无定形固体
多形性
背景(考古学)
化学物理
微晶
非晶硅
硅
材料科学
原子单位
相(物质)
纳米技术
结晶学
晶体硅
物理
化学
光电子学
冶金
古生物学
生物
量子力学
作者
Volker L. Deringer,Noam Bernstein,Gábor Cśanyi,Chiheb Ben Mahmoud,Michele Ceriotti,Mark Wilson,D. A. Drabold,Stephen R. Elliott
出处
期刊:Nature
[Springer Nature]
日期:2021-01-06
卷期号:589 (7840): 59-64
被引量:265
标识
DOI:10.1038/s41586-020-03072-z
摘要
Structurally disordered materials pose fundamental questions1–4, including how different disordered phases (‘polyamorphs’) can coexist and transform from one phase to another5–9. Amorphous silicon has been extensively studied; it forms a fourfold-coordinated, covalent network at ambient conditions and much-higher-coordinated, metallic phases under pressure10–12. However, a detailed mechanistic understanding of the structural transitions in disordered silicon has been lacking, owing to the intrinsic limitations of even the most advanced experimental and computational techniques, for example, in terms of the system sizes accessible via simulation. Here we show how atomistic machine learning models trained on accurate quantum mechanical computations can help to describe liquid–amorphous and amorphous–amorphous transitions for a system of 100,000 atoms (ten-nanometre length scale), predicting structure, stability and electronic properties. Our simulations reveal a three-step transformation sequence for amorphous silicon under increasing external pressure. First, polyamorphic low- and high-density amorphous regions are found to coexist, rather than appearing sequentially. Then, we observe a structural collapse into a distinct very-high-density amorphous (VHDA) phase. Finally, our simulations indicate the transient nature of this VHDA phase: it rapidly nucleates crystallites, ultimately leading to the formation of a polycrystalline structure, consistent with experiments13–15 but not seen in earlier simulations11,16–18. A machine learning model for the electronic density of states confirms the onset of metallicity during VHDA formation and the subsequent crystallization. These results shed light on the liquid and amorphous states of silicon, and, in a wider context, they exemplify a machine learning-driven approach to predictive materials modelling. Machine learning models enable atomistic simulations of phase transitions in amorphous silicon, predict electronic fingerprints, and show that the pressure-induced crystallization occurs over three distinct stages.
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