arXiv - PHYS - Computational Physics最新文献_第6页

Reconciling Kubo and Keldysh Approaches to Fermi-Sea-Dependent Nonequilibrium Observables: Application to Spin Hall Current and Spin-Orbit Torque in Spintronics 调和库勃法和凯尔迪什法的费米海洋非平衡观测值：自旋电子学中自旋霍尔电流和自旋轨道转矩的应用

arXiv - PHYS - Computational Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16611

Simao M. Joao, Marko D. Petrovic, J. M. Viana Parente Lopes, Aires Ferreira, Branislav K. Nikolic

{"title":"Reconciling Kubo and Keldysh Approaches to Fermi-Sea-Dependent Nonequilibrium Observables: Application to Spin Hall Current and Spin-Orbit Torque in Spintronics","authors":"Simao M. Joao, Marko D. Petrovic, J. M. Viana Parente Lopes, Aires Ferreira, Branislav K. Nikolic","doi":"arxiv-2408.16611","DOIUrl":"https://doi.org/arxiv-2408.16611","url":null,"abstract":"Quantum transport studies of spin-dependent phenomena in solids commonly\u0000employ the Kubo or Keldysh formulas for the steady-state density matrix in the\u0000linear-response regime. Its trace with operators of interest -- such as, spin\u0000density, spin current density or spin torque -- gives expectation values of\u0000experimentally accessible observables. For such local quantities, these\u0000formulas require summing over the manifolds of {em both} Fermi-surface and\u0000Fermi-sea quantum states. However, debates have been raging in the literature\u0000about vastly different physics the two formulations can apparently produce,\u0000even when applied to the same system. Here, we revisit this problem using a\u0000testbed of infinite-size graphene with proximity-induced spin-orbit and\u0000magnetic exchange effects. By splitting this system into semi-infinite leads\u0000and central active region, in the spirit of Landauer two-terminal setup for\u0000quantum transport, we prove the {em numerically exact equivalence} of the Kubo\u0000and Keldysh approaches via the computation of spin Hall current density and\u0000spin-orbit torque in both clean and disordered limits. The key to reconciling\u0000the two approaches are the numerical frameworks we develop for: ({em i})\u0000evaluation of Kubo(-Bastin) formula for a system attached to semi-infinite\u0000leads, which ensure continuous energy spectrum and evade the need for\u0000phenomenological broadening in prior calculations; and ({em ii}) proper\u0000evaluation of Fermi-sea term in the Keldysh approach, which {em must} include\u0000the voltage drop across the central active region even if it is disorder free.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PyFR v2.0.3: Towards Industrial Adoption of Scale-Resolving Simulations PyFR v2.0.3：实现规模解析模拟的工业应用

arXiv - PHYS - Computational Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16509

Freddie D. Witherden, Peter E. Vincent, Will Trojak, Yoshiaki Abe, Amir Akbarzadeh, Semih Akkurt, Mohammad Alhawwary, Lidia Caros, Tarik Dzanic, Giorgio Giangaspero, Arvind S. Iyer, Antony Jameson, Marius Koch, Niki Loppi, Sambit Mishra, Rishit Modi, Gonzalo Sáez-Mischlich, Jin Seok Park, Brian C. Vermeire, Lai Wang

引用次数: 0

Persistence of the N = 50 shell closure over the isotopic chains of Sc, Ti, V and Cr nuclei using relativistic energy density functional 利用相对论能量密度函数分析 N = 50 壳封闭在 Sc、Ti、V 和 Cr 核同位素链上的持续性

arXiv - PHYS - Computational Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16588

Praveen K. Yadav, Raj Kumar, M. Bhuyan

{"title":"Persistence of the N = 50 shell closure over the isotopic chains of Sc, Ti, V and Cr nuclei using relativistic energy density functional","authors":"Praveen K. Yadav, Raj Kumar, M. Bhuyan","doi":"arxiv-2408.16588","DOIUrl":"https://doi.org/arxiv-2408.16588","url":null,"abstract":"The analytical expression of the density-dependent binding energy per nucleon\u0000for the relativistic mean field (RMF), also known as the relativistic energy\u0000density functional (Relativistic-EDF), is used to obtain the isospin-dependent\u0000symmetry energy and its components for the isotopic chain of Sc, Ti, V, and Cr\u0000nuclei. The procedure of the coherent density fluctuation model is employed to\u0000formulate the Relativistic-EDF and Brueckner energy density functional\u0000(Brueckner-EDF) at local density. A few signatures of shell and/or sub-shell\u0000closure are observed in the symmetry energy and its components, i.e., surface\u0000and volume symmetry energy, far from the beta-stable region for odd-A Sc and V,\u0000and even-even Ti and Cr nuclei with non-linear NL3 and G3 parameter sets. A\u0000comparison is made with the results obtained from Relativistic-EDF and\u0000Brueckner-EDF with both NL3 and G3 for the considered isotopic chains. We find\u0000Relativistic-EDF outperforms the Brueckner-EDF in predicting the shell and/or\u0000sub-shell closure of neutron-rich isotopes at N = 50 for these atomic nuclei.\u0000Moreover, a relative comparison has been made for the results obtained with the\u0000non-linear NL3 and G3 parameter sets.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SOLAX: A Python solver for fermionic quantum systems with neural network support SOLAX：神经网络支持的费米子量子系统 Python 求解器

arXiv - PHYS - Computational Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16915

