The melting point of a material constitutes a pivotal property with profound implications across various disciplines of science, engineering, and technology. Recent advancements in machine learning potentials have revolutionized the field, enabling ab initio predictions of materials' melting points through atomic-scale simulations. However, a universal simulation methodology that can be universally applied to any material remains elusive. In this paper, we present a generic, fully automated workflow designed to predict the melting points of materials utilizing molecular dynamics simulations. This workflow incorporates two tailored simulation modalities, each addressing scenarios with and without elemental partitioning between solid and liquid phases. When the compositions of both phases remain unchanged upon melting or solidification, signifying the absence of partitioning, the melting point is identified as the temperature at which these phases coexist in equilibrium. Conversely, in cases where elemental partitioning occurs, our workflow estimates both the nominal melting point, marking the initial transition from solid to liquid, and the nominal solidification point, indicating the reverse process. To ensure precision in determining these critical temperatures, we employ an innovative temperature-volume data fitting technique, suitable for a diverse range of materials exhibiting notable volume disparities between their solid and liquid states. This comprehensive approach offers a robust and versatile solution for predicting melting points, fostering advancements in materials science and technology.
{"title":"A Generic and Automated Methodology to Simulate Melting Point","authors":"Fu-Zhi Dai, Si-Hao Yuan, Yan-Bo Hao, Xin-Fu Gu, Shipeng Zhu, Jidong Hu, Yifen Xu","doi":"arxiv-2408.17270","DOIUrl":"https://doi.org/arxiv-2408.17270","url":null,"abstract":"The melting point of a material constitutes a pivotal property with profound\u0000implications across various disciplines of science, engineering, and\u0000technology. Recent advancements in machine learning potentials have\u0000revolutionized the field, enabling ab initio predictions of materials' melting\u0000points through atomic-scale simulations. However, a universal simulation\u0000methodology that can be universally applied to any material remains elusive. In\u0000this paper, we present a generic, fully automated workflow designed to predict\u0000the melting points of materials utilizing molecular dynamics simulations. This\u0000workflow incorporates two tailored simulation modalities, each addressing\u0000scenarios with and without elemental partitioning between solid and liquid\u0000phases. When the compositions of both phases remain unchanged upon melting or\u0000solidification, signifying the absence of partitioning, the melting point is\u0000identified as the temperature at which these phases coexist in equilibrium.\u0000Conversely, in cases where elemental partitioning occurs, our workflow\u0000estimates both the nominal melting point, marking the initial transition from\u0000solid to liquid, and the nominal solidification point, indicating the reverse\u0000process. To ensure precision in determining these critical temperatures, we\u0000employ an innovative temperature-volume data fitting technique, suitable for a\u0000diverse range of materials exhibiting notable volume disparities between their\u0000solid and liquid states. This comprehensive approach offers a robust and\u0000versatile solution for predicting melting points, fostering advancements in\u0000materials science and technology.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204143","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}
We provide code to solve the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution equations for the nucleon transversity parton distribution functions (PDFs), which encode nucleon transverse spin structure. Though codes are widely available for the evolution of unpolarized and polarized PDFs, there are no recent codes publicly available for the transversity PDF. Here, we present Python code which implements two methods of solving the leading order (LO) and next-to-leading order (NLO) approximations of the DGLAP equations for the transversity PDF, and we highlight the theoretical differences between the two.
