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

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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

Searching for neutrino signals correlated with LHAASO diffuse Galactic emission 38

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Searching for neutrino signals correlated with LHAASO diffus...

38th International Cosmic Ray Conference, ICRC 2023

作者： Li, Wenlian Huang, Tian-Qi Xu, Donglian He, Huihai Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China Key Laboratory of Particle Astrophysics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Physics Sun Ya

The diffuse Galactic γ-ray emission is produced from the interaction of cosmic rays and interstellar medium or radiation fields in the Galaxy, where neutrino production is also expected. Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported the measurements of the diffuse γ-ray from the Galactic plane with energies above 10 TeV. In this study, we construct the neutrino emission template based on LHAASO’s observation and search for diffuse neutrinos accompanying the LHAASO diffuse Galactic γ-ray emission using ten-year IceCube track events. No significant signals are found. We set 90% confidence level upper limits, resulting in the neutrino flux of dν/dEν = 1.27 × 10−14(Eν/25TeV)−2.99TeV−1cm−2s−1 for the diffuse γ-ray flux map with a 1.5σ significance cut. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

Influence of the dissipative topological edge state on quantized transport in MnBi2Te4

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Physical Review B 2022年第16期105卷 165411-165411页

作者： Weiyan Lin Yang Feng Yongchao Wang Zichen Lian Hao Li Yang Wu Chang Liu Yihua Wang Jinsong Zhang Yayu Wang Xiaodong Zhou Jian Shen State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China Department of Physics Fudan University Shanghai China Beijing Academy of Quantum Information Science Beijing China Beijing Innovation Center for Future Chips Tsinghua University Beijing China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing China School of Materials Science and Engineering Tsinghua University Beijing China Tsinghua-Foxconn Nanotechnology Research Center Department of Physics Tsinghua University Beijing China Department of Mechanical Engineering Tsinghua University Beijing China Shanghai Research Center for Quantum Sciences Shanghai China Frontier Science Center for Quantum Information Beijing China Shanghai Qi Zhi Institute Shanghai China Zhangjiang Fudan International Innovation Center Fudan University Shanghai China Collaborative Innovation Center of Advanced Microstructures Nanjing China

The beauty of the quantum Hall (QH) effect is the metrological precision of Hall resistance quantization that originates from the topological edge states. Understanding the factors that lead to quantization breakdown not only provides important insights on the nature of the topological protection of these edge states, but is beneficial for device applications involving such quantized transport. In this work, we combine conventional transport and real space conductivity mapping to investigate whether the quantization breakdown is tied to the disappearance of edge state in the hotly studied MnBi2Te4 system. Our experimental results unambiguously show that topological edge state does exist when quantization breakdown occurs. Such edge state is dissipative in nature and could lead to a quantization breakdown due to its diffusive character causing overlapping with bulk and other edge states in real devices. Our findings bring attention to issues that are generally inaccessible in the transport study of QH, but can play important roles in practical measurements and device applications.

关键词： Quantum anomalous Hall effect Topological insulators Scanning probe microscopy

MalCommunity: A graph-based evaluation model for malware family clustering 4th

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MalCommunity: A graph-based evaluation model for malware fam...

4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Chen, Yihang Liu, Fudong Shan, Zheng Liang, Guanghui State Key of Laboratory of Mathematical Engineering and Advanced Computing ZhengzhouHenan450001 China

ISBN: (纸本)9789811322020

Malware clustering analysis plays an important role in large-scale malware homology analysis. However, the generation approach of the ground truth data is usually ignored. The Labels from Anti-virus(AV) engines are most commonly used but some of them are inaccurate or inconsistent. To overcome the drawback, many researchers make ground truth data based on voting mechanism such as AVclass, but this method is difficult to evaluate different-granularity clustering results. Graph-based method like VAMO is more robust but it needs to maintain a large-size database. In this paper, we propose a novel evaluation model named MalCommunity based on the graph named Malware Relation Graph. Different from VAMO, the construction of the graph is free from a large-size database and just needs the AV label information of the samples in the test set. We introduce community detection algorithm Fast Newman to divide the sample set and use modularity parameter to measure the target clustering results. The experiment results indicate that our model has the ability of noise immunity of malware family classification inconsistency and granularity inconsistency from AV labels. Our model is also convenient to evaluate different-granularity clustering methods with different heights. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Graph theory

