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Emulating Neuromorphic and In-Memory Computing Utilizing Defect engineering in 2D-Layered WSeOx and WSe2 Thin Films by Plasma-Assisted Selenization Process

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Advanced Functional materials 2023年第38期33卷

作者： Chaudhary, Mayur Yang, Tzu-Yi Chen, Chieh-Ting Lai, Po-Chien Hsu, Yu-Chieh Peng, Yu-Ren Kumar, Ashish Lee, Chih-Hao Chueh, Yu-Lun Department of Materials Science and Engineering National Tsing-Hua University Hsinchu30013 Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan College of Semiconductor Research National Tsing-Hua University Hsinchu30013 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan Department of Physics National Sun Yat-Sen University Kaohsiung80424 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu30013 Taiwan

The neuromorphic and in-memory computing using memristors are promising for the building of the next generation computing systems. However, the diffusion dynamics of metal ions/atoms inside the switching medium impose variability in conducting filament (CF) formation, thus limiting their use in von-Neumann architecture. The precise modulation on the diffusion of metal ions/atoms and their reduction/oxidation probability holds promise to overcome the speed, size, and energy issues of present-day computers. Here, this study shows that the diffusion of metal ions can be modulated by defects inside the switching medium and confines metal filaments in a precise 1D channel. This filament confinement by the defect engineering leads to an anomalous switching mechanism with two interchangeable modes: unipolar threshold and bipolar modes. The variation between two modes can be modulated by controlling defects in the structures, leading to a uniform switching with low SET/RESET voltage variations of 17.3% and −17.6%, respectively. Moreover, the convolutional neural network is implemented to emulate synaptic plasticity and image recognition to achieve recognition accuracy of 87% due to a highly linear weight update, demonstrating its potential for in-memory computing. © 2023 Wiley-VCH GmbH.

关键词： Image recognition

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Dose Requirements for Fused Multi-Modal Electron Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1968-1969页

作者： Manassa, Jason Schwartz, Jonathan Jiang, Yi Zheng, Huihuo Fessler, Jeffrey A. Wendy Di, Zichao Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Advanced Photon Source Facility Argonne National Laboratory Lemont IL United States Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program University of Michigan Ann Arbor MI United States

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Phase Change materials Employment for Battery Thermal Management System in Electric and Hybrid Vehicles: A Review

SSRN

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SSRN 2022年

作者： Wazeer, Adil Das, Apurba Abeykoon, Chamil Sinha, Arijit Karmakar, Amit School of Laser Science and Engineering Jadavpur University West Bengal Kolkata700032 India Department of Aerospace and Applied Mechanics IIEST Shibpur West Bengal Howrah711103 India North West Composites Centre Aerospace Research Institute Department of Materials Faculty of Science and Engineering University of Manchester ManchesterM13 9PL United Kingdom Department of Metallurgical Engineering Kazi Nazrul University West Bengal Asansol713340 India Department of Mechanical Engineering Jadavpur University West Bengal Kolkata700032 India

Considered as one of the most vital components in an electric vehicle, power batteries produce copious amount of heat energy throughout the course of charging and discharging. Battery thermal management system (BTMS) is crucial for the maximum efficiency and is designed to uphold and conserve the optimum temperature and reliability of the battery. Employment of phase change materials (PCMs) for thermal management has attracted attention in recent years owing to its characteristics such as lesser weight, improved energy efficiency, less intricacy and better thermal homogeneity. These materials are a potential substitute that is economical and simple in operation. PCMs could be utilized as active or passive systems, empowering the universal system to nurture near-autonomous performances. This work consists of the discussions on battery thermal management system (BTMS), PCM, enhancement of PCM thermal conductivity, thermal management schemes and finally concluding with the application sections. The commonly employed heat transfer improvement approaches (namely application of thermally conductive particles, metal fin, metal foam and expanded graphite matrix) for PCMs by various researchers are presented. Newly developed thermal management configurations like multi-layer PCMs and sandwiched are also discussed. Additionally, thermal management system merging PCM cooling via air or liquid is also summarized. © 2022, The Authors. All rights reserved.

