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One-dimensional topological superconductivity at the edges of twisted bilayer graphene nanoribbons

Physical Review B 2019年第9期100卷 094531-094531页

作者： Zhen-Hua Wang Fuming Xu Lin Li Rong Lü Bin Wang Wei-Qiang Chen Shenzhen Key Laboratory of Advanced Thin Films and Applications College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China Beijing Computational Science Research Center Beijing 100193 China College of Physics and Electronic Engineering and Center for Computational Sciences Sichuan Normal University Chengdu 610068 China Department of Physics Tsinghua University Beijing 100084 China Collaborative Innovation Center of Quantum Matter Beijing 100084 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China

Twisted bilayer graphene is one of the simplest van der Waals structures, and its inhomogeneous interlayer coupling can induce rich electronic properties. In twisted bilayer graphene nanoribbons (tBLGNRs), the interlayer coupling strengths are different for two ribbon edges due to the inhomogeneous bonding, which splits the edge states into two individuals in energy. The lower-energy state, localizing at the ribbon edge with the stronger interlayer coupling, is a good candidate to generate one-dimensional (1D) topological superconductivity in the presence of Rashba spin-orbit coupling, Zeeman field, and s-wave superconductivity. Majorana zero modes (MZMs) are found to be localized at both ends of this edge. The topological invariants of the system are explored by evaluating the Berry phase for infinite-length ribbons and Majorana polarization for quasi-1D ribbons, giving the same topological phase diagram. More importantly, by adjusting interlayer dislocation and uniaxial strain of tBLGNRs across the critical values, the lower-energy edge changes and 1D topological superconductivity can “jump” from one ribbon edge to the other one. Finally, by applying a gate voltage bias between bilayers or changing the interlayer distance, a MZM can transfer along the ribbon edge. The tBLGNRs provide an alternative platform to study 1D topological superconductivity and MZMs.

关键词： Majorana bound states Topological superconductors Graphene Tight-binding model

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Synergy of Binary Substitutions for Improving the Cycle Performance in LiNiO2 Revealed by Ab Initio Materials Informatics

arXiv

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arXiv 2020年

作者： Yoshida, Tomohiro Maezono, Ryo Hongo, Kenta Department of Computer-Aided Engineering and Development Sumitomo Metal Mining Co. Ltd. 3-5 Sobiraki-cho NiihamaEhime792-0001 Japan School of Information Science JAIST Asahidai 1-1 IshikawaNomi923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa SaitamaWako351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen TsukubaIbaraki305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho SaitamaKawaguchi-shi322-0012 Japan

We explore LiNiO2-based cathode materials with two-element substitutions by an ab initio simulation based materials informatics (AIMI) approach. According to our previous study, a higher cycle performance strongly correlates with less structural change during charge–discharge cycles;the latter can be used for evaluating the former. However, if we target the full substitution space, full simulations are infeasible even for all binary combinations. To circumvent such an exhaustive search, we rely on Bayesian optimization. Actually, by searching only 4% of all the combinations, our AIMI approach discovered two promising combinations, Cr-Mg and Cr-Re, whereas each atom itself never improved the performance. We conclude that the synergy never emerges from a common strategy restricted to combinations of "good" elements that individually improve the performance. In addition, we propose a guideline for the binary substitutions by elucidating the mechanism of crystal structure change. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary alloys

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First principles calculations of superconducting critical temperature of ThCr2Si2-type structure

arXiv

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arXiv 2019年

作者： Sinaga, Gewinner Senderanto Utimula, Keishu Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan PRESTO JST Kawaguchi Saitama332-0012 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

High critical temperature (Tc) superconductor has a great potential in many industrial applications. However, discovering a compound having high Tc is still remaining a big challenge for experimental approach due to time-consuming and high cost. In this paper, we investigated the critical temperature (Tc) of several compounds of ThCr2Si2-type structure (space group I4/mmm) since some of them had already been investigated and trusted as the potential candidates for superconductivity. First principle calculation was performed to compute the critical temperature (Tc) based on allen-dynes equation modification of McMillian formula. In order to confirm our calculation scheme, we compared our result with compounds which had been experimentally determined obtained from NIMS database. The result showed a very good agreement with experimental data. Based on this scheme, finally, we found ThCu2Si2 and ThAu2Si2 which were most likely to exhibit a superconductivity around 3.88 K and 4.27. Since, these compounds have not been studied experimentally, then we futher investigated their electronic properties showing non-negative phonons which indicate the dynamical stability of the compounds at ambient condition. Band structure and DOS analysis showed metallic nature where the highest contributor come from d-orbital of rare-earth element and p-orbital of Si emerging a mixture of covalent, ionic and metallic bonds exists in both the compounds. Finally, we calculated the electron phonon coupling coefficient showing a quite large different (27%) leading to the increment of Tc, however, both of compounds possess three dimensional electron-like and hole-like multi-band fermi surface which is related to the phonon mediated which is still under electron-phonon mediated superconductivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Temperature

