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Design of small-scale gradient coils in magnetic resonance imaging by using the topology optimization method

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Chinese Physics B 2018年第5期27卷 91-100页

作者： Hui Pan Feng Jia Zhen-Yu Liu maxim Zaitsev Juergen Hennig Jan G Korvink Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China University of Chinese Academy of Sciences Beijing 100049China Deptartment of Radiology Medical PhysicsMedical Center University of FreihurgFaculty of MedicineUniversity of FreiburgFreiburg 79106Germany Institute of Microstructure Technology Karlsruhe Institute of Technology(KIT)Karlsruhe 76344Germany

A topology optimization method based on the solid isotropic material with penalization interpolation scheme is utilized for designing gradient coils for use in magnetic resonance *** the popular stream function method,the proposed method has design variables that are the distribution of conductive material.A voltage-driven transverse gradient coil is proposed to be used as micro-scale magnetic resonance imaging(MRI)gradient coils,thus avoiding introducing a coil-winding pattern and simplifying the coil *** proposed method avoids post-processing errors that occur when the continuous current density is approximated by discrete wires in the stream function *** feasibility and accuracy of the method are verified through designing the z-gradient and y-gradient coils on a cylindrical *** design results show that the proposed method can provide a new coil layout in a compact design space.

关键词： topology optimization method gradient coils solid isotropic material with penalization magnetic resonance imaging

Footprints of axion-like particle in pulsar timing array data and James Webb Space Telescope observations

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Science China(Physics,Mechanics & Astronomy) 2024年第11期67卷 55-62页

作者： Shu-Yuan Guo maxim Khlopov Xuewen Liu Lei Wu Yongcheng Wu Bin Zhu Department of Physics Yantai UniversityYantai 264005 China Institute of Physics Southern Federal UniversityRostov on Don 344090 Russia National Research Nuclear University MEPhI(Moscow Engineering Physics Institute) Moscow 115409 Russia Virtual Institute of Astroparticle Physics Paris 75018 France Department of Physics and Institute of Theoretical Physics Nanjing Normal UniversityNanjing 210023 China

Several pulsar timing array(PTA) collaborations have recently reported the evidence for a stochastic gravitational-wave background(SGWB), which can unveil the formation of primordial seeds of inhomogeneities in the early universe. With the SGWB parameters inferred from PTAs data, we can make a prediction of the seeds for early galaxy formation from the domain walls in the axion-like particles(ALPs) field distribution. This also naturally provides a solution to the observation of high redshifts by the James Webb Space Telescope. The predicted photon coupling of the ALP is within the reach of future experimental searches.

关键词： stochastic gravitational-wave background pulsar timing array axion-like particle JWST

Understanding the Li diffusion mechanism and positive effect of current collector volume expansion in anode free batteries

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Chinese Physics B 2020年第6期29卷 17-24页

作者： Yan Zhuang Zheyi Zou Bo Lu Yajie Li Da Wang maxim Avdeev Siqi Shi State Key Laboratory of Advanced Special Steel Shanghai Key Laboratory of Advanced FerrometallurgySchool of Materials Science and EngineeringShanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering ScienceShanghai UniversityShanghai 200444China Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai UniversityShanghai 200444China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia

In anode free batteries(AFBs), the current collector acts as anode simultaneously and has large volume expansion which is generally considered as a negative effect decreasing the structural stability of a battery. Moreover, despite many studies on the fast lithium diffusion in the current collector materials of AFB such as copper and aluminum, the involved Li diffusion mechanism in these materials remains poorly understood. Through first-principles calculation and stress-assisted diffusion equations, here we study the Li diffusion mechanism in several current collectors and related alloys and clarify the effect of volume expansion on Li diffusion respectively. It is suggested that due to the lower Li migration barriers in aluminum and tin, they should be more suitable to be used as AFB anodes, compared to copper, silver, and lead. The Li diffusion facilitation in copper with a certain number of vacancies is proposed to explain why the use of copper with a thickness≤100 nm as the protective coating on the anode improves the lifetime of the batteries. We show that the volume expansion has a positive effect on Li diffusion via mechanical–electrochemical coupling. Namely, the volume expansion caused by Li diffusion will further induce stress which in turn affects the diffusion. These findings not only provide in-depth insight into the operating principle of AFBs, but also open a new route toward design of improved anode through utilizing the positive effect of mechanical–electrochemical coupling.

