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Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo

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npj Computational Materials 2024年第1期10卷 2360-2369页

作者： Stephan Thaler Felix Mayr Siby Thomas Alessio Gagliardi Julija Zavadlav Professorship of Multiscale Modeling of Fluid Materials Department of Engineering Physics and ComputationTUM School of Engineering and DesignTechnical University of MunichMunichGermany Valence Labs MontrealQCCanada Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical EngineeringTUM School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Munich Data Science Institute Technical University of MunichMunichGermany

Metal-organic frameworks(MOF)are an attractive class of porous materials due to their immense design space,allowing for application-tailored *** of interest,such as gas sorption,can be predicted in silico with molecular mechanics ***,the accuracy is limited by the available empirical force field and partial charge estimation *** this work,we train a graph neural network for partial charge prediction via active learning based on Dropout Monte *** show that active learning significantly reduces the required amount of labeled MOFs to reach a target *** obtained model generalizes well to different distributions of MOFs and *** addition,the uncertainty predictions of Dropout Monte Carlo enable reliable estimation of the mean absolute error for unseen *** work paves the way towards accurate molecular modeling of MOFs via next-generation potentials with machine learning predicted partial charges,supporting insilico material design.

关键词： properties charge estimation

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Algorithms of Time Series Network: Approaches Reproduction and Networks Topology 9

Algorithms of Time Series Network: Approaches Reproduction a...

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Wang, Li-Na Cheng, Yuan-Yuan Tan, Gui-Min Science College Inner Mongolia University of Technology Hohhot010051 China Inner Mongolian Key Lab. of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot010051 China

ISBN: (纸本)9781643684703

Network analysis methods of time series provide a new analytical framework on describing complex behaviors using sample data. Reproducibility is one of the important principles in scientific research. This work focuses on the algorithm review and reproduction of time series network. We paid special attention to the applicability and data characteristics of the five typical algorithms: visibility graph, phase space reconstruction, fluctuation mode, symbolic representation and coarsened multidimensional time series. It provides a reference and inspiration for future analysis of time series with various characteristics. Although two pioneer methods are widely applicable, while the directed weighted network established by coarsening thoughts contains more information about time series. In addition, coarseness process makes these approaches perform better for massive data analysis. It should be noted that: the process of data coarsening needs to ensure that the data characteristics of the original time series are inherited. © 2023 The authors and IOS Press.

关键词： Time series

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A Brief Review of Recent Advances in AI-Based 3D modeling and Reconstruction in Medical, Education, Surveillance and Entertainment

A Brief Review of Recent Advances in AI-Based 3D Modeling an...

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International Conference on Automation and Computing (ICAC)

作者： Gulraiz Khan Heyam Maraha Qingde Li Kevin Pimbblet Data Science Artificial Intelligence and Modeling Center University of Hull Hull UK School of Computer Science University of Hull Hull UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

In the era of rapid technological development, 3D models are being used more frequently. In reaction to the growing need for 3D modeling applications, this survey thoroughly looks at the many uses of 3D technologies with special focus on medical, education, surveillance, and entertainment sectors. In this study we explore techniques used, challenges faced and algorithmic complexities for each application of reviewed 3D technology to improve overall comprehension and enable well-informed decisions. This study adds to the body of knowledge on the technological integration of Machine Learning based 3D applications while also highlighting the qualitative and quantitative comparison of a subset of applications in each of the four domains that were chosen, as shown in the tabular matrix. Further, this paper suggests an ongoing research to address the limitations in 3D modeling applications, highlighting the necessity of directing the path of development towards Generative Networks in these related fields for the betterment of society.

