arXiv - PHYS - Computational Physics最新文献_第8页

Gibbs free energies of Fe clusters can be approximated by Tolman correction to accurately model cluster nucleation and growth 铁簇的吉布斯自由能可用托尔曼修正法近似，以准确模拟铁簇的成核和生长过程

arXiv - PHYS - Computational Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16693

Alexander Khrabry, Louis E. S. Hoffenberg, Igor D. Kaganovich, Yuri Barsukov, David B. Graves

{"title":"Gibbs free energies of Fe clusters can be approximated by Tolman correction to accurately model cluster nucleation and growth","authors":"Alexander Khrabry, Louis E. S. Hoffenberg, Igor D. Kaganovich, Yuri Barsukov, David B. Graves","doi":"arxiv-2408.16693","DOIUrl":"https://doi.org/arxiv-2408.16693","url":null,"abstract":"Accurate Gibbs free energies of Fe clusters are required for predictive\u0000modeling of Fe cluster growth during condensation of a cooling vapor. We\u0000present a straightforward method of calculating free energies of cluster\u0000formation using the data provided by molecular dynamics (MD) simulations. We\u0000apply this method to calculate free energies of Fe clusters having from 2 to\u0000100 atoms. The free energies are verified by comparing to an MD-simulated\u0000equilibrium cluster size distribution in a sub-saturated vapor. We show that\u0000these free energies differ significantly from those obtained with a commonly\u0000used spherical cluster approximation - which relies on a surface tension\u0000coefficient of a flat surface. The spherical cluster approximation can be\u0000improved by using a cluster size-dependent Tolman correction for the surface\u0000tension. The values for the Tolman length and effective surface tension were\u0000derived, which differ from the commonly used experimentally measured surface\u0000tension based on the potential energy. This improved approximation does not\u0000account for geometric magic number effects responsible for spikes and troughs\u0000in densities of neighbor cluster sizes. Nonetheless, it allows to model cluster\u0000formation from a cooling vapor and accurately reproduce the condensation\u0000timeline, overall shape of the cluster size distribution, average cluster size,\u0000and the distribution width. Using a constant surface tension coefficient\u0000resulted in distorted condensation dynamics and inaccurate cluster size\u0000distributions. The analytical expression for cluster nucleation rate from\u0000classical nucleation theory (CNT) was updated to account for the\u0000size-dependence of cluster surface tension.","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":"142204176","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":null,"pages":null},"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

The Importance of Learning without Constraints: Reevaluating Benchmarks for Invariant and Equivariant Features of Machine Learning Potentials in Generating Free Energy Landscapes 无约束学习的重要性：重新评估机器学习潜力在生成自由能谱时的不变和等变特征基准

arXiv - PHYS - Computational Physics Pub Date : 2024-08-28 DOI: arxiv-2408.16157

Gustavo R. Pérez-Lemus, Yinan Xu, Yezhi Jin, Pablo F. Zubieta Rico, Juan J. de Pablo

{"title":"The Importance of Learning without Constraints: Reevaluating Benchmarks for Invariant and Equivariant Features of Machine Learning Potentials in Generating Free Energy Landscapes","authors":"Gustavo R. Pérez-Lemus, Yinan Xu, Yezhi Jin, Pablo F. Zubieta Rico, Juan J. de Pablo","doi":"arxiv-2408.16157","DOIUrl":"https://doi.org/arxiv-2408.16157","url":null,"abstract":"Machine-learned interatomic potentials (MILPs) are rapidly gaining interest\u0000for molecular modeling, as they provide a balance between quantum-mechanical\u0000level descriptions of atomic interactions and reasonable computational\u0000efficiency. However, questions remain regarding the stability of simulations\u0000using these potentials, as well as the extent to which the learned potential\u0000energy function can be extrapolated safely. Past studies have reported\u0000challenges encountered when MILPs are applied to classical benchmark systems.\u0000In this work, we show that some of these challenges are related to the\u0000characteristics of the training datasets, particularly the inclusion of rigid\u0000constraints. We demonstrate that long stability in simulations with MILPs can\u0000be achieved by generating unconstrained datasets using unbiased classical\u0000simulations if the fast modes are correctly sampled. Additionally, we emphasize\u0000that in order to achieve precise energy predictions, it is important to resort\u0000to enhanced sampling techniques for dataset generation, and we demonstrate that\u0000safe extrapolation of MILPs depends on judicious choices related to the\u0000system's underlying free energy landscape and the symmetry features embedded\u0000within the machine learning models.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204147","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":null,"pages":null},"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