关键词： Phase change materials

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Local Order Metrics for Two-Phase Media Across Length Scales

arXiv

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arXiv 2022年

作者： Torquato, Salvatore Skolnick, Murray Kim, Jaeuk Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States

The capacity to devise order metrics for microstructures of multiphase heterogeneous media is a highly challenging task, given the richness of the possible geometries and topologies of the phases that can arise. This investigation initiates a program to formulate order metrics to characterize the degree of order/disorder of the microstructures of two-phase media in d-dimensional Euclidean space Rd across length scales. In particular, we propose the use of the local volume-fraction variance σV2 (R) associated with a spherical window of radius R as an order metric. We determine σV2 (R) as a function of R for 22 different models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. We find that the local volume-fraction variance as well as asymptotic coefficients and integral measures derived from it provide reasonably robust and sensitive order metrics to categorize disordered and ordered two-phase media across all length scales. © 2022, CC BY-SA.

关键词： Volume fraction

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Layer-dependent optically-induced spin polarization in InSe

arXiv

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arXiv 2022年

作者： Nelson, Jovan Stanev, Teodor K. Lebedev, Dmitry LaMountain, Trevor Tyler Gish, J. Zeng, Hongfei Shin, Hyeondeok Heinonen, Olle Watanabe, Kenji Taniguchi, Takashi Hersam, Mark C. Stern, Nathaniel P. Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Material Science & Engineering Northwestern University EvanstonIL60208 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Material Science Division Argonne National Laboratory LemontIL60439 United States Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL60208 United States

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for opto-electronics due to its high electron mobility, tunable direct bandgap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules suggesting potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photo-luminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g-factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab-initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for opto-spintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material. Copyright © 2022, The Authors. All rights reserved.

关键词： Spin dynamics

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Gallium Oxide as Photonic Integrated Platforms in the UV–Visible Spectra

Gallium Oxide as Photonic Integrated Platforms in the UV–Vi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jingan Zhou Xuan Zhao Rui Xu Xiang Zhang Kai Fu Tao Li Mingfei Xu Shisong Luo Ziyi He Jacob T. Robinson Hanyu Zhu Pulickel M. Ajayan Xiaodong Zhang Yuji Zhao Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona USA Nanofabrication Facility Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Suzhou China

We report the Ga 2 O 3 as photonic integrated platforms and its nonlinear optical effects in the UV–visible spectra. A low propagation loss of 3.7 dB/cm and second-order susceptibility of χ (2) =4.89×10 -3 pm... 详细信息

ISBN: (纸本)9781957171258

关键词： Material properties Nonlinear effects Nonlinear susceptibility Optical components Thin films Visible light communications

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Multiscale computational understanding and growth of 2D materials:a review

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npj Computational materials 2020年第1期6卷 1484-1501页

作者： Kasra Momeni Yanzhou Ji Yuanxi Wang Shiddartha Paul Sara Neshani Dundar E.Yilmaz Yun Kyung Shin Difan Zhang Jin-Wu Jiang Harold S.Park Susan Sinnott Adri van Duin Vincent Crespi Long-Qing Chen Mechanical Engineering Department Louisiana Tech UniversityRustonLA 71272USA Department of Mechanical Engineering University of AlabamaTuscaloosaALUSA Institute for Micromanufacturing Louisiana Tech UniversityRustonLAUSA Materials Research Institute The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA 2-Dimensional Crystal Consortium The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Physics The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical Engineering Iowa State UniversityAmesIA 50010USA Mechanical Engineering Department The Pennsylvania State UniversityUniversity ParkPA 16802USA Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy EngineeringShanghai UniversityShanghai 200072China Department of Mechanical Engineering Boston UniversityBostonMA 02215USA Department of Engineering Science and Mechanics The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Mathematics The Pennsylvania State UniversityUniversity ParkPA 16802USA

The successful discovery and isolation of graphene in 2004,and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional(2D)materials that explore their growth,new atomic-scale physics,and potential device *** review aims to provide an overview of theoretical,computational,and machine learning methods and tools at multiple length and time scales,and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene.

