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arXiv 2022年

作者： Xu, Qichen Shen, Zhuanglin Pereiro, Manuel Sjöqvist, Erik Herman, Pawel Eriksson, Olle Delin, Anna Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center StockholmSE-10691 Sweden KTH Royal Institute of Technology StockholmSE-10044 Sweden CAS Key Laboratory of Quantitative Engineering Biology Shenzhen Institute of Synthetic Biology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Physics and Astronomy Uppsala University Box 516 UppsalaSE-75120 Sweden Division of Computational Science and Technology School of Electrical Engineering and Computer Science KTH Royal Institute of Technology AlbaNova University Center StockholmSE-10691 Sweden

A long-standing and difficult problem in, e.g., condensed matter physics is how to find the ground state of a complex many-body system where the potential energy surface has a large number of local minima. Spin systems containing complex and/or topological textures, for example spin spirals or magnetic skyrmions, are prime examples of such systems. We propose here a genetic-tunneling-driven variance-controlled optimization approach, and apply it to two-dimensional magnetic skyrmionic systems. The approach combines a local energy-minimizer backend and a metaheuristic global search frontend. The algorithm is naturally concurrent, resulting in short user execution time. We find that the method performs significantly better than simulated annealing (SA). Specifically, we demonstrate that for the Pd/Fe/Ir(111) system, our method correctly and efficiently identifies the experimentally observed spin spiral, skyrmion lattice and ferromagnetic ground states as a function of external magnetic field. To our knowledge, no other optimization method has until now succeeded to do this. We envision that our findings will pave the way for evolutionary computing in mapping out phase diagrams for spin systems in general. Copyright © 2022, The Authors. All rights reserved.

关键词： Simulated annealing

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Hybrid Oscillator-Qubit Quantum Processors: Simulating Fermions, Bosons, and Gauge Fields

arXiv

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arXiv 2024年

作者： Crane, Eleanor Smith, Kevin C. Tomesh, Teague Eickbusch, Alec Martyn, John M. Kühn, Stefan Funcke, Lena DeMarco, Michael Austin Chuang, Isaac L. Wiebe, Nathan Schuckert, Alexander Girvin, Steven M. Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Joint Quantum Institute Joint Center for Quantum Information and Computer Science University of Maryland NIST College ParkMD20742 United States Brookhaven National Laboratory UptonNY11973 United States Yale Quantum Institute PO Box 208 334 17 Hillhouse Ave New HavenCT06520-8263 United States Departments of Applied Physics and Physics Yale University New HavenCT06511 United States Department of Computer Science Princeton University PrincetonNJ08544 United States Infleqtion ChicagoIL60604 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CQTA Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany University of Bonn Nussallee 14-16 Bonn53115 Germany Department of Computer Science University of Toronto Canada Pacific Northwest National Laboratory RichlandWA United States Canadian Institute for Advanced Studies Toronto Canada

We develop a hybrid oscillator-qubit processor framework for quantum simulation of strongly correlated fermions and bosons that avoids the boson-to-qubit mapping overhead encountered in qubit hardware. This framework gives exact decompositions of particle interactions such as density-density terms and gauge-invariant hopping, as well as approximate methods based on the Baker-Campbell Hausdorff formulas including the magnetic field term for the U(1) quantum link model in (2 + 1)D. We use this framework to show how to simulate dynamics using Trotterisation, perform ancilla-free partial error detection using Gauss's law, measure non-local observables, estimate ground state energies using a oscillator-qubit variational quantum eigensolver as well as quantum signal processing, and we numerically study the influence of hardware errors in circuit QED experiments. To show the advantages over all-qubit hardware, we perform an end-to-end comparison of the gate complexity for the gauge-invariant hopping term and find an improvement of the asymptotic scaling with the boson number cutoff S from O(log(S)2) to O(1) in our framework as well as, for bosonic matter, a constant factor improvement of better than 104. We also find an improvement from O(log(S)) to O(1) for the U(1) magnetic field term. While our work focusses on an implementation in superconducting hardware, our framework can also be used in trapped ion, and neutral atom hardware. This work establishes digital quantum simulation with hybrid oscillator-qubit hardware as a viable and advantageous method for the study of qubit-boson models in materials science, chemistry, and high-energy physics. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

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Correction to: SleepBoost: a multi‑level tree‑based ensemble model for automatic sleep stage classification

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Medical & biological engineering & computing 2024年第9期62卷 2785页

作者： Akib Zaman Shiu Kumar Swakkhar Shatabda Iman Dehzangi Alok Sharma Computer Science and Artificial Intelligence Laboratory (CSAIL) Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge MA USA. School of Electrical & Electronics Engineering Fiji National University Suva Fiji. shiu.kumar@fnu.ac.fj. Centre for Artificial Intelligence and Robotics (CAIR) United International University Dhaka Bangladesh. Department of Computer Science Rutgers University Camden NJ USA. Center for Computational and Integrative Biology Rutgers University Camden USA. Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230‑0045 Japan. Institute for Integrated and Intelligent Systems Griffith University Nathan Brisbane QLD Australia.

