM yielded an optimal AUC of 0.63(95%CI:0.51-0.75)in the validation cohort.For the clinical-radiomics-fused model,RF classifier yielded an optimal AUC of 0.84(95%CI:0.74-0.95)in the testing cohort and 0.76(95%CI:0.67-0.86)in the validation cohort.An openaccess,user-friendly online tool was developed for clinical application.This multicenter study suggests that the clinical-radiomicsfused model,constructed using RF algorithm,effectively differentiated between complicated and uncomplicated AA. 展开更多
关键词 complicated acute appendicitis uncomplicated acute appendicitis machine-learning algorithm appendectomy
A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search 认领 引用 被引量：25
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作者 Kang Xia Hao Gao +4 位作者 Cong Liu Jianan Yuan Jian Sun Hui-Tian Wang Dingyu Xing 《Science Bulletin》 SCIE EI CAS CSCD 2018年第13期817-824,共8页
Transition metal nitrides have been suggested to have both high hardness and good thermal stability with large potential application value, but so far stable superhard transition metal nitrides have not been synthesiz... Transition metal nitrides have been suggested to have both high hardness and good thermal stability with large potential application value, but so far stable superhard transition metal nitrides have not been synthesized. Here, with our newly developed machine-learning accelerated crystal structure searching method, we designed a superhard tungsten nitride, h-WN6, which can be synthesized at pressure around 65 GPa and quenchable to ambient pressure. This h-WN6 is constructed with single-bonded armchair-like N6 rings and presents ionic-like features, which can be formulated as W^2.4＋N^2.4-. It has a band gap of 1.6 eV at 0GPa and exhibits an abnormal gap broadening behavior under pressure. Excitingly, this h-WN6 is found to be the hardest among transition metal nitrides known so far （Vickers hardness around 57 GPa） and also has a very high melting temperature （around 1,900 K）. Additionally, the good gravimet- ric （3.1 kJ/g/and volumetric （28.0 kJ/cm3） energy densities make this nitrogen-rich compound a potential high-energy-density material, These predictions support the designing rules and may stimulate future experiments to synthesize superhard and high-energy-density material. 展开更多
关键词 Tungsten nitride Transition metal nitrides Machine-learning accelerated crystal structure searching method Superhard tungsten nitride
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DQNN: Pore-scale variables-based digital permeability assessment of carbonates using quantum mechanism-based machine-learning 认领 引用 被引量：1
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作者 ZHAO Zhi ZHOU XiaoPing QIAN QiHu 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2022年第2期458-469,共12页
Permeability is a key parameter of rock reservoirs, suggesting the flow characteristics of rock reservoirs. Permeability prediction of carbonate reservoirs is still a great challenge due to its complex pore network an... Permeability is a key parameter of rock reservoirs, suggesting the flow characteristics of rock reservoirs. Permeability prediction of carbonate reservoirs is still a great challenge due to its complex pore network and wide range permeability. This work is to establish a digital quantum mechanism-based neural network(DQNN) to study the permeability using the digital porosity,coordination number and pore network size. Experiments and artificial neural network methods(ANN) are applied to validate the accuracy of the proposed DQNN method. In these methods, the pore-scale variables extracted from the micro-CT images of200 carbonate samples are applied. Results show that the permeabilities obtained from experimental, artificial neural network and DQNN methods agree well with each other. Digital pore size, pore throat size and length are better parameters, while coordination number and porosity are relatively secondary parameters for permeability descriptions of carbonate reservoirs.Compared with the ANN method, the proposed DQNN method is superior in low computation time and high ability for complicated problems. 展开更多
关键词 permeability estimation quantum mechanism machine-learning model micro-pore network carbonates
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Systematic assessment of various universal machine-learning interatomic potentials 认领 引用 被引量：10
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作者 Haochen Yu Matteo Giantomassi +2 位作者 Giuliana Materzanini Junjie Wang Gian-Marco Rignanese 《Materials Genome Engineering Advances》 2024年第3期59-70,共12页
Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length ... Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length scales.More recently,various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest.In this paper,we review and evaluate four different universal machine-learning interatomic potentials(uMLIPs),all based on graph neural network architectures which have demonstrated transferability from one chemical system to another.The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project.Through this comprehensive evaluation,we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems,offer recommendations for model selection and optimization,and stimulate discussion on potential areas for improvement in current machinelearning methodologies in materials science. 展开更多
关键词 formation energy geometry optimization machine learning phonons,universal machine-learning interatomic potentials verification
Sequential phase transformations in Ta0.4Ti2Zr alloy via tensile molecular dynamics simulations with deep potential 认领 引用 被引量：1
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作者 Hongyang Liu Rong Chen +3 位作者 Bo Chen Jingzhi He Dongdong Kang Jiayu Dai 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第1期46-55,共10页