关键词： anode free battery current collector Li diffusion mechanism mechanical-electrochemical coupling stress-assisted diffusion

Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

About Some Aspects of Use of Optical Sensors for Monitoring the Aquatic Environment

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Journal of Environmental & Earth Sciences 2024年第1期6卷 1-10页

作者： Ferdenant Mkrtchyan Vladimiir Soldatov maxim Mkrtchyan Kotelnikov Institute of Radioengineering&Electronics RAS Fryazino BranchVvedensky Sq.1FryazinoMoscow141190Russia

Multi-channel polarization optical technology is increasingly used for prompt monitoring of water *** devices during the assessment of water quality determine the intensity of light through the studied aquatic *** devices measure the spectrum of weakening of light through the aquatic *** devices receive spectra in vertical and horizontal *** presented article develops an adaptive optical hardware and image system for monitoring water *** system is *** consists of 2 parts:1)automated spectrophotometer-refractometer,and 2)adaptive *** system is equipped with a corresponding algorithmic and software,including algorithms for identifying spectral curves,databases and knowledge of spectral curves algorithms for solving reverse *** presented system is original since it differs from modern foreign systems by a new method of spectrophotometric and spectroellipsometric measurements,an original elemental base of polarization optics and a comprehensive mathematical approach to assessing the quality of a water *** are no rotating polarization elements in the ***,this makes it possible to increase the signal-to-noise ratio and,as a result,improve measurement stability and simplify multichannel spectrophotometers and *** proposed system can be used in various water systems where it is necessary to assess water quality or identify the presence of a certain set of chemical elements.

关键词： Monitoring Aquatic environment Polarization optics Water object Pollutants Spectral images Classification Identification

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

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npj Computational Materials 2021年第1期7卷 909-916页

作者： Ayana Ghosh Bobby G.Sumpter Ondrej Dyck Sergei V.Kalinin maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines,allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of ***,applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments,where the network trained for one set of imaging conditions becomes sub-optimal for different *** limitation is particularly stringent in the quest to have an automated experiment setting,where retraining or transfer learning becomes impractical due to the need for human intervention and associated *** we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature *** approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the *** methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

关键词： learning experiment enable

Software for Evaluating Ionic Conductivity of Inorganic-Polymer Composite Solid Electrolytes

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Energy Material Advances 2023年第1期4卷 353-362页

作者： Yuqing Ding Bing He Da Wang maxim Avdeev Yajie Li Siqi Shi School of Materials Science and Engineering Shanghai UniversityShanghai 200444China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Australian Nuclear Science and Technology Organisation Sydney 2232Australia School of Chemistry The University of SydneySydney 2006Australia Materials Genome Institute Shanghai UniversityShanghai 200444China

Inorganic-polymer composite solid electrolytes(IPCSEs)obtained by filling the polymer matrix with inorganic materials usually have higher ionic conductivity compared with individual *** important increase in ionic conductivity is explained in terms of the new percolation paths formed by the highly conductive interface between inorganic filler and *** conduction in such systems can be investigated using the effective medium theory(EMT)and random resistance model(RRM).EMT can be used to analyze the effect of filler size on the ionic conductivity of disordered IPCSEs,while RRM can describe the composites with inorganic fillers of various shapes(nano-particles,nano-wires,nano-sheets,and nano-networks)in ordered or disordered ***,we present software evaluating the ionic conductivity in IPCSEs by combining EMT and *** approach is illustrated by considering the size,shapes,and arrangements of inorganic *** ionic conductivities of different types of IPCSEs are predicted theoretically and found in good agreement with the experimental *** software can be used as an auxiliary tool to design composite electrolytes.

关键词： composite composites inorganic

Gaussian process analysis of electron energy loss spectroscopy data:multivariate reconstruction and kernel control

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npj Computational Materials 2021年第1期7卷 1395-1404页

作者： Sergei V.Kalinin Andrew R.Lupini Rama K.Vasudevan maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA

Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data *** multivariate linear unmixing methods generally explore similarities in the energy dimension,but ignore correlations in the spatial *** the same time,Gaussian process(GP)explicitly incorporate spatial correlations in the form of kernel functions but is computationally ***,we implement a GP method operating on the full spatial domain and reduced representations in the energy *** this multivariate GP,the information between the components is shared via a common spatial kernel structure,while allowing for variability in the relative noise magnitude or image *** explore the role of kernel constraints on the quality of the reconstruction,and suggest an approach for estimating them from the experimental *** further show that spatial information contained in higher-order components can be reconstructed and spatially localized.

关键词： domain kernel multivariate

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