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DeceFL: A principled decentralized federated learning framework

arXiv

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arXiv 2021年

作者： Yuan, Ye Liu, Jun Jin, Dou Yue, Zuogong Chen, Ruijuan Wang, Maolin Sun, Chuan Xu, Lei Hua, Feng He, Xin Yi, Xinlei Yang, Tao Zhang, Hai-Tao Sui, Shaochun Ding, Han School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Department of Applied Mathematics University of Waterloo Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern University United States School of Electrical Engineering and Computer Science Digital Futures Kth Royal Institute of Technology Co. Ltd.

Traditional machine learning relies on a centralized data pipeline, i.e., data are provided to a central server for model training. In many applications, however, data are inherently fragmented. Such a decentralized nature of these databases presents the biggest challenge for collaboration: sending all decentralized datasets to a central server raises serious privacy concerns. Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client. Here we propose a principled decentralized federated learning algorithm (DeceFL), which does not require a central client and relies only on local information transmission between clients and their neighbors, representing a fully decentralized learning framework. It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1/T) (where T is the number of iterations in gradient descent) as centralized federated learning when the loss function is smooth and strongly convex. Finally, the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions, demonstrating its applicability to a wide range of real-world medical and industrial applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Error rate reduction of single-qubit gates via noise-aware decomposition into native gates

arXiv

引用

arXiv 2021年

作者： Maldonado, Thomas J. Flick, Johannes Krastanov, Stefan Galda, Alexey Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Physics Harvard University CambridgeMA02138 United States Center for Computational Quantum Physics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Menten AI Inc. San FranciscoCA94111 United States James Franck Institute University of Chicago ChicagoIL60637 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States

In the current era of Noisy Intermediate-Scale Quantum (NISQ) technology, the practical use of quantum computers remains inhibited by our inability to aptly decouple qubits from their environment to mitigate computational errors. In this paper, we introduce an approach by which knowledge of a qubit's initial quantum state and the standard parameters describing its decoherence can be leveraged to mitigate the noise present during the execution of a single-qubit gate. We benchmark our protocol using cloud-based access to IBM quantum processors. On ibmq rome, we demonstrate a reduction of the single-qubit error rate by 38%, from 1.6×10-3to 1.0×10-3, provided the initial state of the input qubit is known. On ibmq bogota, we prove that our protocol will never decrease gate fidelity, provided the system's T1and T2times have not drifted above 100 times their assumed values. The protocol can be used to reduce quantum state preparation errors, as well as to improve the fidelity of quantum circuits for which some knowledge of the qubits' intermediate states can be inferred. This paper presents a pathway to using information about noise levels and quantum state distributions to significantly reduce error rates associated with quantum gates via optimized decomposition into native hardware gates. © 2021, CC BY.

关键词： Qubits

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ABACUS: An Electronic Structure Analysis Package for the AI Era

arXiv

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arXiv 2025年

作者： Zhou, Weiqing Zheng, Daye Liu, Qianrui Lu, Denghui Liu, Yu Lin, Peize Huang, Yike Peng, Xingliang Bao, Jie J. Cai, Chun Jin, Zuxin Wu, Jing Zhang, Haochong Jin, Gan Ji, Yuyang Shen, Zhenxiong Liu, Xiaohui Sun, Liang Cao, Yu Sun, Menglin Liu, Jianchuan Chen, Tao Liu, Renxi Li, Yuanbo Han, Haozhi Liang, Xinyuan Bao, Taoni Chen, Nuo Ren, Hongxu Zhang, Xiaoyang Liu, Zhaoqing Fu, Yiwei Liu, Maochang Li, Zhuoyuan Wen, Tongqi Tang, Zechen Xu, Yong Duan, Wenhui Wang, Xiaoyang Gu, Qiangqiang Dai, Fu-Zhi Zheng, Qijing Zhao, Jin Zhang, Yuzhi Ou, Qi Jiang, Hong Liu, Shi Xu, Ben Xu, Shenzhen Ren, Xinguo He, Lixin Zhang, Linfeng Chen, Mohan AI for Science Institute Beijing100080 China HEDPS CAPT School of Physics College of Engineering Peking University Beijing100871 China College of Engineering Peking University Beijing100871 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Supercomputing Center University of Science and Technology of China Anhui Hefei230026 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Materials Science and Engineering Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China College of Chemistry and Molecular Engineering Peking University Beijing100871 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow Xi’an Jiaotong University Shaanxi Xi’an710049 China Suzhou Academy of Xi’an Jiaotong University Jiangsu Suzhou215123 China Center for Structural Materials Department of Mechanical Engineering The University of Hong Kong Hong Kong The University of Hong Kong Shenzhen China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Beijing China Saitama Wako351-0198 Japan Institute for Advanced Study Tsinghua University Beijing100084 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China Department of Physics University of Science and