Understanding the complex deformation mechanisms of non-equimolar multi-principal element alloys(MPEAs)requires high-fidelity atomic-scale simulations.This study develops a deep potential(DP)model to enable molecular ... Understanding the complex deformation mechanisms of non-equimolar multi-principal element alloys(MPEAs)requires high-fidelity atomic-scale simulations.This study develops a deep potential(DP)model to enable molecular dynamics simulations of the Ta0.4Ti2Zr(Ta0.4)alloy.Monte Carlo simulations using this potential reveal Ta atom precipitation in the Ta0.4alloy.Under uniaxial tensile loading along the[100]direction in the NPT ensemble,the alloy undergoes a remarkable sequence of phase transformations:an initial body-centered cubic(BCC1)to face-centered cubic(FCC)transformation,followed by a reverse transformation from FCC to a distinct BCC phase(BCC2),and finally a BCC2 to hexagonal close-packed(HCP)transformation.Critically,the reverse FCC to BCC2 transformation induces significant volume contraction.We demonstrate that the inversely transformed BCC2 phase primarily accommodates compressive stress.Concurrently,the reorientation of BCC2 crystals contributes substantially to the observed high strain hardening.These simulations provide atomic-scale insights into the dynamic structural evolution,sequential phase transformations,and stress partitioning during deformation of the Ta0.4alloy.The developed DP model and the revealed mechanisms offer fundamental theoretical guidance for accelerating the design of high-performance MPEAs. 展开更多
关键词 multi-principal element alloys machine-learning potential phase transformation stress partitioning
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Redefining atomistic simulations of all-solid-state batteries through machine learning interatomic potentials 认领 引用
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作者 Qian Chen Siwen Wang Chen Ling 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期666-687,I0015,共22页
All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lit... All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lithium-ion batteries.Understanding and optimizing the complex chemistries and interfaces that underpin ASSB performance present significant challenges from both experimental and modeling perspectives.In particular,atomistic simulations face difficulties in capturing the complex structure,disorder,and dynamic evolution of materials and interfaces under practically relevant conditions.While established methods such as density functional theory and classical force fields have provided valuable insights,some questions remain difficult to address,particularly those involving large system sizes or long timescales.Recently,machine learning interatomic potentials(MLIPs)have emerged as a transformative tool,enabling atomistic simulations at length and time scales that were previously challenging to access with conventional approaches.By delivering near first-principles accuracy with much greater efficiency,MLIPs open new avenues for large-scale,long-timescale,and high-throughput simulations of solid-state battery materials.In this review,we present a comparative overview of density functional theory,classical force fields,and MLIPs,highlighting their respective strengths and limitations in ASSB research.We then discuss how MLIPs enable simulations that reach longer timescales,larger system sizes,and support high-throughput calculations,providing unique insights into ion transport and interfacial evolution in ASSBs.Finally,we conclude with a summary and outlook on current challenges and future opportunities for expanding MLIP capabilities and accelerating their impact in solid-state battery research. 展开更多
关键词 All-solid-state batteries Solid-state electrolytes Machine-learning interatomic potential Atomistic modeling lon transport Interfaces
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Carbon-doped GeTe-based ovonic threshold switch for highly reliable artificial neuron devices 认领 引用
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作者 Jeong Hwan Song Changmin Lim +4 位作者 Jae-Seung Jeong Shinyeong Park Min Kyu Yang Jiwon Chang Gun Hwan Kim 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第3期805-825,共21页
Spiking neural networks(SNNs)rely on precise spike timing for computation;however,their performance often suffers jitter-induced errors and constraints on synaptic weight updates.In this study,we address this challeng... Spiking neural networks(SNNs)rely on precise spike timing for computation;however,their performance often suffers jitter-induced errors and constraints on synaptic weight updates.In this study,we address this challenge by expanding the neuronal integration window,enhancing temporal robustness while maintaining efficient learning dynamics.We introduce a material-driven approach to expand the operational window of artificial neuron devices,which is defined as the difference between the threshold voltage(Vth)and holding voltage(Vhold),in ovonic threshold switch(OTS)-based neurons,demonstrating its direct impact on synaptic weight updates and error mitigation.Carbon doping in GeTe-based OTS devices is employed to systematically modulate trap depth variations under an electric field,achieving precise control over Vth and Vhold.Electrical measurements confirm that an optimal 3.4%carbon concentration maximizes the operational window,stabilizing threshold switching and ensuring reliable neuronal operation.To reveal the atomic-scale mechanisms behind this behavior,we perform density functional theory(DFT)simulations,analyzing coordination number and bond angles to elucidate how carbon incorporation modifies trap distributions and influences device characteristics.Finally,we assess the practical impact of operational window expansion by implementing the optimized OTS neurons in a learning framework based on the tempotron learning rule,revealing enhanced spike timing robustness and reduced synaptic weight update constraints.This study provides a scalable pathway toward more reliable spike-based neuromorphic computing to advance the next generation of artificial intelligence hardware. 展开更多
关键词 carbon-doped germanium telluride ovonic threshold switching neuron devices threshold and holding voltage differences trap depth variations machine-learning molecular dynamics simulations tempotron learning rules neuromorphic computing hardware
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