重氢液-液相变亚临界特性的解释
Elucidation of the subcritical character of the liquid--liquid transition in dense hydrogen
摘要：
高压氢气的液-液相变是一个长期存在且有争议的问题。Cheng等人最近在《自然》杂志上发表的论文[第585卷，第217页]提出了一系列强有力的主张，大意是所有以前的密度泛函理论分子动力学（MD-DFT）和量子蒙特卡罗计算都是不正确的，因为有限尺寸效应，而且在MD-DFT的情况下，运行时间很短。这些主张的基础是他们使用大型系统和长时间的经典MD，由他们开发的机器学习潜力（MLP）驱动。他们的声明的直接测试是在比Cheng等人使用的更大或更大的系统上进行MD-DFT，并且持续时间比以前的MD-DFT模拟要长得多。我们已经这样做了，发现他们的诊断（规模效应，持续时间限制）都不正确。相反，我们发现，MLP不驱动MD保真度与底层的DFT电子结构，它应该复制。其结果是，MLP驱动的MD结果是人为的，没有系统地连接到MLP训练的理论基础。
英文摘要：
The liquid-liquid phase transition in high-pressure Hydrogen is a problem of longstanding and controversy. The recent Nature paper by Cheng et al. [vol. 585, p. 217] makes a set of strong claims to the effect that all the previous density functional theory molecular dynamics (MD-DFT) and quantum Monte Carlo calculations of that transition are incorrect because of finite size effects and, in the MD-DFT case, short run times. The basis of those claims is their use of large systems and long durations for classical MD driven by a machine-learnt potential (MLP) which they developed. The straightforward test of their claims is to do MD-DFT on systems as large or larger than Cheng et al. used and for significantly longer durations than in the previous MD-DFT simulations. We have done so and find that neither diagnosis of theirs (size effects, duration limits) is correct. Instead, we find that the MLP does not drive MD in fidelity with the underlying DFT electronic structure that it is supposed to replicate. The result is that the MLP-driven MD results are artifactual, not systematically connected to the theoretical underpinning on which the MLP was trained.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
