arXiv - PHYS - Computational Physics最新文献_第3页

Learning local and semi-local density functionals from exact exchange-correlation potentials and energies 从精确交换相关电势和能量中学习局部和半局部密度函数

arXiv - PHYS - Computational Physics Pub Date : 2024-09-10 DOI: arxiv-2409.06498

Bikash Kanungo, Jeffrey Hatch, Paul M. Zimmerman, Vikram Gavini

{"title":"Learning local and semi-local density functionals from exact exchange-correlation potentials and energies","authors":"Bikash Kanungo, Jeffrey Hatch, Paul M. Zimmerman, Vikram Gavini","doi":"arxiv-2409.06498","DOIUrl":"https://doi.org/arxiv-2409.06498","url":null,"abstract":"Finding accurate exchange-correlation (XC) functionals remains the defining\u0000challenge in density functional theory (DFT). Despite 40 years of active\u0000development, the desired chemical accuracy is still elusive with existing\u0000functionals. We present a data-driven pathway to learn the XC functionals by\u0000utilizing the exact density, XC energy, and XC potential. While the exact\u0000densities are obtained from accurate configuration interaction (CI), the exact\u0000XC energies and XC potentials are obtained via inverse DFT calculations on the\u0000CI densities. We demonstrate how simple neural network (NN) based local density\u0000approximation (LDA) and generalized gradient approximation (GGA), trained on\u0000just five atoms and two molecules, provide remarkable improvement in total\u0000energies, densities, atomization energies, and barrier heights for hundreds of\u0000molecules outside the training set. Particularly, the NN-based GGA functional\u0000attains similar accuracy as the higher rung SCAN meta-GGA, highlighting the\u0000promise of using the XC potential in modeling XC functionals. We expect this\u0000approach to pave the way for systematic learning of increasingly accurate and\u0000sophisticated XC functionals.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204042","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

An Update to Isomers of Rydberg Excitations in Argon Clusters 氩簇中的雷伯格激发同分异构体的最新进展

arXiv - PHYS - Computational Physics Pub Date : 2024-09-10 DOI: arxiv-2409.06484

Mukul Dhiman, Benoit Gervais

引用次数: 0

FEAST nonlinear eigenvalue algorithm for $GW$ quasiparticle equations 针对 $GW$ 准粒子方程的 FEAST 非线性特征值算法

arXiv - PHYS - Computational Physics Pub Date : 2024-09-10 DOI: arxiv-2409.06119

Dongming Li, Eric Polizzi

引用次数: 0

A Journey with THeSeuSS: Automated Python Tool for Modeling IR and Raman Vibrational Spectra of Molecules and Solids THeSeuSS 之旅：模拟分子和固体红外和拉曼振动光谱的 Python 自动工具

arXiv - PHYS - Computational Physics Pub Date : 2024-09-10 DOI: arxiv-2409.06597

Ariadni Boziki, Frédéric Ngono Mebenga, Philippe Fernandes, Alexandre Tkatchenko

{"title":"A Journey with THeSeuSS: Automated Python Tool for Modeling IR and Raman Vibrational Spectra of Molecules and Solids","authors":"Ariadni Boziki, Frédéric Ngono Mebenga, Philippe Fernandes, Alexandre Tkatchenko","doi":"arxiv-2409.06597","DOIUrl":"https://doi.org/arxiv-2409.06597","url":null,"abstract":"Vibrational spectroscopy is an indispensable analytical tool that provides\u0000structural fingerprints for molecules, solids, and interfaces thereof. This\u0000study introduces THeSeuSS (THz Spectra Simulations Software) - an automated\u0000computational platform that efficiently simulates IR and Raman spectra for both\u0000periodic and non-periodic systems. Utilizing DFT and DFTB, THeSeuSS offers\u0000robust capabilities for detailed vibrational spectra simulations. Our extensive\u0000evaluations and benchmarks demonstrate that THeSeuSS accurately reproduces both\u0000previously calculated and experimental spectra, enabling precise comparisons\u0000and interpretations of vibrational features across various test cases,\u0000including H2O and glycine molecules in the gas phase, as well as solid ammonia\u0000and solid ibuprofen. Designed with a user-friendly interface and seamless\u0000integration with existing computational chemistry tools, THeSeuSS enhances the\u0000accessibility and applicability of advanced spectroscopic simulations,\u0000supporting research and development in chemical, pharmaceutical, and material\u0000sciences. Future updates aim to expand its methodological diversity by\u0000incorporating machine learning techniques to analyze larger and more complex\u0000systems, solidifying THeSeuSS's role as an essential tool in the computational\u0000chemist's arsenal.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204043","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

Configuration Interaction Guided Sampling with Interpretable Restricted Boltzmann Machine 利用可解释的受限玻尔兹曼机进行配置交互引导采样

arXiv - PHYS - Computational Physics Pub Date : 2024-09-10 DOI: arxiv-2409.06146

Jorge I. Hernandez-Martinez, Gerardo Rodriguez-Hernandez, Andres Mendez-Vazquez

引用次数: 0

Simulating real-time molecular electron dynamics efficiently using the time-dependent density matrix renormalization group 利用随时间变化的密度矩阵重正化群高效模拟实时分子电子动力学

arXiv - PHYS - Computational Physics Pub Date : 2024-09-09 DOI: arxiv-2409.05959

Imam S. Wahyutama, Henrik R. Larsson

{"title":"Simulating real-time molecular electron dynamics efficiently using the time-dependent density matrix renormalization group","authors":"Imam S. Wahyutama, Henrik R. Larsson","doi":"arxiv-2409.05959","DOIUrl":"https://doi.org/arxiv-2409.05959","url":null,"abstract":"Compared to ground state electronic structure optimizations, accurate\u0000simulations of molecular real-time electron dynamics are usually much more\u0000difficult to perform. To simulate electron dynamics, the time-dependent density\u0000matrix renormalization group (TDDMRG) has been shown to offer an attractive\u0000compromise between accuracy and cost. However, many simulation parameters\u0000significantly affect the quality and efficiency of a TDDMRG simulation. So far,\u0000it is unclear whether common wisdom from ground state DMRG carries over to the\u0000TDDMRG, and a guideline on how to choose these parameters is missing. Here, in\u0000order to establish such a guideline, we investigate the convergence behavior of\u0000the main TDDMRG simulation parameters, such as time integrator, the choice of\u0000orbitals, and the choice of MPS representation for complex-valued non-singlet\u0000states. In addition, we propose a method to select orbitals that are tailored\u0000to optimize the dynamics. Lastly, we showcase the TDDMRG by applying it to\u0000charge migration ionization dynamics in furfural, where we reveal a rapid\u0000conversion from an ionized state with a $sigma$ character to one with a $pi$\u0000character within less than a femtosecond.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204055","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

Characterization of Crystal Properties and Defects in CdZnTe Radiation Detectors 碲锌镉辐射探测器的晶体特性和缺陷表征

arXiv - PHYS - Computational Physics Pub Date : 2024-09-09 DOI: arxiv-2409.06738

Manuel Ballester, Jaromir Kaspar, Francesc Massanes, Srutarshi Banerjee, Alexander Hans Vija, Aggelos K. Katsaggelos

引用次数: 0

Constructing multicomponent cluster expansions with machine-learning and chemical embedding 利用机器学习和化学嵌入构建多组分簇扩展

arXiv - PHYS - Computational Physics Pub Date : 2024-09-09 DOI: arxiv-2409.06071

Yann L. Müller, Anirudh Raju Natarajan

{"title":"Constructing multicomponent cluster expansions with machine-learning and chemical embedding","authors":"Yann L. Müller, Anirudh Raju Natarajan","doi":"arxiv-2409.06071","DOIUrl":"https://doi.org/arxiv-2409.06071","url":null,"abstract":"Cluster expansions are commonly employed as surrogate models to link the\u0000electronic structure of an alloy to its finite-temperature properties. Using\u0000cluster expansions to model materials with several alloying elements is\u0000challenging due to a rapid increase in the number of fitting parameters and\u0000training set size. We introduce the embedded cluster expansion (eCE) formalism\u0000that enables the parameterization of accurate on-lattice surrogate models for\u0000alloys containing several chemical species. The eCE model simultaneously learns\u0000a low dimensional embedding of site basis functions along with the weights of\u0000an energy model. A prototypical senary alloy comprised of elements in groups 5\u0000and 6 of the periodic table is used to demonstrate that eCE models can\u0000accurately reproduce ordering energetics of complex alloys without a\u0000significant increase in model complexity. Further, eCE models can leverage\u0000similarities between chemical elements to efficiently extrapolate into\u0000compositional spaces that are not explicitly included in the training dataset.\u0000The eCE formalism presented in this study unlocks the possibility of employing\u0000cluster expansion models to study multicomponent alloys containing several\u0000alloying elements.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204054","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

Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials 通过应用材料中的图神经网络研究材料界面扩散现象

arXiv - PHYS - Computational Physics Pub Date : 2024-09-09 DOI: arxiv-2409.05306

Zirui Zhao, Haifeng-Li

{"title":"Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials","authors":"Zirui Zhao, Haifeng-Li","doi":"arxiv-2409.05306","DOIUrl":"https://doi.org/arxiv-2409.05306","url":null,"abstract":"Understanding and predicting interface diffusion phenomena in materials is\u0000crucial for various industrial applications, including semiconductor\u0000manufacturing, battery technology, and catalysis. In this study, we propose a\u0000novel approach utilizing Graph Neural Networks (GNNs) to investigate and model\u0000material interface diffusion. We begin by collecting experimental and simulated\u0000data on diffusion coefficients, concentration gradients, and other relevant\u0000parameters from diverse material systems. The data are preprocessed, and key\u0000features influencing interface diffusion are extracted. Subsequently, we\u0000construct a GNN model tailored to the diffusion problem, with a graph\u0000representation capturing the atomic structure of materials. The model\u0000architecture includes multiple graph convolutional layers for feature\u0000aggregation and update, as well as optional graph attention layers to capture\u0000complex relationships between atoms. We train and validate the GNN model using\u0000the preprocessed data, achieving accurate predictions of diffusion\u0000coefficients, diffusion rates, concentration profiles, and potential diffusion\u0000pathways. Our approach offers insights into the underlying mechanisms of\u0000interface diffusion and provides a valuable tool for optimizing material design\u0000and engineering. Additionally, our method offers possible strategies to solve\u0000the longstanding problems related to materials interface diffusion.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204059","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 design, verification, and applications of Hotspice: a Monte Carlo simulator for artificial spin ice Hotspice：人工自旋冰蒙特卡罗模拟器的设计、验证和应用

arXiv - PHYS - Computational Physics Pub Date : 2024-09-09 DOI: arxiv-2409.05580

Jonathan Maes, Diego De Gusem, Ian Lateur, Jonathan Leliaert, Aleksandr Kurenkov, Bartel Van Waeyenberge

引用次数: 0