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, etc. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Multichannel nonlinear holography in a two-dimensional nonlinear photonic crystal

引用

Physical Review A 2020年第4期102卷 043506-043506页

作者： Xinyuan Fang Huijun Wang Haocheng Yang Zhilin Ye Yongmei Wang Yong Zhang Xiaopeng Hu Shining Zhu Min Xiao National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences School of Physics Nanjing University Nanjing 210093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Department of Physics University of Arkansas Fayetteville Arkansas 72701 USA

To manipulate the wave front of the harmonic wave, nonlinear photonic crystals (NPCs) have been employed in constructing nonlinear holography. Through designing a multi-channel χ2 structure, nonlinear multiplexing holography is reported here in NPCs. We propose a multi-channel nonlinear holography in an orbital-angular-momentum- (OAM-)multiplexing NPC. In experiment, three images encoded in a single NPC are reconstructed in second-harmonic waves distinctively, when the necessary Fourier-domain OAM-matching conditions are met, respectively. Our method not only conceptually extends nonlinear holography, but also provides a multifunctional platform for high-capacity security storage.

关键词： Holography Nonlinear optics Photonic crystals Optical second-harmonic generation

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A practical guide to machine learning interatomic potentials – Status and future

arXiv

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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Radiomics-enhanced prediction of Constant-Murley scores following ultrasound-guided percutaneous irrigation of calcific tendinopathy

European Journal of Radiology Artificial Intelligence

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European Journal of Radiology Artificial intelligence 2025年 2卷 100019-100019页

作者： Matthaios Triantafyllou Evangelia E. Vassalou Alexia Maria Goulianou Theodoros H. Tosounidis Kostas Marias Apostolos H. Karantanas Michail E. Klontzas Artificial Intelligence and Translational Imaging (ATI) Lab Department of Radiology School of Medicine University of Crete Voutes Campus Heraklion Greece Department of Medical Imaging University Hospital of Heraklion Heraklion Crete Greece Department of Orthopaedic Surgery University Hospital Heraklion Heraklion Crete 71500 Greece Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research and Technology (FORTH) Heraklion Crete Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Division of Radiology Department for Clinical Science Intervention and Technology (CLINTEC) Karolinska Institutet Stockholm Sweden

Objective Calcific tendinopathy of the rotator cuff presents challenges in symptom management and functional recovery with image-guided treatment. This study aimed to develop a regression model integrating radiomic and clinical features to predict 1-year Constant-Murley Score (CMS) improvement following ultrasound-guided percutaneous irrigation of calcific tendinopathy (US-PICT). Materials and Methods This retrospective study included 84 patients undergoing US-PICT between 2017 and 2019. Advanced Contrast Limited Adaptive Histogram Equilization (CLAHE) preprocessing was applied to US images for radiomic feature extraction. Manual segmentation of calcifications yielded 849 features, later refined to 6 predictive features through inter-rater ICC analysis, correlation filtering, and Recursive Feature Elimination (RFE). Clinical variables, including age, calcification type, and maximum diameter, were integrated. Model performance was evaluated using R 2 , MAE, RMSE, calibration curves, and SHAP analysis, while decision curve analysis assessed clinical utility. Results The regression model achieved an R 2 of 0.66 (95 % CI: 0.52–0.76) with an RMSE of 8.26. Younger age, larger calcifications, and fluid-like consistency correlated with greater CMS improvement (p < 0.001). SHAP analysis identified calcification type, age, and radiomic features as key predictors. Calibration curves demonstrated reasonable alignment between predictions and observed outcomes, while decision curve analysis highlighted the model’s clinical utility. Conclusion Radiomics combined with clinical features provides robust predictions of 1-year functional improvement after US-PICT. Further validation in larger cohorts is warranted to refine and generalize these findings.

关键词： Radiomics Calcific Tendinopathy/(US-PICT) Machine Learning Artificial intelligence Shoulder

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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