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Towards understanding structure–property relations in materials with interpretable deep learning

npj Computational Materials 2023年第1期9卷 157-168页

作者： Tien-Sinh Vu Minh-Quyet Ha Duong-Nguyen Nguyen Viet-Cuong Nguyen Yukihiro Abe Truyen Tran Huan Tran Hiori Kino Takashi Miyake Koji Tsuda Hieu-Chi Dam Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material *** address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art ***,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical *** properties encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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Physics and chemistry from parsimonious representations:image analysis via invariant variational autoencoders

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npj Computational Materials 2024年第1期10卷 1348-1366页

作者： Mani Valleti Maxim Ziatdinov Yongtao Liu Sergei V.Kalinin Bredesen Center for Interdisciplinary Research University of TennesseeKnoxvilleTN37916USA Physical Sciences Division Pacific Northwest National LaboratoryRichlandWA99352USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN37831USA Institute for Advanced Materials and Manufacturing Department of Materials Science and EngineeringUniversity of TennesseeKnoxvilleTN37996USA

Electron,optical,and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and *** necessitates the development of themachine learning methods for discovery of physical and chemical phenomena from the data,such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images,or variability of the *** autoencoders(VAEs)are emerging as a powerful paradigm for the unsupervised data analysis,allowing to disentangle the factors of variability and discover optimal parsimonious ***,we summarize recent developments in VAEs,covering the basic principles and intuition behind the VAEs.

关键词： image invariant analysis

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Highly ordered inkjet-printed quantum-dot thin films enable efficient and stable QLEDs with EQE exceeding 23%

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escience 2024年第3期4卷 152-162页

作者： Changting Wei Bo Xu Meng Zhang Zhenhuang Su Jiawei Gu Wenrui Guo Xingyu Gao Wenming Su Zheng Cui Seokwoo Jeon Zhiyong Fan Haibo Zeng MIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics&NanomaterialsSchool of Materials Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Ningbo Nato New Material Technology Co. LtdNingbo 315600China Shanghai Synchrotron Radiation Facility(SSRF) Zhangjiang LaboratoryShanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China School of Mechanical Engineering Jiangsu UniversityZhenjiang 212013China Printable Electronics Research Center Nano Devices and Materials DivisionSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Department of Materials Science and Engineering Korea UniversitySeoul 02841South Korea Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

Inkjet-printed quantum dot light-emitting diodes(QLEDs)are emerging as a promising technology for next-generation ***,the progress in fabricating QLEDs using inkjet printing technique has been slower compared to spin-coated devices,particularly in terms of efficiency and *** key to achieving high performance QLEDs lies in creating a highly ordered and uniform inkjet-printed quantum dot(QD)thin *** this study,we present a highly effective strategy to significantly improve the quality of inkjet-printed CdZnSe/CdZnS/ZnS QD thin films through a pressure-assisted thermal annealing(PTA)*** from this PTA process,a high quality QD thin film with ordered packing,low surface roughness,high photoluminescence and excellent electrical property is *** mechanism behind the PTA process and its profound impact on device performance have been thoroughly investigated and ***,a record high external quantum efficiency(EQE)of 23.08%with an impressive operational lifetime(T50)of up to 343,342h@100cdm−2,and a record EQE of 22.43%with T50 exceeding to 1,500,463h@100cdm−2 are achieved in inkjet-printed red and green CdZnSe-based QLEDs,*** work highlights the PTA process as an important approach to realize highly efficient and stable inkjet-printed QLEDs,thus advancing QLED technology to practical applications.

关键词： Inkjet printing Quantum dot light-emitting diode High efficiency Thermal annealing Highly ordered

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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A FILLING DETECTION EXPERIMENT OF FULL-SCALE CFT COLUMN USING LONG AND NARROW WETTING SENSOR MONITORING SYSTEM

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AIJ Journal of Technology and Design 2022年第70期28卷 1107-1112页

作者： Uehara, Makoto Matsumoto, Kenjiro Ohkubo, Takaaki Kizuka, Masatsugu Karasawa, Tomoyuki Teramoto, Atsushi Architectural Division Tekken Corporation Japan Deputy Executive General Manager Architectural Division Tekken Corporation Japan Architectural Division Tekken Corporation Japan Research & Development Center Tekken Corporation Japan Graduate School of Advanced Science and Engineering Hiroshima Univ. Japan Graduate School of Advanced Science and Engineering Hiroshima Univ. Japan

In CFT construction, unlike the conventional method of RC construction, it is not possible to visually confirm the launch state of concrete after dismantling the formwork. Therefore, in the guideline, quality is ensured by establishing concrete specifications and construction method regulations. Therefore, we considered confirming the filling status of CFT concrete by the long and narrow wetting sensor monitoring system. As a result of the experiment, it was confirmed that the developed long and narrow wetting sensor monitoring system can estimate the filling status of the concrete under the diaphragm of the CFT construction. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Wetting

