{"title":"GTS:一个Python工具包,用于构建吉布斯热力学表面,并使用应用程序获取高压熔化数据","authors":"Xuan Zhao, Kun Yin","doi":"10.1016/j.cpc.2025.109858","DOIUrl":null,"url":null,"abstract":"<div><div>Various methods are commonly applied for data acquisition in the melting process of substances under high pressure. However, throughout the application of these methods, challenges persist, including significant time and computational requirements, as well as issues related to hysteresis effects. We introduce the GTS package, a Python toolkit based on the work of J. W. Gibbs to obtain melting data at high pressures in a geometrical manner. We outline the theory behind constructing the Gibbs thermodynamic surface, which includes regions representing the solid and liquid phases. Several examples are presented to demonstrate program execution and validate accuracy by comparing results with prior studies. GTS is openly accessible on GitHub: <span><span>https://github.com/computation-mineral-physics-group/GTS</span><svg><path></path></svg></span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> GTS</div><div><em>CPC Library link to program files:</em> <span><span><span>https://doi.org/10.17632/wkkkv6twgk.1</span></span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span><span>https://github.com/computation-mineral-physics-group/GTS</span></span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License, version 3</div><div><em>Programming language:</em> Python 3</div><div><em>Nature of problem:</em> A Python toolkit that efficiently obtains high-pressure melting data, including melting points and thermodynamic potentials of materials, by constructing the Gibbs thermodynamic surface using a geometrical method.</div><div><em>Solution method:</em> With the <em>ab initio</em> molecular dynamics (AIMD) simulation data in the NVT (N, number of atoms; V, volume; T, temperature) ensemble and the reference point, GTS consists of two steps: first building the surface, second producing the melting data.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109858"},"PeriodicalIF":3.4000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GTS: A Python toolkit for building Gibbs thermodynamic surface with application to obtain high-pressure melting data\",\"authors\":\"Xuan Zhao, Kun Yin\",\"doi\":\"10.1016/j.cpc.2025.109858\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Various methods are commonly applied for data acquisition in the melting process of substances under high pressure. However, throughout the application of these methods, challenges persist, including significant time and computational requirements, as well as issues related to hysteresis effects. We introduce the GTS package, a Python toolkit based on the work of J. W. Gibbs to obtain melting data at high pressures in a geometrical manner. We outline the theory behind constructing the Gibbs thermodynamic surface, which includes regions representing the solid and liquid phases. Several examples are presented to demonstrate program execution and validate accuracy by comparing results with prior studies. GTS is openly accessible on GitHub: <span><span>https://github.com/computation-mineral-physics-group/GTS</span><svg><path></path></svg></span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> GTS</div><div><em>CPC Library link to program files:</em> <span><span><span>https://doi.org/10.17632/wkkkv6twgk.1</span></span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span><span>https://github.com/computation-mineral-physics-group/GTS</span></span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License, version 3</div><div><em>Programming language:</em> Python 3</div><div><em>Nature of problem:</em> A Python toolkit that efficiently obtains high-pressure melting data, including melting points and thermodynamic potentials of materials, by constructing the Gibbs thermodynamic surface using a geometrical method.</div><div><em>Solution method:</em> With the <em>ab initio</em> molecular dynamics (AIMD) simulation data in the NVT (N, number of atoms; V, volume; T, temperature) ensemble and the reference point, GTS consists of two steps: first building the surface, second producing the melting data.</div></div>\",\"PeriodicalId\":285,\"journal\":{\"name\":\"Computer Physics Communications\",\"volume\":\"317 \",\"pages\":\"Article 109858\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-09-15\",\"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/S0010465525003601\",\"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/S0010465525003601","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
物质在高压下熔融过程的数据采集通常采用各种方法。然而,在这些方法的应用过程中,挑战仍然存在,包括大量的时间和计算需求,以及与滞后效应相关的问题。我们介绍了GTS包,这是一个基于J. W. Gibbs工作的Python工具包,用于以几何方式获得高压下的熔化数据。我们概述了构建吉布斯热力学表面背后的理论,其中包括代表固相和液相的区域。给出了几个例子来演示程序的执行情况,并通过将结果与先前的研究结果进行比较来验证准确性。GTS可在GitHub上公开访问:https://github.com/computation-mineral-physics-group/GTS.Program summary程序标题:GTSCPC库链接到程序文件:https://doi.org/10.17632/wkkkv6twgk.1Developer's存储库链接:https://github.com/computation-mineral-physics-group/GTSLicensing条款:GNU通用公共许可证,版本3编程语言:Python 3问题性质:一个Python工具包,通过使用几何方法构造吉布斯热力学面,有效地获得高压熔化数据,包括材料的熔点和热力学势。求解方法:以NVT (N,原子数;V,体积;T,温度)系综和参考点中的从头算分子动力学(AIMD)模拟数据为基础,GTS分为两步:首先建立表面,其次生成熔化数据。
GTS: A Python toolkit for building Gibbs thermodynamic surface with application to obtain high-pressure melting data
Various methods are commonly applied for data acquisition in the melting process of substances under high pressure. However, throughout the application of these methods, challenges persist, including significant time and computational requirements, as well as issues related to hysteresis effects. We introduce the GTS package, a Python toolkit based on the work of J. W. Gibbs to obtain melting data at high pressures in a geometrical manner. We outline the theory behind constructing the Gibbs thermodynamic surface, which includes regions representing the solid and liquid phases. Several examples are presented to demonstrate program execution and validate accuracy by comparing results with prior studies. GTS is openly accessible on GitHub: https://github.com/computation-mineral-physics-group/GTS.
Program summary
Program Title: GTS
CPC Library link to program files:https://doi.org/10.17632/wkkkv6twgk.1
Licensing provisions: GNU General Public License, version 3
Programming language: Python 3
Nature of problem: A Python toolkit that efficiently obtains high-pressure melting data, including melting points and thermodynamic potentials of materials, by constructing the Gibbs thermodynamic surface using a geometrical method.
Solution method: With the ab initio molecular dynamics (AIMD) simulation data in the NVT (N, number of atoms; V, volume; T, temperature) ensemble and the reference point, GTS consists of two steps: first building the surface, second producing the melting data.
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.