高压液态的超临界行为证据
Bingqing Cheng1,2,3, Guglielmo Mazzola4, Chris J Pickard5,6
1Department of Chemistry, University of Cambridge, Cambridge, UK. bc509@cam.ac.uk.
Nature
|September 10, 2020
概括
研究人员使用机器学习研究密集的气, 揭示了其液态阶段持续的分子到原子的转变. 这一发现有助于解释行星的内部, 并解决了在中的实验差异
科学领域:
- 凝聚物质物理学
- 计算化学
- 星球科学
背景情况:
- 密集在极大压力下表现出复杂的行为,包括多形态和异常线,这给实验和理论带来了重大挑战.
- 之前对高压的研究受到实验困难和精确量子力学的高计算成本的限制.
研究的目的:
- 通过先进的计算方法理论研究密集的相图.
- 通过使用机器学习进行高效的潜在能量表面计算,克服模拟密集的局限性.
主要方法:
- 开发并利用在参考量子力学计算上训练的机器学习潜力来预测原子间的力量和能量.
- 进行大规模模拟以探索相位图,包括固体多态和融化行为,克服长度和时间尺度的限制.
主要成果:
- 成功复制了密集的回流化行为和固体多态性.
- 提供了液态中连续的分子到原子过渡的证据,而不是线以上的第一阶段过渡.
- 机器学习潜力显著降低了计算成本,使得以前所未有的规模进行模拟.
结论:
- 这项研究表明,在密集的液态中,隔热和金属状态之间有平稳的过渡.
- 通过将它们解释为超临界行为的表现,研究结果使实验观测中的差异和解.
- 开发的机器学习方法为在极端条件下研究其他复杂材料提供了强大的工具.
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