我们提供了求解核子横向部分子分布函数（PDF）的多克什策-格里波夫-利帕托夫-阿尔塔雷利-帕里斯（DGLAP）演化方程的代码，PDF编码核子横向自旋结构。虽然非极化和极化 PDF 的演化代码已广泛存在，但最近还没有公开的横向 PDF 代码。在这里，我们将介绍 Python 代码，它实现了两种方法来求解横向 PDF 的 DGLAP 方程的前导阶（LO）和次前导阶（NLO）近似，并强调了这两种方法的理论差异。
{"title":"tParton: an updated implementation of next-to-leading order transversity evolution","authors":"Congzhou M Sha, Bailing Ma","doi":"arxiv-2409.00221","DOIUrl":"https://doi.org/arxiv-2409.00221","url":null,"abstract":"We provide code to solve the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi\u0000(DGLAP) evolution equations for the nucleon transversity parton distribution\u0000functions (PDFs), which encode nucleon transverse spin structure. Though codes\u0000are widely available for the evolution of unpolarized and polarized PDFs, there\u0000are no recent codes publicly available for the transversity PDF. Here, we\u0000present Python code which implements two methods of solving the leading order\u0000(LO) and next-to-leading order (NLO) approximations of the DGLAP equations for\u0000the transversity PDF, and we highlight the theoretical differences between the\u0000two.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204139","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}
Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In this study, the prediction results of three trained ResNet networks are presented, by investigating charged particle multiplicities at event-by-event level. The widely used Lund string fragmentation model is applied as a training-baseline at $sqrt{s}= 7$ TeV proton-proton collisions. We found that neural-networks with $ gtrsimmathcal{O}(10^3)$ parameters can predict the event-by-event charged hadron multiplicity values up to $ N_mathrm{ch}lesssim 90 $.
{"title":"Estimating event-by-event multiplicity by a Machine Learning Method for Hadronization Studies","authors":"Gábor Bíró, Gábor Papp, Gergely Gábor Barnaföldi","doi":"arxiv-2408.17130","DOIUrl":"https://doi.org/arxiv-2408.17130","url":null,"abstract":"Hadronization is a non-perturbative process, which theoretical description\u0000can not be deduced from first principles. Modeling hadron formation requires\u0000several assumptions and various phenomenological approaches. Utilizing\u0000state-of-the-art Deep Learning algorithms, it is eventually possible to train\u0000neural networks to learn non-linear and non-perturbative features of the\u0000physical processes. In this study, the prediction results of three trained\u0000ResNet networks are presented, by investigating charged particle multiplicities\u0000at event-by-event level. The widely used Lund string fragmentation model is\u0000applied as a training-baseline at $sqrt{s}= 7$ TeV proton-proton collisions.\u0000We found that neural-networks with $ gtrsimmathcal{O}(10^3)$ parameters can\u0000predict the event-by-event charged hadron multiplicity values up to $\u0000N_mathrm{ch}lesssim 90 $.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204057","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}
Quentin Gueuning, Eloy de Lera Acedo, Anthony Keith Brown, Christophe Craeye, Oscar O'Hara
We present a numerical method for the analysis of mutual coupling effects in large, dense and irregular arrays with identical antennas. Building on the Method of Moments (MoM), our technique employs a Macro Basis Function (MBF) approach for rapid direct inversion of the MoM impedance matrix. To expedite the reduced matrix filling, we propose an extension of the Steepest-Descent Multipole expansion which remains numerically stable and efficient across a wide bandwidth. This broadband multipole-based approach is well suited to quasi-planar problems and requires only the pre-computation of each MBF's complex patterns, resulting in low antenna-dependent pre-processing costs. The method also supports arrays with arbitrarily rotated antennas at low additional cost. A simulation of all embedded element patterns of irregular arrays of 256 complex log-periodic antennas completes in just 10 minutes per frequency point on a current laptop, with an additional minute per new layout.
{"title":"A Broadband Multipole Method for Accelerated Mutual Coupling Analysis of Large Irregular Arrays Including Rotated Antennas","authors":"Quentin Gueuning, Eloy de Lera Acedo, Anthony Keith Brown, Christophe Craeye, Oscar O'Hara","doi":"arxiv-2409.00153","DOIUrl":"https://doi.org/arxiv-2409.00153","url":null,"abstract":"We present a numerical method for the analysis of mutual coupling effects in\u0000large, dense and irregular arrays with identical antennas. Building on the\u0000Method of Moments (MoM), our technique employs a Macro Basis Function (MBF)\u0000approach for rapid direct inversion of the MoM impedance matrix. To expedite\u0000the reduced matrix filling, we propose an extension of the Steepest-Descent\u0000Multipole expansion which remains numerically stable and efficient across a\u0000wide bandwidth. This broadband multipole-based approach is well suited to\u0000quasi-planar problems and requires only the pre-computation of each MBF's\u0000complex patterns, resulting in low antenna-dependent pre-processing costs. The\u0000method also supports arrays with arbitrarily rotated antennas at low additional\u0000cost. A simulation of all embedded element patterns of irregular arrays of 256\u0000complex log-periodic antennas completes in just 10 minutes per frequency point\u0000on a current laptop, with an additional minute per new layout.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204141","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}
Simao M. Joao, Marko D. Petrovic, J. M. Viana Parente Lopes, Aires Ferreira, Branislav K. Nikolic
Quantum transport studies of spin-dependent phenomena in solids commonly employ the Kubo or Keldysh formulas for the steady-state density matrix in the linear-response regime. Its trace with operators of interest -- such as, spin density, spin current density or spin torque -- gives expectation values of experimentally accessible observables. For such local quantities, these formulas require summing over the manifolds of {em both} Fermi-surface and Fermi-sea quantum states. However, debates have been raging in the literature about vastly different physics the two formulations can apparently produce, even when applied to the same system. Here, we revisit this problem using a testbed of infinite-size graphene with proximity-induced spin-orbit and magnetic exchange effects. By splitting this system into semi-infinite leads and central active region, in the spirit of Landauer two-terminal setup for quantum transport, we prove the {em numerically exact equivalence} of the Kubo and Keldysh approaches via the computation of spin Hall current density and spin-orbit torque in both clean and disordered limits. The key to reconciling the two approaches are the numerical frameworks we develop for: ({em i}) evaluation of Kubo(-Bastin) formula for a system attached to semi-infinite leads, which ensure continuous energy spectrum and evade the need for phenomenological broadening in prior calculations; and ({em ii}) proper evaluation of Fermi-sea term in the Keldysh approach, which {em must} include the voltage drop across the central active region even if it is disorder free.
{"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":null,"pages":null},"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}
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
PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption of the capability. This paper provides details of those enhancements as released in PyFR v2.0.3, explains efforts to grow an engaged developer and user community, and provides latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.
{"title":"PyFR v2.0.3: Towards Industrial Adoption of Scale-Resolving Simulations","authors":"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","doi":"arxiv-2408.16509","DOIUrl":"https://doi.org/arxiv-2408.16509","url":null,"abstract":"PyFR is an open-source cross-platform computational fluid dynamics framework\u0000based on the high-order Flux Reconstruction approach, specifically designed for\u0000undertaking high-accuracy scale-resolving simulations in the vicinity of\u0000complex engineering geometries. Since the initial release of PyFR v0.1.0 in\u00002013, a range of new capabilities have been added to the framework, with a view\u0000to enabling industrial adoption of the capability. This paper provides details\u0000of those enhancements as released in PyFR v2.0.3, explains efforts to grow an\u0000engaged developer and user community, and provides latest performance and\u0000scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at\u0000ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204175","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}
The analytical expression of the density-dependent binding energy per nucleon for the relativistic mean field (RMF), also known as the relativistic energy density functional (Relativistic-EDF), is used to obtain the isospin-dependent symmetry energy and its components for the isotopic chain of Sc, Ti, V, and Cr nuclei. The procedure of the coherent density fluctuation model is employed to formulate the Relativistic-EDF and Brueckner energy density functional (Brueckner-EDF) at local density. A few signatures of shell and/or sub-shell closure are observed in the symmetry energy and its components, i.e., surface and volume symmetry energy, far from the beta-stable region for odd-A Sc and V, and even-even Ti and Cr nuclei with non-linear NL3 and G3 parameter sets. A comparison is made with the results obtained from Relativistic-EDF and Brueckner-EDF with both NL3 and G3 for the considered isotopic chains. We find Relativistic-EDF outperforms the Brueckner-EDF in predicting the shell and/or sub-shell closure of neutron-rich isotopes at N = 50 for these atomic nuclei. Moreover, a relative comparison has been made for the results obtained with the non-linear NL3 and G3 parameter sets.
利用相对论平均场（RMF）（也称为相对论能量密度函数（Relativistic-EDF））中与密度相关的每个核子结合能的解析表达式，获得了Sc、Ti、V和Cr核同位素链的等空间素相关不对称能及其分量。利用相干密度波动模型的程序来计算局部密度下的相对论-EDF 和布鲁克纳能量密度函数（Brueckner-EDF）。对于具有非线性 NL3 和 G3 参数集的奇-A Sc 核和 V 核，以及偶-偶 Ti 核和 Cr 核，在对称能及其分量（即表面对称能和体积对称能）中观察到一些远离β稳定区的壳和/或亚壳封闭特征。对于所考虑的同位素链，我们对相对论-EDF 和布鲁克纳-EDF（同时具有 NL3 和 G3 参数集）得出的结果进行了比较。我们发现相对论-EDF 在预测 N = 50 时这些原子核的富中子同位素的壳和/或亚壳闭合方面优于 Brueckner-EDF。
{"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":null,"pages":null},"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}
Ifeanyi J. Onuorah, Miki Bonacci, Muhammad M. Isah, Marcello Mazzani, Roberto De Renzi, Giovanni Pizzi, Pietro Bonfa`
Positive muon spin rotation and relaxation spectroscopy is a well established experimental technique for studying materials. It provides a local probe that generally complements scattering techniques in the study of magnetic systems and represents a valuable alternative for materials that display strong incoherent scattering or neutron absorption. Computational methods can effectively quantify the microscopic interactions underlying the experimentally observed signal, thus substantially boosting the predictive power of this technique. Here, we present an efficient set of algorithms and workflows devoted to the automation of this task. In particular, we adopt the so-called DFT+{mu} procedure, where the system is characterised in the density functional theory (DFT) framework with the muon modeled as a hydrogen impurity. We devise an automated strategy to obtain candidate muon stopping sites, their dipolar interaction with the nuclei, and hyperfine interactions with the electronic ground state. We validate the implementation on well-studied compounds, showing the effectiveness of our protocol in terms of accuracy and simplicity of use
{"title":"Automated computational workflows for muon spin spectroscopy","authors":"Ifeanyi J. Onuorah, Miki Bonacci, Muhammad M. Isah, Marcello Mazzani, Roberto De Renzi, Giovanni Pizzi, Pietro Bonfa`","doi":"arxiv-2408.16722","DOIUrl":"https://doi.org/arxiv-2408.16722","url":null,"abstract":"Positive muon spin rotation and relaxation spectroscopy is a well established\u0000experimental technique for studying materials. It provides a local probe that\u0000generally complements scattering techniques in the study of magnetic systems\u0000and represents a valuable alternative for materials that display strong\u0000incoherent scattering or neutron absorption. Computational methods can\u0000effectively quantify the microscopic interactions underlying the experimentally\u0000observed signal, thus substantially boosting the predictive power of this\u0000technique. Here, we present an efficient set of algorithms and workflows\u0000devoted to the automation of this task. In particular, we adopt the so-called\u0000DFT+{mu} procedure, where the system is characterised in the density\u0000functional theory (DFT) framework with the muon modeled as a hydrogen impurity.\u0000We devise an automated strategy to obtain candidate muon stopping sites, their\u0000dipolar interaction with the nuclei, and hyperfine interactions with the\u0000electronic ground state. We validate the implementation on well-studied\u0000compounds, showing the effectiveness of our protocol in terms of accuracy and\u0000simplicity of use","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204158","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}
Numerical modeling of fermionic many-body quantum systems presents similar challenges across various research domains, necessitating universal tools, including state-of-the-art machine learning techniques. Here, we introduce SOLAX, a Python library designed to compute and analyze fermionic quantum systems using the formalism of second quantization. SOLAX provides a modular framework for constructing and manipulating basis sets, quantum states, and operators, facilitating the simulation of electronic structures and determining many-body quantum states in finite-size Hilbert spaces. The library integrates machine learning capabilities to mitigate the exponential growth of Hilbert space dimensions in large quantum clusters. The core low-level functionalities are implemented using the recently developed Python library JAX. Demonstrated through its application to the Single Impurity Anderson Model, SOLAX offers a flexible and powerful tool for researchers addressing the challenges of many-body quantum systems across a broad spectrum of fields, including atomic physics, quantum chemistry, and condensed matter physics.
{"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":null,"pages":null},"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}
Alan John Varghese, Zhen Zhang, George Em Karniadakis
Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combines symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: i) G-SympGNN and ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. We also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.
{"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":null,"pages":null},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：