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Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

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npj Computational Materials 2023年第1期9卷 733-743页

作者： Evan R.Antoniuk Gowoon Cheon George Wang Daniel Bernstein william cai Evan J.Reed Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Department of Chemistry Stanford UniversityStanfordCAUSA Department of Applied Physics Stanford UniversityStanfordCAUSA Google Research Mountain ViewCAUSA Department of Physics Stanford UniversityStanfordCAUSA Department of Mathematics Stanford UniversityStanfordCAUSA Department of Computer Science Stanford UniversityStanfordCAUSA Department of Materials Science and Engineering Stanford UniversityStanfordCAUSA

Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials *** this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical *** reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation *** a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human ***,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability *** development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

关键词： inorganic crystalline utilize

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SiC功率模块封装技术及展望

汽车工程 2022年第4期44卷 638-647页

作者：蔡蔚杨茂通刘洋李道会哈尔滨理工大学电气与电子工程学院哈尔滨150080 哈尔滨理工大学材料学院哈尔滨150080 上海蔚来汽车有限公司上海201805

SiC MOSFET器件的集成化、高频化和高效化需求,对功率模块封装形式和工艺提出了更高的要求。本文中总结了近年来封装形式的结构优化和技术创新,包括键合式功率模块的金属键合线长度、宽度和并联数量对寄生电感的影响,直接覆铜(DBC)的陶... 详细信息

SiC MOSFET器件的集成化、高频化和高效化需求,对功率模块封装形式和工艺提出了更高的要求。本文中总结了近年来封装形式的结构优化和技术创新,包括键合式功率模块的金属键合线长度、宽度和并联数量对寄生电感的影响,直接覆铜(DBC)的陶瓷基板中陶瓷层的面积和高度对寄生电容的影响,以及采用叠层换流技术优化寄生参数等成果;综述了双面散热结构的缓冲层厚度和形状对散热指标和应力与形变的影响;汇总了功率模块常见失效机理和解决措施,为模块的安全使用提供参考。最后探讨了先进烧结银技术的要求和关键问题,并展望了烧结封装技术和材料的发展方向。

关键词：功率模块模块封装失效机理双面散热烧结封装银烧结技术寄生参数

Correlation between the rock mass properties and maximum horizontal stress:A case study of overcoring stress measurements

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 39-48页

作者： Peng Li Meifeng cai Shengjun Miao Yuan Li Yu Wang Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Coal Resources and Safe Mining China University of Mining and TechnologyXuzhou 221116China

Understanding the mechanical properties of the lithologies is crucial to accurately determine the horizontal stress *** investigate the correlation between the rock mass properties and maximum horizontal stress,the three-dimensional(3D)stress tensors at 89 measuring points determined using an improved overcoring technique in nine mines in China were adopted,a newly defined characteristic parameter C_(ERP)was proposed as an indicator for evaluating the structural properties of rock masses,and a fuzzy relation matrix was established using the information distribution *** results indicate that both the vertical stress and horizontal stress exhibit a good linear growth relationship with *** is no remarkable correlation between the elastic modulus,Poisson's ratio and depth,and the distribution of data points is scattered and ***,there is no obvious relationship between the rock quality designation(RQD)and *** maximum horizontal stress σ_(H) is a function of rock properties,showing a certain linear relationship with the C_(ERP)at the same *** addition,the overall change trend of σ_(H) determined by the established fuzzy identification method is to increase with the increase of C_(ERP).The fuzzy identification method also demonstrates a relatively detailed local relationship betweenσ_H and C_(ERP),and the predicted curve rises in a fluctuating way,which is in accord well with the measured stress data.

关键词： overcoring stress measurements elastic modulus Poisson's ratio rock quality designation maximum horizontal stress fuzzy identification

SensFL:Privacy-Preserving Vertical Federated Learning with Sensitive Regularization

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 385-404页

作者： Chongzhen Zhang Zhichen Liu Xiangrui Xu Fuqiang Hu Jiao Dai Baigen cai Wei Wang School of Automation and Intelligence Beijing Jiaotong UniversityBeijing100044China Shuohuang Railway Development Co. Ltd.National Energy GroupCangzhou062350China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation School of Computer Science and TechnologyBeijing Jiaotong UniversityBeijing100044China

In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data *** Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training ***,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the *** vulnerability poses a significant threat to VFL-based intelligent railway transportation *** this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in ***,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared *** reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the *** experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of *** results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning *** results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.

