GPR_calculator:一个动态代理模型,用于加速大规模的轻推弹性带计算

IF 3.4 2区 物理与天体物理 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Isaac Onyango , Byungkyun Kang , Qiang Zhu
{"title":"GPR_calculator:一个动态代理模型,用于加速大规模的轻推弹性带计算","authors":"Isaac Onyango ,&nbsp;Byungkyun Kang ,&nbsp;Qiang Zhu","doi":"10.1016/j.cpc.2025.109781","DOIUrl":null,"url":null,"abstract":"<div><div>We present <span>GPR_calculator</span>, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of <span>GPR_calculator</span>, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at <span><span>https://github.com/MaterSim/GPR_calculator</span><svg><path></path></svg></span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> GPR_calculator</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/vyhpdf9fkh.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT [1]</div><div><em>Programming language::</em> Python 3 &amp; C++</div><div><em>Nature of problem:</em> Many atomistic simulations—such as geometry optimization, barrier calculations, molecular dynamics, and equation-of-state simulations—require sampling a large number of atomic configurations in a compact phase space. While Density Functional Theory (DFT) provides good accuracy and relatively scalable performance for systems with fewer than hundreds of atoms, it can become prohibitively expensive for massive simulations. This is particularly evident in energy barrier calculations for surface diffusion or reaction studies, where hundreds or thousands of energy and force evaluations are needed.</div><div><em>Solution method:</em> The <span>GPR_calculator</span> is an On-the-Fly Atomistic Calculator based on Gaussian Process Regression (GPR), designed as an add-on module that can be used with the popular Atomic Simulation Environment (ASE). It is essentially a hybrid approach that consists of: (i) a base calculator to provide ground truth reference energy and forces for the given input structure, and (ii) a surrogate model serving as the less expensive approximation trained on-the-fly. When the uncertainty of the GPR prediction exceeds a user-defined threshold, the base calculator is invoked to obtain accurate results and update the GPR model. This adaptive approach ensures accuracy while significantly reducing computational cost.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div><span><span>https://opensource.org/licenses/MIT</span><svg><path></path></svg></span></div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109781"},"PeriodicalIF":3.4000,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations\",\"authors\":\"Isaac Onyango ,&nbsp;Byungkyun Kang ,&nbsp;Qiang Zhu\",\"doi\":\"10.1016/j.cpc.2025.109781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We present <span>GPR_calculator</span>, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of <span>GPR_calculator</span>, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at <span><span>https://github.com/MaterSim/GPR_calculator</span><svg><path></path></svg></span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> GPR_calculator</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/vyhpdf9fkh.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT [1]</div><div><em>Programming language::</em> Python 3 &amp; C++</div><div><em>Nature of problem:</em> Many atomistic simulations—such as geometry optimization, barrier calculations, molecular dynamics, and equation-of-state simulations—require sampling a large number of atomic configurations in a compact phase space. While Density Functional Theory (DFT) provides good accuracy and relatively scalable performance for systems with fewer than hundreds of atoms, it can become prohibitively expensive for massive simulations. This is particularly evident in energy barrier calculations for surface diffusion or reaction studies, where hundreds or thousands of energy and force evaluations are needed.</div><div><em>Solution method:</em> The <span>GPR_calculator</span> is an On-the-Fly Atomistic Calculator based on Gaussian Process Regression (GPR), designed as an add-on module that can be used with the popular Atomic Simulation Environment (ASE). It is essentially a hybrid approach that consists of: (i) a base calculator to provide ground truth reference energy and forces for the given input structure, and (ii) a surrogate model serving as the less expensive approximation trained on-the-fly. When the uncertainty of the GPR prediction exceeds a user-defined threshold, the base calculator is invoked to obtain accurate results and update the GPR model. This adaptive approach ensures accuracy while significantly reducing computational cost.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div><span><span>https://opensource.org/licenses/MIT</span><svg><path></path></svg></span></div></span></li></ul></div></div>\",\"PeriodicalId\":285,\"journal\":{\"name\":\"Computer Physics Communications\",\"volume\":\"316 \",\"pages\":\"Article 109781\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Physics Communications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010465525002838\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Physics Communications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010465525002838","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

我们提出了GPR_calculator,一个基于Python和c++编程语言的包,使用高斯过程回归(GPR)构建一个动态代理模型来近似计算昂贵的电子结构计算。其关键思想是在仿真过程中动态训练一个探地雷达模型,该模型可以准确地预测能量和力的不确定性量化。当不确定度较大时,采用昂贵的电子结构计算来获取地真值数据,并将其用于更新探地雷达模型。为了说明GPR_calculator的有效性,我们演示了它在微推弹性带(Nudged Elastic Band, NEB)表面扩散和反应模拟中的应用,与纯从头计算相比,实现了3-10倍的加速度。源代码可在https://github.com/MaterSim/GPR_calculator.Program summaryProgram Title: GPR_calculatorCPC Library程序文件链接:https://doi.org/10.17632/vyhpdf9fkh.1Licensing条款:MIT[1]编程语言:Python 3 &;问题的性质:许多原子模拟——例如几何优化、势垒计算、分子动力学和状态方程模拟——需要在紧实的相空间中采样大量的原子构型。虽然密度泛函理论(DFT)为少于数百个原子的系统提供了良好的准确性和相对可扩展的性能,但对于大规模模拟来说,它可能变得过于昂贵。这在表面扩散或反应研究的能量势垒计算中尤其明显,这些计算需要数百或数千个能量和力的评估。解决方法:GPR_calculator是一个基于高斯过程回归(GPR)的动态原子计算器,设计为一个附加模块,可以与流行的原子模拟环境(ASE)一起使用。它本质上是一种混合方法,包括:(i)一个基本计算器,为给定的输入结构提供地面真实参考能量和力,(ii)一个代理模型,作为在飞行中训练的较便宜的近似值。当探地雷达预测的不确定性超过用户定义的阈值时,调用基本计算器来获得准确的结果并更新探地雷达模型。这种自适应方法确保了准确性,同时显著降低了计算成本。参考文献[1]https://opensource.org/licenses/MIT
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations
We present GPR_calculator, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of GPR_calculator, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at https://github.com/MaterSim/GPR_calculator.

Program summary

Program Title: GPR_calculator
CPC Library link to program files: https://doi.org/10.17632/vyhpdf9fkh.1
Licensing provisions: MIT [1]
Programming language:: Python 3 & C++
Nature of problem: Many atomistic simulations—such as geometry optimization, barrier calculations, molecular dynamics, and equation-of-state simulations—require sampling a large number of atomic configurations in a compact phase space. While Density Functional Theory (DFT) provides good accuracy and relatively scalable performance for systems with fewer than hundreds of atoms, it can become prohibitively expensive for massive simulations. This is particularly evident in energy barrier calculations for surface diffusion or reaction studies, where hundreds or thousands of energy and force evaluations are needed.
Solution method: The GPR_calculator is an On-the-Fly Atomistic Calculator based on Gaussian Process Regression (GPR), designed as an add-on module that can be used with the popular Atomic Simulation Environment (ASE). It is essentially a hybrid approach that consists of: (i) a base calculator to provide ground truth reference energy and forces for the given input structure, and (ii) a surrogate model serving as the less expensive approximation trained on-the-fly. When the uncertainty of the GPR prediction exceeds a user-defined threshold, the base calculator is invoked to obtain accurate results and update the GPR model. This adaptive approach ensures accuracy while significantly reducing computational cost.

References

  • [1]
    https://opensource.org/licenses/MIT
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computer Physics Communications
Computer Physics Communications 物理-计算机:跨学科应用
CiteScore
12.10
自引率
3.20%
发文量
287
审稿时长
5.3 months
期刊介绍: The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper. Computer Programs in Physics (CPiP) These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged. Computational Physics Papers (CP) These are research papers in, but are not limited to, the following themes across computational physics and related disciplines. mathematical and numerical methods and algorithms; computational models including those associated with the design, control and analysis of experiments; and algebraic computation. Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信