Louis Thirion, Philipp Hansmann, Pavlo Bilous

{"title":"SOLAX: A Python solver for fermionic quantum systems with neural network support","authors":"Louis Thirion, Philipp Hansmann, Pavlo Bilous","doi":"arxiv-2408.16915","DOIUrl":"https://doi.org/arxiv-2408.16915","url":null,"abstract":"Numerical modeling of fermionic many-body quantum systems presents similar\u0000challenges across various research domains, necessitating universal tools,\u0000including state-of-the-art machine learning techniques. Here, we introduce\u0000SOLAX, a Python library designed to compute and analyze fermionic quantum\u0000systems using the formalism of second quantization. SOLAX provides a modular\u0000framework for constructing and manipulating basis sets, quantum states, and\u0000operators, facilitating the simulation of electronic structures and determining\u0000many-body quantum states in finite-size Hilbert spaces. The library integrates\u0000machine learning capabilities to mitigate the exponential growth of Hilbert\u0000space dimensions in large quantum clusters. The core low-level functionalities\u0000are implemented using the recently developed Python library JAX. Demonstrated\u0000through its application to the Single Impurity Anderson Model, SOLAX offers a\u0000flexible and powerful tool for researchers addressing the challenges of\u0000many-body quantum systems across a broad spectrum of fields, including atomic\u0000physics, quantum chemistry, and condensed matter physics.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification SympGNNs：用于识别高维哈密顿系统和节点分类的交映图神经网络

arXiv - PHYS - Computational Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16698

Alan John Varghese, Zhen Zhang, George Em Karniadakis

{"title":"SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification","authors":"Alan John Varghese, Zhen Zhang, George Em Karniadakis","doi":"arxiv-2408.16698","DOIUrl":"https://doi.org/arxiv-2408.16698","url":null,"abstract":"Existing neural network models to learn Hamiltonian systems, such as\u0000SympNets, although accurate in low-dimensions, struggle to learn the correct\u0000dynamics for high-dimensional many-body systems. Herein, we introduce\u0000Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system\u0000identification in high-dimensional Hamiltonian systems, as well as node\u0000classification. SympGNNs combines symplectic maps with permutation\u0000equivariance, a property of graph neural networks. Specifically, we propose two\u0000variants of SympGNNs: i) G-SympGNN and ii) LA-SympGNN, arising from different\u0000parameterizations of the kinetic and potential energy. We demonstrate the\u0000capabilities of SympGNN on two physical examples: a 40-particle coupled\u0000Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a\u0000two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the\u0000performance of SympGNN in the node classification task, achieving accuracy\u0000comparable to the state-of-the-art. We also empirically show that SympGNN can\u0000overcome the oversmoothing and heterophily problems, two key challenges in the\u0000field of graph neural networks.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep potential for interaction between hydrated Cs+ and graphene 水合 Cs+ 与石墨烯之间相互作用的深层潜力

arXiv - PHYS - Computational Physics Pub Date : 2024-08-28 DOI: arxiv-2408.15797

Yangjun Qin, Xiao Wan, Liuhua Mu, Zhicheng Zong, Tianhao Li, Nuo Yang

{"title":"Deep potential for interaction between hydrated Cs+ and graphene","authors":"Yangjun Qin, Xiao Wan, Liuhua Mu, Zhicheng Zong, Tianhao Li, Nuo Yang","doi":"arxiv-2408.15797","DOIUrl":"https://doi.org/arxiv-2408.15797","url":null,"abstract":"The influence of hydrated cation-{pi} interaction forces on the adsorption\u0000and filtration capabilities of graphene-based membrane materials is\u0000significant. However, the lack of interaction potential between hydrated Cs+\u0000and graphene limits the scope of adsorption studies. Here, it is developed that\u0000a deep neural network potential function model to predict the interaction force\u0000between hydrated Cs+ and graphene. The deep potential has DFT-level accuracy,\u0000enabling accurate property prediction. This deep potential is employed to\u0000investigate the properties of the graphene surface solution, including the\u0000density distribution, mean square displacement, and vibrational power spectrum\u0000of water. Furthermore, calculations of the molecular orbital electron\u0000distributions indicate the presence of electron migration in the molecular\u0000orbitals of graphene and hydrated Cs+, resulting in a strong electrostatic\u0000interaction force. The method provides a powerful tool to study the adsorption\u0000behavior of hydrated cations on graphene surfaces and offers a new solution for\u0000handling radionuclides.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts 为基于深度学习的核磁共振化学位移预测建立统一基准和框架

arXiv - PHYS - Computational Physics Pub Date : 2024-08-28 DOI: arxiv-2408.15681

Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang, Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng