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DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing

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Neural Networks 2023年 161卷 83-92页

作者： Lin, Jiun-Da Han, Yue-Hua Huang, Po-Han Tan, Julianne Chen, Jun-Cheng Tanveer, M. Hua, Kai-Lung Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Mathematics Indian Institute of Technology Indore Simrol453552 India

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model's capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods. © 2023 Elsevier Ltd

关键词： Large dataset

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Generative artificial intelligence and its applications in materials science:Current situation and future perspectives

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Journal of Materiomics 2023年第4期9卷 798-816页

作者： Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear Science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia

Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity ***,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of *** applications of task-specific generative models involving materials inverse design and data augmentation are also *** ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials ***,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding *** work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

关键词： Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning

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Analysis of the electron transfer pathway in small laccase by EPR and UV-vis spectroscopy coupled with redox titration

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Magnetic Resonance Letters 2024年第3期4卷 52-59页

作者： Lu Yu Aokun Liu Jian Kuang Ruotong Wei Zhiwen Wang Changlin Tian High Magnetic Field Laboratory Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei230031China Department of Endocrinology Institute of Endocrine and Metabolic DiseaseThe First Affiliated Hospital of USTCDivision of Life Sciences and MedicineHefei National Research Center for Physical Sciences at the MicroscaleJoint Center for Biological Analytical ChemistryAnhui Engineering Laboratory of Peptide DrugAnhui Laboratory of Advanced Photonic Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China

Bacterial small laccases(SLAC) are promising industrial biocatalysts due to their ability to oxidize a broad range of substrates with exceptional thermostability and tolerance for alkaline p H. Electron transfer between substrate, copper centers, and O2is one of the key steps in the catalytic turnover of SLAC. However, limited research has been conducted on the electron transfer pathway of SLAC and SLAC-catalyzed reactions, hindering further engineering of SLAC to produce tunable biocatalysts for novel applications. Herein, the combinational use of electron paramagnetic resonance(EPR) and ultraviolet-visible(UV-vis) spectroscopic methods coupled with redox titration were employed to monitor the electron transfer processes and obtain further insights into the electron transfer pathway in SLAC. The reduction potentials for type 1 copper(T1Cu), type 2 copper(T2Cu) and type 3copper(T3Cu) were determined to be 367 ± 2 mV, 378 ± 5 m V and 403 ± 2 mV,respectively. Moreover, the reduction potential of a selected substrate of SLAC, hydroquinone(HQ), was determined to be 288 mV using cyclic voltammetry(CV). In this way, an electron transfer pathway was identified based on the reduction potentials. Specifically,electrons are transferred from HQ to T1Cu, then to T2Cu and T3Cu, and finally to ***, superhyperfine splitting observed via EPR during redox titration indicated a modification in the covalency of T2Cu upon electron uptake, suggesting a conformational alteration in the protein environment surrounding the copper sites, which could potentially influence the reduction potential of the copper sites during catalytic processes. The results presented here not only provide a comprehensive method for analyzing the electron transfer pathway in metalloenzymes through reduction potential measurements, but also offer valuable insights for further engineering and directed evolution studies of SLAC in the aim for biotechnological and industrial applications.

关键词： Electron paramagnetic resonance Redox titration Electron transfer Reduction Potential Small laccase

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Numerical Procedure for Fractional HBV Infection with Impact of Antibody Immune

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computers, Materials & Continua 2023年第2期74卷 2575-2588页

作者： Sakda Noinang Zulqurnain Sabir Muhammad Asif Zahoor Raja Soheil Salahshour Wajaree Weera Thongchai Botmart Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbul34494Turkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The current investigations are presented to solve the fractional order HBV differential infection system(FO-HBV-DIS)with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation(LMB)neural networks(NNs),i.e.,*** FO-HBV-DIS with the response of antibody immune is categorized into five dynamics,healthy hepatocytes(H),capsids(D),infected hepatocytes(I),free virus(V)and antibodies(W).The investigations for three different FO variants have been tested numerically to solve the nonlinear *** data magnitudes are implemented 75%for training,10%for certification and 15%for testing to solve the FO-HBV-DIS with the response of antibody *** numerical observations are achieved using the stochastic LMBNNs procedures for soling the FO-HBV-DIS with the response of antibody immune and comparison of the results is presented through the database Adams-Bashforth-Moulton *** authenticate the validity,competence,consistency,capability and exactness of the LMBNNs,the numerical presentations using the mean square error(MSE),error histograms(EHs),state transitions(STs),correlation and regression are accomplished.

关键词： Fractional order HBV differential infection system artificial neural networks nonlinear Levenberg-Marquardt backpropagation Adams-Bashforth-Moulton

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