基于第一性原理与机器学习的氢液-液相转变研究
Hydrogen liquid-liquid transition from first principles and machine learning
AI总结：
针对高压氢液-液相转变本质的争议，本研究构建基于MACE架构的机器学习原子间势开展多尺度模拟，发现熔融温度以上该转变为超临界态，仅分子晶体内为一级相变，修正了此前认知。
摘要：
高压氢中从分子态到原子态的液-液相转变（LLT）是涉及行星科学到材料建模等领域的基础课题，但目前学界对LLT的本质仍存在争议。要解决这一问题，数值模拟必须覆盖跨多个数量级的长度与时间尺度。我们通过构建基于MACE神经网络架构的快速高精度机器学习原子间势（MLIP），突破了上述尺寸与时间限制。该MLIP基于Perdew-Burke-Ernzerhof（PBE）密度泛函计算结果训练，采用修正后的损失函数校正分子相的能量偏差。由该MLIP驱动的经典分子动力学与路径积分分子动力学模拟结果显示，在熔融温度以上，LLT始终为超临界状态；对应的Widom线位置与此前从头算PBE计算结果一致，但后者曾预测LLT为一级相变。根据我们的计算，该跨接曲线仅在分子晶体区域内才会变为一级相变，这些结果要求学界重新审视此前提出的LLT图像。
英文摘要：
The molecular-to-atomic liquid-liquid transition (LLT) in high-pressure hydrogen is a fundamental topic touching domains from planetary science to materials modeling. Yet, the nature of the LLT is still under debate. To resolve it, numerical simulations must cover length and time scales spanning several orders of magnitude. We overcome these size and time limitations by constructing a fast and accurate machine-learning interatomic potential (MLIP) built on the MACE neural network architecture. The MLIP is trained on Perdew-Burke-Ernzerhof (PBE) density functional calculations and uses a modified loss function correcting for an energy bias in the molecular phase. Classical and path-integral molecular dynamics driven by this MLIP show that the LLT is always supercritical above the melting temperature. The position of the corresponding Widom line agrees with previous ab initio PBE calculations, which in contrast predicted a first-order LLT. According to our calculations, the crossover line becomes a first-order transition only inside the molecular crystal region. These results call for a reconsideration of the LLT picture previously drawn.
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