{"title":"Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts","authors":"Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang, Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng","doi":"arxiv-2408.15681","DOIUrl":"https://doi.org/arxiv-2408.15681","url":null,"abstract":"The study of structure-spectrum relationships is essential for spectral\u0000interpretation, impacting structural elucidation and material design.\u0000Predicting spectra from molecular structures is challenging due to their\u0000complex relationships. Herein, we introduce NMRNet, a deep learning framework\u0000using the SE(3) Transformer for atomic environment modeling, following a\u0000pre-training and fine-tuning paradigm. To support the evaluation of NMR\u0000chemical shift prediction models, we have established a comprehensive benchmark\u0000based on previous research and databases, covering diverse chemical systems.\u0000Applying NMRNet to these benchmark datasets, we achieve state-of-the-art\u0000performance in both liquid-state and solid-state NMR datasets, demonstrating\u0000its robustness and practical utility in real-world scenarios. This marks the\u0000first integration of solid and liquid state NMR within a unified model\u0000architecture, highlighting the need for domainspecific handling of different\u0000atomic environments. Our work sets a new standard for NMR prediction, advancing\u0000deep learning applications in analytical and structural chemistry.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics chemtrain：通过自动微分和统计物理学学习深度电位模型

arXiv - PHYS - Computational Physics Pub Date : 2024-08-28 DOI: arxiv-2408.15852

Paul Fuchs, Stephan Thaler, Sebastien Röcken, Julija Zavadlav

{"title":"chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics","authors":"Paul Fuchs, Stephan Thaler, Sebastien Röcken, Julija Zavadlav","doi":"arxiv-2408.15852","DOIUrl":"https://doi.org/arxiv-2408.15852","url":null,"abstract":"Neural Networks (NNs) are promising models for refining the accuracy of\u0000molecular dynamics, potentially opening up new fields of application. Typically\u0000trained bottom-up, atomistic NN potential models can reach first-principle\u0000accuracy, while coarse-grained implicit solvent NN potentials surpass classical\u0000continuum solvent models. However, overcoming the limitations of costly\u0000generation of accurate reference data and data inefficiency of common bottom-up\u0000training demands efficient incorporation of data from many sources. This paper\u0000introduces the framework chemtrain to learn sophisticated NN potential models\u0000through customizable training routines and advanced training algorithms. These\u0000routines can combine multiple top-down and bottom-up algorithms, e.g., to\u0000incorporate both experimental and simulation data or pre-train potentials with\u0000less costly algorithms. chemtrain provides an object-oriented high-level\u0000interface to simplify the creation of custom routines. On the lower level,\u0000chemtrain relies on JAX to compute gradients and scale the computations to use\u0000available resources. We demonstrate the simplicity and importance of combining\u0000multiple algorithms in the examples of parametrizing an all-atomistic model of\u0000titanium and a coarse-grained implicit solvent model of alanine dipeptide.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SPACIER: On-Demand Polymer Design with Fully Automated All-Atom Classical Molecular Dynamics Integrated into Machine Learning Pipelines SPACIER：将全自动全原子经典分子动力学集成到机器学习管道中的按需聚合物设计

arXiv - PHYS - Computational Physics Pub Date : 2024-08-09 DOI: arxiv-2408.05135

Shun Nanjo, Arifin, Hayato Maeda, Yoshihiro Hayashi, Kan Hatakeyama-Sato, Ryoji Himeno, Teruaki Hayakawa, Ryo Yoshida

{"title":"SPACIER: On-Demand Polymer Design with Fully Automated All-Atom Classical Molecular Dynamics Integrated into Machine Learning Pipelines","authors":"Shun Nanjo, Arifin, Hayato Maeda, Yoshihiro Hayashi, Kan Hatakeyama-Sato, Ryoji Himeno, Teruaki Hayakawa, Ryo Yoshida","doi":"arxiv-2408.05135","DOIUrl":"https://doi.org/arxiv-2408.05135","url":null,"abstract":"Machine learning has rapidly advanced the design and discovery of new\u0000materials with targeted applications in various systems. First-principles\u0000calculations and other computer experiments have been integrated into material\u0000design pipelines to address the lack of experimental data and the limitations\u0000of interpolative machine learning predictors. However, the enormous\u0000computational costs and technical challenges of automating computer experiments\u0000for polymeric materials have limited the availability of open-source automated\u0000polymer design systems that integrate molecular simulations and machine\u0000learning. We developed SPACIER, an open-source software program that integrates\u0000RadonPy, a Python library for fully automated polymer property calculations\u0000based on all-atom classical molecular dynamics into a Bayesian\u0000optimization-based polymer design system to overcome these challenges. As a\u0000proof-of-concept study, we successfully synthesized optical polymers that\u0000surpass the Pareto boundary formed by the tradeoff between the refractive index\u0000and Abbe number.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SchrödingerNet: A Universal Neural Network Solver for The Schrödinger Equation 薛定谔网络薛定谔方程的通用神经网络求解器

arXiv - PHYS - Computational Physics Pub Date : 2024-08-08 DOI: arxiv-2408.04497

Yaolong Zhang, Bin Jiang, Hua Guo

引用次数: 0