关键词： Surveys Solid modeling Visualization Three-dimensional displays Machine learning algorithms Reviews Surveillance Education Entertainment industry Security

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Approximate solutions to fractional differential equations

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Applied Mathematics and Mechanics(English Edition) 2023年第10期44卷 1791-1802页

作者： Yue LIU Zhen ZHAO Yanni ZHANG Jing PANG College of Sciences Inner Mongolia University of TechnologyHohhot 010051China Department of Mathematics and Computer Science Hetao CollegeBayannur 015000Inner Mongolia Autonomous RegionChina Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot 010051China College of Mathematics and Statistics Ningbo UniversityNingbo 315211Zhejiang ProvinceChina College of Science Liaoning University of TechnologyJinzhou 121001Liaoning ProvinceChina

In this paper, the time-fractional coupled viscous Burgers' equation(CVBE)and Drinfeld-Sokolov-Wilson equation(DSWE) are solved by the Sawi transform coupled homotopy perturbation method(HPM). The approximate series solutions to these two equations are obtained. Meanwhile, the absolute error between the approximate solution given in this paper and the exact solution given in the literature is analyzed. By comparison of the graphs of the function when the fractional order α takes different values, the properties of the equations are given as a conclusion.

关键词： Caputo fractional derivative coupled viscous Burgers'equation(CVBE) Drinfeld-Sokolov-Wilson equation(DSWE) Sawi transform homotopy perturbation method(HPM)

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An Alternative Algorithm for the Symmetry Classification of Ordinary Differential Equations

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Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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Multi-objective Optimization of Adhesive Bonding Process in Constrained and Noisy Settings 6th

Multi-objective Optimization of Adhesive Bonding Process in...

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6th International Conference on Optimization and Learning, OLA 2023

作者： Morales-Hernández, Alejandro Van Nieuwenhuyse, Inneke Rojas Gonzalez, Sebastian Jordens, Jeroen Witters, Maarten Van Doninck, Bart Flanders Make@UHasselt Faculty of Science Hasselt University Diepenbeek3590 Belgium Data Science Institute Hasselt University Diepenbeek3590 Belgium Surrogate Modeling Lab Ghent University Ghent9000 Belgium CoDesignS Flanders Make Lommel3920 Belgium

ISBN: (纸本)9783031340192

Finding the optimal process parameters for an adhesive bonding process is challenging: the optimization is inherently multi-objective (aiming to maximize break strength while minimizing cost), constrained (the process should not result in any visual damage to the materials, and stress tests should not result in adhesive failures), and uncertain (measuring the same process parameters several times lead to different break strength). Real-life physical experiments in the lab are expensive to perform (∼ 6 h of experimentation and subsequent production costs);traditional evolutionary approaches are then ill-suited to solve the problem, due to the prohibitive amount of experiments required for evaluation. In this research, we successfully applied specific machine learning techniques (Gaussian Process Regression and Logistic Regression) to emulate the objective and constraint functions based on a limited amount of experimental data. The techniques are embedded in a Bayesian optimization algorithm, which succeeds in detecting Pareto-optimal process settings in a highly efficient way (i.e., requiring a limited number of experiments). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Development of a high-resolution near-surface meteorological forcing dataset for the Third Pole region

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science China Earth sciences 2025年第4期68卷 1274-1290页

作者： Yaozhi JIANG Wenjun TANG Kun YANG Jie HE Changkun SHAO Xu ZHOU Hui LU Yingying CHEN Xin LI Jiancheng SHI School of Resources and Environment University of Electronic Science and Technology of ChinaChengdu611731China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing100084China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing100101China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing100084China National Space Science Center Chinese Academy of SciencesBeijing100190China