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":null,"pages":null},"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

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":null,"pages":null},"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

A Space-Time Multigrid Method for Space-Time Finite Element Discretizations of Parabolic and Hyperbolic PDEs 用于抛物线和双曲型 PDE 的时空有限元离散化的时空多网格法

arXiv - PHYS - Computational Physics Pub Date : 2024-08-08 DOI: arxiv-2408.04372

Nils Margenberg, Peter Munch

{"title":"A Space-Time Multigrid Method for Space-Time Finite Element Discretizations of Parabolic and Hyperbolic PDEs","authors":"Nils Margenberg, Peter Munch","doi":"arxiv-2408.04372","DOIUrl":"https://doi.org/arxiv-2408.04372","url":null,"abstract":"We present a space-time multigrid method based on tensor-product space-time\u0000finite element discretizations. The method is facilitated by the matrix-free\u0000capabilities of the {ttfamily deal.II} library. It addresses both high-order\u0000continuous and discontinuous variational time discretizations with spatial\u0000finite element discretizations. The effectiveness of multigrid methods in\u0000large-scale stationary problems is well established. However, their application\u0000in the space-time context poses significant challenges, mainly due to the\u0000construction of suitable smoothers. To address these challenges, we develop a\u0000space-time cell-wise additive Schwarz smoother and demonstrate its\u0000effectiveness on the heat and acoustic wave equations. The matrix-free\u0000framework of the {ttfamily deal.II} library supports various multigrid\u0000strategies, including $h$-, $p$-, and $hp$-refinement across spatial and\u0000temporal dimensions. Extensive empirical evidence, provided through scaling and\u0000convergence tests on high-performance computing platforms, demonstrate high\u0000performance on perturbed meshes and problems with heterogeneous and\u0000discontinuous coefficients. Throughputs of over a billion degrees of freedom\u0000per second are achieved on problems with more than a trillion global degrees of\u0000freedom. The results prove that the space-time multigrid method can effectively\u0000solve complex problems in high-fidelity simulations and show great potential\u0000for use in coupled problems.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937396","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

Accelerating crystal structure search through active learning with neural networks for rapid relaxations 通过神经网络主动学习加速晶体结构搜索，实现快速松弛

arXiv - PHYS - Computational Physics Pub Date : 2024-08-07 DOI: arxiv-2408.04073

Stefaan S. P. Hessmann, Kristof T. Schütt, Niklas W. A. Gebauer, Michael Gastegger, Tamio Oguchi, Tomoki Yamashita

{"title":"Accelerating crystal structure search through active learning with neural networks for rapid relaxations","authors":"Stefaan S. P. Hessmann, Kristof T. Schütt, Niklas W. A. Gebauer, Michael Gastegger, Tamio Oguchi, Tomoki Yamashita","doi":"arxiv-2408.04073","DOIUrl":"https://doi.org/arxiv-2408.04073","url":null,"abstract":"Global optimization of crystal compositions is a significant yet\u0000computationally intensive method to identify stable structures within chemical\u0000space. The specific physical properties linked to a three-dimensional atomic\u0000arrangement make this an essential task in the development of new materials. We\u0000present a method that efficiently uses active learning of neural network force\u0000fields for structure relaxation, minimizing the required number of steps in the\u0000process. This is achieved by neural network force fields equipped with\u0000uncertainty estimation, which iteratively guide a pool of randomly generated\u0000candidates towards their respective local minima. Using this approach, we are\u0000able to effectively identify the most promising candidates for further\u0000evaluation using density functional theory (DFT). Our method not only reliably\u0000reduces computational costs by up to two orders of magnitude across the\u0000benchmark systems Si16 , Na8Cl8 , Ga8As8 and Al4O6 , but also excels in finding\u0000the most stable minimum for the unseen, more complex systems Si46 and Al16O24 .\u0000Moreover, we demonstrate at the example of Si16 that our method can find\u0000multiple relevant local minima while only adding minor computational effort.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937399","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

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