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Molecular dynamics simulation approach to explore atomistic molecular mechanism of peroxidase activity of apoptotic cytochrome c mutants

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Informatics in Medicine Unlocked 2018年 11卷 51-60页

作者： Muneeswaran, Gurusamy Pandiaraj, Manickam Kartheeswaran, Subramanian Sankaralingam, Muniyandi Muthukumar, Kaliappan Karunakaran, Chandran Biomedical Research Lab Department of Chemistry VHNSN College (Autonomous) Virudhunagar Tamilnadu India Department of Chemistry School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil 626 126 Tamilnadu India Electrodics and Electrocatalysis Division CSIR-Central Electrochemical Research Institute Karaikudi 630003 India Department of Master of Computer Applications School of Computing Kalasalingam Academy of Research and Education Krishnankoil 626 126 Tamilnadu India Department of Chemistry and Nano Science Ewha Womans University Seoul 03760 South Korea Applied Materials Sunnyvale 94085 CA United States

Mutations in cytochrome c (Cyt c) have been reported in tuning peroxidase activity, which in-turn cause Cyt c release from mitochondria and early apoptosis. However, the molecular tuning mechanism underlying this activity remains elusive. Herein, multiple 20 ns molecular dynamics (MD) simulations of wild type (WT), Y67F and K72W mutated Cyt c in aqueous solutions have been carried out to study how the changes in structural features alters the peroxidase activity of the protein. MD simulation results indicate that Y67F mutation caused, (i) increased distances between critical electron-transfer residues, (ii) higher fluctuations in omega loops, and (iii) weakening of intraprotein hydrogen bonds result in open conformation at heme crevice loop in Cyt c leading to an enhanced peroxidase activity. Interestingly, the aforementioned structural features are strengthened in K72W compared to WT and Y67F, which triggers K72W mutated Cyt c into a poor peroxidase. Essential dynamics results unveil that first two eigenvectors are responsible for overall motions of WT, Y67F and K72W mutated Cyt c. This study thus provides atomic level insight into molecular mechanism of peroxidase activity of Cyt c, which will help in designing novel Cyt c structures that is more desirable than natural Cyt c for biomedical and industrial processes. © 2018

关键词： Apoptosis Cytochrome c Molecular dynamics Mutations Peroxidase activity

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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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Topical sentence embedding for query focused document summarization

Topical sentence embedding for query focused document summar...

引用

2017 IJCAI Workshop on Semantic Machine Learning, SML 2017

作者： Gao, Yang Wei, Linjing Huang, Heyan Liu, Qian Beijing Eng. Res. Ctr. of High Vol. Language Information Processing and Cloud Computing Applications China BIT Beijing Advanced Innovation Center for Imaging Technology Capital Normal University China

Distributed vector representation for sentences have been utilized in summarization area, since it simplifies semantic cosine calculation between sentence to sentence as well as sentence to document. Many extension works have been done to incorporate latent topics and word embedding, however, few of them assign sentences with explicit topics. Besides, much sentence embedding framework follows the same spirit of prediction task about a word in the sentence, which omits the sentence-to-sentence coherence. To address these problems, we proposed a novel sentence embedding framework to collaborate the current sentence representation, word-based content and topic assignment of the sentence to predict the next sentence representation. The experiments on summarization tasks show our model outperforms state-of-the-art methods. Copyright © by the paper's authors. Copying permitted for private and academic purposes.

关键词： Semantics

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First-principles study of structural transition in LiNiO2 and high throughput screening for long life battery

arXiv

引用

arXiv 2019年

作者： Yoshida, Tomohiro Hongo, Kenta Maezono, Ryo Department of Computer-Aided Engineering and Development Sumitomo Metal Mining Co. Ltd. Minato-ku Tokyo105-8716 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

Herein, we performed ab initio screening to identify the best doping of LiNiO2 to achieve improved cycle performance in lithium ion batteries. The interlayer interaction that dominates the c-axis contraction and overall performance was captured well by density functional theory using van der Waals exchange-correlation functionals. The screening indicated that Nb-doping is promising for improving cycle performance. To extract qualitative reasonings, we performed data analysis in a materials informatics manner to obtain a reasonable regression to reproduce the obtained results. LASSO analysis implied that the charge density between the layers in the discharged state is the dominant factor influencing cycle performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Nickel compounds

