氩气凝固的混合蒙特卡洛研究

Vahideh Alizadeh, Marco Garofalo, Carsten Urbach, Barbara Kirchner
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摘要

本文介绍了基于 GPU 的混合蒙特卡洛(HMC)算法的实现,以探索其在液态氩到固态氩的实例凝固化学中的应用。我们通过比较 HMC 和 MD 模拟中氩流体样相的结构特征来验证我们的实现。在凝固过程中,MD 和 HMC 显示出相似的趋势。尽管存在可观察到的差异,但在相变过程中,MD 模拟和 HMC 在误差范围内保持一致。空隙的引入降低了凝固温度,有助于形成结构良好的固体。此外,我们的研究结果还强调了较大系统尺寸在模拟凝固过程中的重要性。使用与温度相关的电势进行模拟时,凝固过程的结果并不明确,这可能是由于系统尺寸较小的缘故。未来工作的目标是扩展 HMC 的功能,以处理相变中的复杂化学现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Monte Carlo study of argon solidification
A GPU-based implementation of the Hybrid Monte Carlo (HMC) algorithm is presented to explore its utility in the chemistry of solidification at the example of liquid to solid argon. We validate our implementation by comparing structural characteristics of argon fluid-like phases from HMC and MD simulations. Examining solidification, both MD and HMC show similar trends. Despite observable differences, MD simulations and HMC agree within the errors during the phase transition. Introducing voids decreases the solidification temperature, aiding in the formation of a well-structured solids. Further, our findings highlight the importance of larger system sizes in simulating solidification processes. Simulations with a temperature dependent potential show ambiguous results for the solidification which may be attributed to the small system sizes. Future work aims to expand HMC capabilities for complex chemical phenomena in phase transitions.
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