关键词： synthesis utilized subsequent

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Highly Sensitive, Ultra-Reliable Flexible Piezoelectret Sensor for Non-Contact Sitting Motion Tacking and Physiological Signal Monitoring

SSRN

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SSRN 2023年

作者： Ma, Xingchen Qi, Yan Niu, Yangyang Zhang, Qi Xiang, Xinhao Zhang, Ke He, Pengfei Dai, Ying Niu, Wenxin Zhang, Xiaoqing Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China School of Medicine Tongji University Shanghai201619 China Department of Rehabilitation Sciences Tongji University School of Medicine Shanghai200092 China Shenzhen Data Thinking Technology Co. Ltd. Shenzhen518055 China School of Aerospace Engineering and Applied Mechanics Tongji University Shanghai200092 China

Non-contact, cost-effective, multifunctional and robust monitoring platform without mechanical wearing burden is highly desired to enhance the immersive experience for users during the long-term health management. However, most reported intelligent electronics in human life need to be adhered conformably to key human body parts for accurate signal detection, giving rising to activity interference and wearing burden in the long-term usage. Herein, a highly sensitive flexible film pressure sensor with a folded structure configuration assembled from irradiation-cross linked polypropylene (IXPP) piezoelectret film featured with piezoelectric-like dynamic electrical output attributes are reported. Such folded structure incorporated specially into the device design endows the piezoelectric film sensor with merits such as enhanced sensitivity, high signal-to-noise ratio and excellent anti-interference capacity. Consequently, the sensor can achieve a linear pressure sensitivity of 105 pC/kPa over a wide pressure range of 0.6 to 62.4 kPa, a pressure resolution of 0.98%, and fast response/recovery time of 40 ms. The flexible piezoelectric film sensors are embedded into cushions and attached to the back of chairs for application demonstrations of non-contact sensing. Remarkably, low-frequency human motion and weak physiological signal detection are achieved in a non-contact mode by a highly sensitive piezoelectric sensing principle in an all-in-one configuration, simplifying the signal processing and monitoring system design. A vital sign monitoring system based on the intelligent cushion is further developed to unlock the health status of sedentary crowd. Moving forward, such piezoelectret sensors show the capability to be connected to the commercial "C-life" smart home ecosystem, presenting its strong practicability and application prospects in smart households and remote healthcare beyond the laboratory. © 2023, The Authors. All rights reserved.

关键词： Pressure sensors

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Hyperuniformity of maximally random jammed packings of hyperspheres across spatial dimensions

arXiv

引用

arXiv 2023年

作者： Maher, Charles Emmett Jiao, Yang Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Materials Science and Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Materials Institute Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

The maximally random jammed (MRJ) state is the most random (i.e., disordered) configuration of strictly jammed (mechanically rigid) nonoverlapping objects. MRJ packings are hyperuniform, meaning their long-wavelength density fluctuations are anomalously suppressed compared to typical disordered systems, i.e., their structure factors S(k) tend to zero as the wave number |k| tends to zero. Here, we show that generating high-quality strictly jammed states for Euclidean space dimensions d = 3, 4, and 5 is of paramount importance in ensuring hyperuniformity and extracting precise values of the hyperuniformity exponent α > 0 for MRJ states, defined by the power-law behavior of S(k) ∼ |k|α in the limit |k| → 0. Moreover, we show that for fixed d it is more difficult to ensure jamming as the particle number N increases, which results in packings that are nonhyperuniform. Free-volume theory arguments suggest that the ideal MRJ state does not contain rattlers, which act as defects in numerically generated packings. As d increases, we find that the fraction of rattlers decreases substantially. Our analysis of the largest truly jammed packings suggests that the ideal MRJ packings for all dimensions d ≥ 3 are hyperuniform with α = d − 2, implying the packings become more hyperuniform as d increases. The differences in α between MRJ packings and recently proposed Manna-class random close packed (RCP) states, which were reported to have α = 0.25 in d = 3 and be nonhyperuniform (α = 0) for d = 4 and d = 5, demonstrate the vivid distinctions between the large-scale structure of RCP and MRJ states in these dimensions. Our paper clarifies the importance of the link between true jamming and hyperuniformity and motivates the development of an algorithm to produce rattler-free three-dimensional MRJ packings. © 2023, CC BY.

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