关键词： Vertical federated learning privacy defenses

Identifying the dynamic behaviors in complete reconstruction of Co-based complex precatalysts during electrocatalytic oxygen evolution

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Journal of Energy Chemistry 2025年第1期100卷 226-233页

作者： Jingfang Zhang Danyang Wu Linke cai Youluan Lu Fanpeng Cheng Lijuan Shi Qun Yi Yao Liu Yi Huang Key Laboratory of Green Chemical Engineering Process of Ministry of Education Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education Hubei Key Laboratory of Novel Reactor and Green Chemical Technology School of Chemical Engineering & Pharmacy Wuhan Institute of Technology Key Laboratory of Pesticide & Chemical Biology of Ministry of Education Engineering Research Center of Photoenergy Utilization for Pollution Control and Carbon Reduction of Ministry of Education College of Chemistry Central China Normal University College of Materials Science and Engineering Fuzhou University

Transition metal-based nanomaterials have emerged as promising electrocatalysts for oxygen evolution reaction（OER）. Considerable research efforts have shown that self-reconstruction occurs on these nanomaterials under operating conditions of OER process. However, most of them undergo incomplete reconstruction with limited thickness of reconstruction layer, leading to low component utilization and arduous exploration of real catalytic mechanism. Herein, we identify the dynamic behaviors in complete reconstruction of Co-based complexes during OER. The hollow phytic acid（PA） cross-linked CoFe-based complex nanoboxes with porous nanowalls are designed because of their good electrolyte penetration and mass transport ability, in favor of the fast and complete reconstruction. A series of experiment characterizations demonstrate that the reconstruction process includes the fast substitution of PA by OH-to form Co（Fe）(OH)xand subsequent potential-driven oxidation to Co（Fe）OOH. The obtained CoFeOOH delivers a low overpotential of 290 mV at a current density of 10 mA cm-2and a long-term *** experiment results together with theory calculations reveal that the Fe incorporation can result in the electron rearrangement of reconstructed CoFeOOH and optimization of their electronic structure,accounting for the enhanced OER activity. The work provides new insights into complete reconstruction of metal-based complexes during OER and offers guidelines for rational design of high-performance electrocatalysts.

关键词： Complete reconstruction Co-based complex Precatalysts Oxygen evolution reaction

Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 18-23页

作者： Jiarui cai Bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation Center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina China Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases

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Neural Regeneration Research 2025年第8期20卷 2373-2381页

作者： Lifang Zhao Mingkai Zhang Qimeng Li Xuemin Wang Jie Lu Ying Han Yanning cai Department of Clinical Biobank Xuanwu HospitalCapital Medical UniversityBeijingChina Beijing Geriatric Medical Research Center BeijingChina Department of Neurology Xuanwu HospitalCapital Medical UniversityBeijingChina Department of Radiology and Nuclear Medicine Xuanwu HospitalCapital Medical UniversityBeijingChina Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics BeijingChina Key Laboratory of Neurodegenerative diseases Ministry of EducationBeijingChina School of Biomedical Engineering Hainan UniversityHaikouHainan ProvinceChina Center of Alzheimer’s Disease Beijing Institute for Brain DisordersBeijingChina National Clinical Research Center for Geriatric Diseases BeijingChina Institute of Biomedical Engineering Shenzhen Bay LaboratoryGaoke Innovation CenterShenzhenGuangdong ProvinceChina Department of Neurobiology Xuanwu HospitalCapital Medical UniversityBeijingChina

Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative ***,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°*** aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large *** samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline *** samples were stored at-80°C for up to 6 years before being used in this *** results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage ***,the level of total tau showed a negative correlation with sample storage ***,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage *** was not observed in individuals with cognitive ***,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage ***,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's ***,in cohort studies,it is important to consider the impact of storage time on the overall results.

关键词： Alzheimer’s disease amyloid-β diagnostic ability glial fibrillary acidic protein neurodegeneration neurofilament light chain plasma biomarkers single molecule array storage time tau

Feature Selection for Multi-label Classification Using Neighborhood Preservation

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 320-330页

作者： Zhiling cai william Zhu Laboratory of Granular Computing and AI Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China

Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.

关键词： Feature selection multi-label learning neighborhood relationship preserving sample similarity

Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

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Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation