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.

中文翻译：

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氩凝固的混合蒙特卡罗研究

提出了基于 GPU 的混合蒙特卡罗 (HMC) 算法实现，以探索其在液态到固态氩的凝固化学中的实用性。我们通过比较来自 HMC 和 MD 模拟的类氩流体相的结构特征来验证我们的实施。检查凝固情况，MD 和 HMC 都显示出相似的趋势。尽管存在明显差异，MD 模拟和 HMC 在相变期间的误差范围内是一致的。引入空隙会降低凝固温度，有助于形成结构良好的固体。此外，我们的研究结果强调了较大系统尺寸在模拟凝固过程中的重要性。与温度相关的电势模拟显示出不明确的凝固结果，这可能归因于系统尺寸较小。未来的工作旨在扩展 HMC 处理相变中复杂化学现象的能力。