无序中的结构和电子转换的起源
Volker L Deringer1, Noam Bernstein2, Gábor Csányi3
1Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, UK. volker.deringer@chem.ox.ac.uk.
Nature
|January 7, 2021
概括
机器学习模型显示无形
科学领域:
- 材料科学
- 计算物理
- 固态化学
背景情况:
- 结构不整的材料,如无形,提出了关于相位共存和转换的复杂问题.
- 了解这些转变至关重要,但受到当前实验和计算技术的限制.
研究的目的:
- 阐明在压力下的无形结构转变的机制性理解.
- 使用先进的计算方法探索多态过渡和高密度无形相的形成.
主要方法:
- 在量子力学计算上训练的原子机器学习模型.
- 大规模系统 (100,000个原子) 的模拟以研究液态-无形和无形-无形过渡.
- 电子密度状态的机器学习模型来预测金属性.
主要成果:
- 在压力下观察到低密度和高密度无形区域的共存.
- 确定结构崩进入非常高密度无形 (VHDA) 阶段.
- 证明了VHDA的短暂性质,导致其结晶成为多晶体结构.
结论:
- 该研究提供了压力下无形的详细三步转换序列.
- 机器学习可以对大型系统进行模拟,
- 这项工作突出了基于机器学习的预测材料建模方法.
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