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computing environments for reproducibility: Capturing the wholetale

arXiv

引用

arXiv 2018年

作者： Brinckman, Adam Chard, Kyle Ganey, Niall Hategan, Mihael Jones, Matthew B. Kowalik, Kacper Kulasekaran, Sivakumar Ludscher, Bertram Mecum, Bryce D. Nabrzyski, Jarek Stodden, Victoria Taylor, Ian J. Turk, Matthew J. Turner, Kandace School of Information Sciences University of Illinois at Urbana-Champaign Computation Institute University of Chicago Argonne National Laboratory Texas Advanced Computing Center University of Texas National Center for Ecological Analysis and Synthesis University of California Santa Barbara United States Center for Research Computing University of Notre Dame National Center for Supercomputing Applications University of Illinois Urbana-Champaign School of Computer Science Cardi University Cardi United Kingdom

The act of sharing scientific knowledge is rapidly evolving away from traditional articles and presentations to the delivery of executable objects that integrate the data and computational details (e.g., scripts and workows) upon which the findings rely. This envisioned coupling of data and process is essential to advancing science but faces technical and institutional barriers. The WholeTale project aims to address these barriers by connecting computational, data-intensive research efforts with the larger research process|transforming the knowledge discovery and dissemination process into one where data products are united with research articles to create filiving publications" or tales. The WholeTale focuses on the full spectrum of science, empowering users in the long tail of science, and power users with demands for access to big data and compute resources. We report here on the design, architecture, and implementation of the WholeTale environment. Copyright © 2018, The Authors. All rights reserved.

关键词： Metadata

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Virtualization detection strategies and their outcomes in public clouds

Virtualization detection strategies and their outcomes in pu...

引用

Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics (PrimeAsia)

作者： B. Asvija R. Eswari M B. Bijoy Centre for Development of Advanced Computing Bangalore India Dept. of Computer Applications National Institute of Technology Tiruchirappalli India

Virtualization detection on publicly exposed computing resources can have serious security implications. It can lead to the exploitation of known vulnerabilities in the virtualization software and also to carrying out attacks on shared virtual resources. The impact of such attacks can be very severe on public clouds, as numerous clients who subscribe to the cloud services share the infrastructure. Yet the threat of virtualization detection has been often overlooked by many cloud service providers. In this paper, we show how the three popular public clouds namely the Amazon EC2, Google computing Engine and the Microsoft Azure clouds are vulnerable to this risk. We summarize the various approaches used for virtualization detection and present the results obtained on the public clouds from an attacker's perspective. We demonstrate that the publicly exposed guest instances are not hardened to prevent this information leakage to the attackers. In addition to the prior known approaches, we also propose and demonstrate a new approach for detecting virtualization, based on the location and size of the descriptor tables.

关键词： Cloud computing Virtual machine monitors Virtualization Registers Google Engines Tools

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Author Correction: Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy

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Nature Photonics 2024年 998页

作者： Xuchen Shan Dajing Wang Jinlong Shi Hongyan Zhu Xiaolan Zhong Baolei Liu Weichang Hao Fan Wang Lei Ding Qian Peter Su Shihui Wen Dayong Jin Lei Jiang Chaohao Chen Yang Wang Zhaocun Huang Lingqian Chang Shen S. J. Wang Peter John Reece Wei Ren Xunyu Lu Jie Lu Lingdong Sun School of Physics Beihang University Beijing People’s Republic of China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field Infrastructure Hangzhou People’s Republic of China School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney New South Wales Australia Centre for Atomaterials and Nanomanufacturing School of Science RMIT University Melbourne Victoria Australia Institute for Biomedical Materials and Devices (IBMD) School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney Sydney New South Wales Australia Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo People’s Republic of China Key Laboratory of Bio-Inspired Materials and Interfaces Sciences Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing People’s Republic of China Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials Engineering Research School of Physics The Australian National University Canberra Australian Capital Territory Australia Key Laboratory of Biomechanics and Mechanobiology (Ministry of Education) Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing People’s Republic of China Computing and Information Systems Department Melbourne School of Engineering University of Melbourne Melbourne Victoria Australia School of Physics The University of New South Wales Sydney New South Wales Australia School of Chemistry & Chemical Engineering Shaanxi Normal University Xi’an People’s Republic of China Particles and Catalysis Research Laboratory School of Chemical Engineering The University of New South Wales Sydney New South Wales Australia Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology

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