Binary semantic Similarity Comparison based on software gene

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Binary semantic Similarity Comparison based on software gene

作者： Wenjie Sun Yihang Chen Zheng Shan Fudong liu Qiuna Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Materials branch of State Grid Henan Electric Power Company

The similarity of binary code is widely used in code copyright protection, vulnerability mining, malicious code analysis and etc. In this paper, we proposed a method for measuring/evaluating the similarity of two binary files based on software genes. Some of Natural language processing methods were introduced into program semantics analysis, including word2vec and doc2vec models to generate assembly instruction embeddings and gene semantic embeddings. Then the longest common subsequence method was applied to evaluate the software similarity. Experiments show that our method can effectively evaluate the similarity of binary files.

关键词： Binary semantic Similarity Comparison based on software gene

Dynamic Nonreciprocity with a Kerr Nonlinear Resonator

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arXiv 2022年

作者： Pan, Rui-Kai Tang, Lei Xia, keyu Nori, Franco College of Engineering and Applied Sciences National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China College of Physics and Electronic Engineering Sichuan Normal University Chengdu610101 China Jiangsu Key Laboratory of Artificial Functional Materials Nanjing University Nanjing210023 China Shishan Laboratory Suzhou Campus of Nanjing University Suzhou215000 China RIKEN Quantum Computing Center RIKEN Cluster for Pioneering Research Saitama Wako-shi351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States

On-chip optical nonreciprocal devices are vital components for integrated photonic systems and scalable quantum information processing. Nonlinear optical isolators and circulators have attracted considerable attention because of their fundamental interest and their important advantages in integrated photonic circuits. However, optical nonreciprocal devices based on Kerr or Kerr-like nonlinearity are subject to dynamical reciprocity when the forward and backward signals coexist simultaneously in a nonlinear system. Here, we theoretically propose a method for realizing on-chip nonlinear isolators and circulators with dynamic nonreciprocity. Dynamic nonreciprocity is achieved via the chiral modulation on the resonance frequency due to coexisting self- and cross-Kerr nonlinearities in an optical ring resonator. This work showing dynamic nonreciprocity with a Kerr nonlinear resonator can be an essential step toward integrated optical isolation. © 2022, CC BY.

关键词： Nonlinear optics

Upper security bounds for coherent-one-way quantum key distribution

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arXiv 2020年

作者： González-Payo, Javier Trényi, Róbert Wang, Weilong Curty, Marcos Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450001 China Henan Key Laboratory of Network Cryptography Technology ZhengzhouHenan450001 China

The performance of quantum key distribution (QKD) is severely limited by multi-photon pulses emitted by laser sources due to the photon-number splitting attack. Coherent-one-way (COW) QKD has been introduced as a promising solution to overcome this limitation, and thus extend the achievable distance of practical QKD. Indeed, thanks to its experimental simplicity, the COW protocol is already used in commercial applications. Here, we derive simple upper security bounds on its secret key rate, which suggest that it scales quadratically with the system’s transmittance, thus solving a long-standing problem. That is, in contrast to what has been claimed, this approach does not seem to be appropriate for long-distance QKD transmission. Our findings imply that all long-distance implementations of the COW protocol performed so far are actually insecure. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum cryptography

Graph classification based on skeleton and component features

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arXiv 2021年

作者： Liu, Xue Wei, Wei Feng, Xiangnan Cao, Xiaobo Sun, Dan Beijing System Design Institute of Electro-Mechanic Engineering Beijing100854 China School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen Guangdong518066 China

Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation. To address this weakness, we propose a novel graph embedding algorithm named GraphCSC that realizes classification based on skeleton information using fixed-order structures learned in anonymous random walks manner, and component information using different size subgraphs. Two graphs are similar if their skeletons and components are both similar, thus in our model, we integrate both of them together into embeddings as graph homogeneity characterization. We demonstrate our model on different datasets in comparison with a comprehensive list of up-to-date state-of-the-art baselines, and experiments show that our work is superior in real-world graph classification tasks. © 2021, CC BY.

关键词： Musculoskeletal system

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二维材料最新研究进展

物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion