arXiv - PHYS - Computational Physics最新文献_第9页

Machine learning supported annealing for prediction of grand canonical crystal structures 机器学习支持退火法预测大规范晶体结构

arXiv - PHYS - Computational Physics Pub Date : 2024-08-07 DOI: arxiv-2408.03556

Yannick Couzinie, Yuya Seki, Yusuke Nishiya, Hirofumi Nishi, Taichi Kosugi, Shu Tanaka, Yu-ichiro Matsushita

引用次数: 0

Classification of synchronization in nonlinear systems using ICO learning 利用 ICO 学习对非线性系统中的同步进行分类

arXiv - PHYS - Computational Physics Pub Date : 2024-08-07 DOI: arxiv-2408.04024

J. P. Deka

{"title":"Classification of synchronization in nonlinear systems using ICO learning","authors":"J. P. Deka","doi":"arxiv-2408.04024","DOIUrl":"https://doi.org/arxiv-2408.04024","url":null,"abstract":"In this work, we investigate the implications of the differential Hebbian\u0000learning rule known as Input-Correlations (ICO) learning in the classification\u0000of synchronization in coupled nonlinear oscillator systems. We are\u0000investigating the parity-time symmetric coupled Duffing oscillator system with\u0000nonlinear dissipation/amplification. In our investigation of the temporal\u0000dynamics of this system, it is observed that the system exhibits chaotic as\u0000well as quasiperiodic dynamics. On further investigation, it is found that the\u0000chaotic dynamics is distorted anti-phase synchronized, whereas the\u0000quasiperiodic dynamics is desynchronized. So, on the application of the ICO\u0000learning in these two parametric regimes, we observe that the weight associated\u0000with the stimulus remains constant when the oscillators are anti-phase\u0000synchronized, in spite of there being distortion in the synchronization. But\u0000when the oscillators exhibit quasiperiodic dynamics, there is erratic evolution\u0000of the weight with time. So, from this, it could be ascertained that the ICO\u0000learning could be made use of in the classification of synchronization dynamics\u0000in nonlinear systems.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"128 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937400","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

Uncertainty of DFT calculated mechanical and structural properties of solids due to incompatibility of pseudopotentials and exchange-correlation functionals 伪势和交换相关函数不兼容导致 DFT 计算的固体力学和结构特性的不确定性

arXiv - PHYS - Computational Physics Pub Date : 2024-08-07 DOI: arxiv-2408.03835

Marcin Maździarz

引用次数: 0

Review of the finite difference Hartree-Fock method for atoms and diatomic molecules, and its implementation in the x2dhf program 原子和双原子分子的有限差分哈特里-福克方法及其在 x2dhf 程序中的实现回顾

arXiv - PHYS - Computational Physics Pub Date : 2024-08-07 DOI: arxiv-2408.03679

Jacek Kobus, Susi Lehtola

{"title":"Review of the finite difference Hartree-Fock method for atoms and diatomic molecules, and its implementation in the x2dhf program","authors":"Jacek Kobus, Susi Lehtola","doi":"arxiv-2408.03679","DOIUrl":"https://doi.org/arxiv-2408.03679","url":null,"abstract":"We present an extensive review of the two-dimensional finite difference\u0000Hartree--Fock (FD HF) method, and present its implementation in the newest\u0000version of X2DHF, the FD HF program for atoms and diatomic molecules. The\u0000program was originally published in Comput. Phys. Commun. in 1996, and was last\u0000revised in 2013. X2DHF can be used to obtain HF limit values of total energies\u0000and multipole moments for a wide range of diatomic molecules and their ions,\u0000using either point nuclei or a finite nuclear model. Polarizabilities\u0000($alpha_{zz}$) and hyperpolarizabilities ($beta_{zzz}$, $gamma_{zzzz}$,\u0000${A}_{z,zz}$, ${B}_{zz,zz}$) can also be computed by the program with the\u0000finite-field method. X2DHF has been extensively used in the literature to\u0000assess the accuracy of existing atomic basis sets and to help in developing new\u0000ones. As a new feature since the last revision, the program can now also\u0000perform Kohn-Sham density functional calculations with local and generalized\u0000gradient exchange-correlation functionals with the Libxc library of density\u0000functionals, enabling new types of studies. Furthermore, the initialization of\u0000calculations has been greatly simplified. As before, X2DHF can also perform\u0000one-particle calculations with (smooth) Coulomb, Green-Sellin-Zachor and\u0000Krammers-Henneberger potentials, while calculations with a superposition of\u0000atomic potentials have been added as a new feature. The program is easy to\u0000install from the GitHub repository and build via CMake using the x2dhfctl\u0000script that facilitates creating its single- and multiple-threaded versions, as\u0000well as building in Libxc support. Calculations can be carried out with X2DHF\u0000in double- or quadruple-precision arithmetic.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937470","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

Structural transitions of calcium carbonate by molecular dynamics simulation 分子动力学模拟碳酸钙的结构转变

arXiv - PHYS - Computational Physics Pub Date : 2024-08-07 DOI: arxiv-2408.04036

Elizaveta Sidler, Raffaela Cabriolu

{"title":"Structural transitions of calcium carbonate by molecular dynamics simulation","authors":"Elizaveta Sidler, Raffaela Cabriolu","doi":"arxiv-2408.04036","DOIUrl":"https://doi.org/arxiv-2408.04036","url":null,"abstract":"Calcium carbonate plays a crucial role in the global carbon cycle, and its\u0000phase diagram has always been of significant scientific interest. In this\u0000study, we used molecular dynamics (MD) to investigate several structural phase\u0000transitions of calcium carbonate. Using the Raiteri potential model, we\u0000explored the structural transitions occurring at a constant pressure of 1 bar\u0000with temperatures ranging from 300 K to 2500 K, and at a constant temperature\u0000of 1600 K with pressures ranging from 0 to 13 GPa. At increasing temperatures,\u0000the transitions calcite, CaCO$_3$-IV, and CaCO$_3$-V are observed and\u0000characterized. Within the calcite structure, CO$_3^{2-}$ ions are ordered\u0000between layers. As temperature increases, the calcite to CaCO$_3$-IV transition\u0000occurs, determining the partial disordering of CO$_3^{2-}$ ions. At a higher\u0000temperature, CaCO$_3$-IV transforms into CaCO$_3$-V. By applying free energy\u0000analysis, we have classified the last transition as a continuous order-disorder\u0000phase transition. At a temperature of 2000 K, it appears a `disordered\u0000CaCO$_3$' structure, characterized by low order within the calcium and\u0000carbonate sublattices and the free rotation of CO$_3^{2-}$ ions. At increasing\u0000pressures, two calcium carbonate transformations were observed. At $P=$ 2 GPa,\u0000the CaCO$_3$-IV phase undergoes a phase transition into CaCO$_3$-V,\u0000demonstrating that the model can describe the transition between these two\u0000phases as pressure and temperature-driven. Another phase transition was\u0000detected at $P=$ 4.25 GPa -- CaCO$_3$-V transits into the recently discovered\u0000CaCO$_3$-Vb phase. This transition is classified as a first-order phase\u0000transition by structural analysis and free energy-based arguments.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937401","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

Phase field simulations of thermal annealing for all-small molecule organic solar cells 全小分子有机太阳能电池热退火的相场模拟

arXiv - PHYS - Computational Physics Pub Date : 2024-08-06 DOI: arxiv-2408.03190

Yasin Ameslon, Olivier J. J. Ronsin, Christina Harreiss, Johannes Will, Stefanie Rechberger Mingjian Wu, Erdmann Spiecker, Jens Harting

{"title":"Phase field simulations of thermal annealing for all-small molecule organic solar cells","authors":"Yasin Ameslon, Olivier J. J. Ronsin, Christina Harreiss, Johannes Will, Stefanie Rechberger Mingjian Wu, Erdmann Spiecker, Jens Harting","doi":"arxiv-2408.03190","DOIUrl":"https://doi.org/arxiv-2408.03190","url":null,"abstract":"Interest in organic solar cells (OSCs) is constantly rising in the field of\u0000photovoltaic devices. The device performance relies on the bulk heterojunction\u0000(BHJ) nanomorphology, which develops during the drying process and additional\u0000post-treatment. This work studies the effect of thermal annealing (TA) on an\u0000all-small molecule DRCN5T: PC71 BM blend with phase field simulations. The\u0000objective is to determine the physical phenomena driving the evolution of the\u0000BHJ morphology for a better understanding of the posttreatment/morphology\u0000relationship. Phase-field simulation results are used to investigate the impact\u0000on the final BHJ morphology of the DRCN5T crystallization-related mechanisms,\u0000including nucleation, growth, crystal stability, impingement, grain coarsening,\u0000and Ostwald ripening, of the amorphous-amorphous phase separation (AAPS), and\u0000of diffusion limitations. The comparison of simulation results with\u0000experimental data shows that the morphological evolution of the BHJ under TA is\u0000dominated by dissolution of the smallest, unstable DRCN5T crystals and\u0000anisotropic growth of the largest crystals.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937478","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

A non-Hermitian quantum mechanics approach for extracting and emulating continuum physics based on bound-state-like calculations 基于类似边界态计算的非赫米提量子力学方法，用于提取和模拟连续体物理学

arXiv - PHYS - Computational Physics Pub Date : 2024-08-06 DOI: arxiv-2408.03309

Xilin Zhang

引用次数: 0

Carlo.jl: A general framework for Monte Carlo simulations in Julia Carlo.jl：在 Julia 中进行蒙特卡罗模拟的通用框架

arXiv - PHYS - Computational Physics Pub Date : 2024-08-06 DOI: arxiv-2408.03386

Lukas Weber

引用次数: 0

A Workflow-Centric Approach to Generating FAIR Data Objects for Mechanical Properties of Materials 以工作流为中心的材料力学性能 FAIR 数据对象生成方法

arXiv - PHYS - Computational Physics Pub Date : 2024-08-06 DOI: arxiv-2408.03965

Ronak Shoghi, Alexander Hartmaier

{"title":"A Workflow-Centric Approach to Generating FAIR Data Objects for Mechanical Properties of Materials","authors":"Ronak Shoghi, Alexander Hartmaier","doi":"arxiv-2408.03965","DOIUrl":"https://doi.org/arxiv-2408.03965","url":null,"abstract":"From a data perspective, the field of materials mechanics is characterized by\u0000a sparsity of available data, mainly due to the strong\u0000microstructure-sensitivity of properties such as strength, fracture toughness,\u0000and fatigue limit. Consequently, individual tests are needed for specimens with\u0000various thermo-mechanical process histories, even if their chemical composition\u0000remains the same. Experimental data on the mechanical behavior of materials is\u0000usually rare, as mechanical testing is typically a destructive method requiring\u0000large amounts of material and effort for specimen preparation and testing.\u0000Furthermore, mechanical behavior is typically characterized in simplified tests\u0000under uniaxial loading conditions, whereas a complete characterization of\u0000mechanical material behavior requires multiaxial testing conditions. To address\u0000this data sparsity, different simulation methods, such as micromechanical\u0000modeling or even atomistic simulations, can contribute to\u0000microstructure-sensitive data collections. These methods cover a wide range of\u0000materials with different microstructures characterized under multiaxial loading\u0000conditions. In the present work, we describe a novel data schema that\u0000integrates metadata and mechanical data itself, following the workflows of the\u0000material modeling processes by which the data has been generated. Each run of\u0000this workflow results in unique data objects due to the incorporation of\u0000various elements such as user, system, and job-specific information in\u0000correlation with the resulting mechanical properties. Hence, this integrated\u0000data format provides a sustainable way of generating data objects that are\u0000Findable, Accessible, Interoperable, and Reusable (FAIR). The choice of\u0000metadata elements has centered on necessary features required to characterize\u0000microstructure-specific data objects, simplifying how purpose-specific datasets\u0000are collected by search algorithms.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937397","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

Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks 利用孪生神经网络构建具有复杂边界的受约束流体的多尺度建模框架

arXiv - PHYS - Computational Physics Pub Date : 2024-08-06 DOI: arxiv-2408.03263

Peiyuan Gao, George Em Karniadakis, Panos Stinis

{"title":"Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks","authors":"Peiyuan Gao, George Em Karniadakis, Panos Stinis","doi":"arxiv-2408.03263","DOIUrl":"https://doi.org/arxiv-2408.03263","url":null,"abstract":"The properties of constrained fluids have increasingly gained relevance for\u0000applications ranging from materials to biology. In this work, we propose a\u0000multiscale model using twin neural networks to investigate the properties of a\u0000fluid constrained between solid surfaces with complex shapes. The atomic scale\u0000model and the mesoscale model are connected by the coarse-grained potential\u0000which is represented by the first neural network. Then we train the second\u0000neural network model as a surrogate to predict the velocity profile of the\u0000constrained fluid with complex boundary conditions at the mesoscale. The effect\u0000of complex boundary conditions on the fluid dynamics properties and the\u0000accuracy of the neural network model prediction are systematically\u0000investigated. We demonstrate that the neural network-enhanced multiscale\u0000framework can connect simulations at atomic scale and mesoscale and reproduce\u0000the properties of a constrained fluid at mesoscale. This work provides insight\u0000into multiscale model development with the aid of machine learning techniques\u0000and the developed model can be used for modern nanotechnology applications such\u0000as enhanced oil recovery and porous materials design.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937476","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