Recently,the Third Pole(TP)region has experienced rapid environmental *** data are essential for hydrometeorological and ecological applications but still have large uncertainties on the TP owing to the heterogeneous land surface,complex terrain,and sparse weather *** this study,a long-term(1979–2020)high-resolution(1/30°)meteorological forcing dataset for the TP(TPMFD)was developed,as a sister to the widely used China Meteorological Forcing dataset(CMFD).The TPMFD comprises seven components necessary for driving land surface *** have previously contributed precipitation and downward shortwave radiation data for the TPMFD,and this study presents the development of five other components and focuses on validations for all ***,2-meter air temperature,2-meter specific humidity,10-meter wind speed,and surface air pressure were generated by combining the fifth-generation atmospheric reanalysis for European Center for Medium-Range Weather Forecasts(ERA5),a short-term high-resolution atmospheric simulation,and in situ observations,and the downward longwave radiation was calculated using semi-physical *** cross-validation and independent-validation demonstrated that most variables in the developed dataset outperformed those in widely used reanalysis datasets,including ERA5,ERA5-Land,and the Global Land data Assimilation System(GLDAS).This dataset is expected to be beneficial for climate analyses and modeling applications of land-surface processes on the TP.

关键词： Meteorological data Third Pole High-resolution data downscaling and merging

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Full-waveform inversion via the scaled boundary finite element method

arXiv

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arXiv 2025年

作者： Daneshyar, Alireza Kollmannsberger, Stefan Chair of Computational Modeling and Simulation Technical University of Munich Germany Data Science in Civil Engineering Bauhaus-Universität Weimar Germany

We begin by addressing the time-domain full-waveform inversion using the adjoint method. Next, we derive the scaled boundary semi-weak form of the scalar wave equation in heterogeneous media through the Galerkin method. Unlike conventional scaled boundary finite element formulations, the resulting system incorporates variable density and two additional terms involving its spatial derivative. As a result, the coefficient matrices are no longer constant over the elements but instead depend on the so-called radial coordinate, rendering the common solution methods such as low-frequency expansion and continued fractions—both of which assume that these matrices can be defined solely by boundary values—inapplicable. In this context, we introduce a radial discretization scheme for solving the transient scalar wave equation with spatially varying density within the framework of the scaled boundary finite element method. This approach begins with temporal discretization in the time domain, followed by spatial discretization along the radial coordinate. We employ the Newmark method for the former and a finite differencing scheme for the latter. However, the choices underlying our ansatz are made for demonstration purposes and remain flexible. Following these discretizations, we introduce an algorithmic condensation procedure to compute the dynamic stiffness matrices of the elements on the fly. Therefore, we maneuver around the need to introduce auxiliary unknowns into the global algebraic system. As a result, the optimization problem is structured in a two-level hierarchy. The higher level computes the nodal wavefields on a relatively coarse mesh, while the lower level provides the gradient of the cost function for a finely distributed set of model parameters. We obtain the Fréchet kernel by computing the zero-lag cross-correlations of the forward and adjoint wavefields, and solve the minimization problems iteratively by moving downhill on the cost function hypersurface throug

关键词： Cost functions

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Effect of Overdamped Phonons on the Fundamental Band Gap of Perovskites

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Physical Review Letters 2025年第1期134卷 016403-016403页

作者： Xiangzhou Zhu David A. Egger Physics Department TUM School of Natural Sciences Technical University of Munich 85748 Garching Germany Atomistic Modeling Center Munich Data Science Institute Technical University of Munich 85748 Garching Germany

Anharmonic atomic motions can strongly influence the optoelectronic properties of materials but how these effects are connected to the underlying phonon band structure is not understood well. We investigate how the electronic band gap is influenced by overdamped phonons, which occur in an intriguing regime of phonon-phonon interactions where vibrational lifetimes fall below one oscillation period. We contrast the anharmonic halide perovskite CsPbBr3, known to exhibit overdamped phonons in its cubic phase, with the anharmonic oxide perovskite SrTiO3 where the phonons are underdamped at sufficiently high temperatures. Our results show that overdamped phonons strongly impact the band gap and cause slow dynamic fluctuations of electronic levels that have been implicated in the unique optoelectronic properties of halide perovskites. This finding is enabled by developing augmented stochastic Monte Carlo methods accounting for phonon renormalization and imaginary modes that are typically neglected. Our work provides guidelines for capturing anharmonic effects in theoretical calculations of materials.

关键词： Perovskite

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