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Evidence for supercritical behaviour of high-pressure liquid hydrogen

机译：高压液体氢气超临界行为的证据

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摘要

Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression~(1). Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar pressures almost a century ago~(2), several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism~(1,3-5), an anomalous melting line~(6)and the possible transition to a superconducting state~(7). Experiments at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theoretical investigations are constrained by the huge computational cost of sufficiently accurate quantum mechanical calculations. Here we present a theoretical study of the phase diagram of dense hydrogen that uses machine learning to 'learn' potential-energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behaviour and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials provide evidence for a continuous molecular-to-atomic transition in the liquid, with no first-order transition observed above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between experiments as a manifestation of supercritical behaviour.
机译：氢气，宇宙中最简单和最丰富的元素，在压缩时发育出色的复杂行为〜（1）。由于Wigner预测Megabar压力在巨大的氢气的解离和金属化以前〜（2），已经努力解释了致密氢的许多不寻常的性质，包括富含且较差的固体多态性〜（1,3 -5），异常熔化线〜（6）和可能的过渡到超导状态〜（7）。在这种极端条件下的实验是挑战，并且通常导致难以解释和争议的观察，而理论研究受到足够精确的量子力学计算的巨大计算成本的约束。在这里，我们展示了一种致密氢气相图的理论研究，该致密氢相图使用机器学习来“学习”潜在 - 能量表面以及来自参考计算的势力，然后以低计算成本预测它们，克服长度和时间尺度。我们再现重新进入熔化行为和固相的多态性。使用我们的机器学习的电位模拟提供了液体中连续分子对原子过渡的证据，在熔融线上没有观察到的一阶转变。这表明巨型气体行星中的绝缘和金属层之间的平滑过渡，并与实验之间存在的存在差异视为超临界行为的表现。

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  • 来源
    《Nature》 |2020年第7824期|217-220|共4页
  • 作者

    Bingqing Cheng; Guglielmo Mazzola; Chris J Pickard; Michele Ceriotti;

  • 作者单位

    Department of Chemistry University of Cambridge|TCM Group Cavendish Laboratory University of Cambridge|Trinity College;

    IBM Quantum IBM Research - Zurich Rüschlikon;

    Department of Materials Science and Metallurgy University of Cambridge|Advanced Institute for Materials Research Tohoku University;

    Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne|National Centre for Computational Design and Discovery of Novel Materials (MARVEL) École Polytechnique Fédérale de Lausanne;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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