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Origins of structural and electronic transitions in disordered silicon

机译：硅中结构和电子转换的起源

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Structurally disordered materials pose fundamental questions(1-4), including how different disordered phases ('polyamorphs') can coexist and transform from one phase to another(5-9). Amorphous silicon has been extensively studied; it forms a fourfold-coordinated, covalent network at ambient conditions and much-higher-coordinated, metallic phases under pressure(10-12). 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 experiments(13-15) but not seen in earlier simulations(11,16-18). 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.
机译：结构无序材料构成基本问题（1-4），包括不同的无序相（'多甜点）可以将其与一个相的共存和转化（5-9）。非晶硅已被广泛研究;它在环境条件下形成了四倍协调的，共价网络，在压力（10-12）下的高度协调的金属相。然而，由于甚至最先进的实验和计算技术的内在限制，缺乏对无序硅中的结构转变的详细机械理解，例如，在通过模拟可访问的系统尺寸方面，甚至是最先进的实验和计算技术的内在限制。在这里，我们展示了在精确量子机械计算上培训的原始机器学习模型如何有助于描述100,000原子（十纳米长度），预测结构，稳定性和电子性能的系统的液体非晶态和无定形非晶转变。我们的模拟显示了在增加外部压力下的非晶硅的三步变换序列。首先，发现多素数低和高密度无定形区域共存，而不是顺序出现。然后，我们观察到结构塌陷成明显的非常高密度的无定形（VHDA）相。最后，我们的模拟表明该VHDA阶段的瞬态性质：它迅速成核，最终导致形成多晶结构，与实验一致（13-15），但在早期的模拟中没有见过（11,16-18）。用于电子密度的机器学习模型确认了VHDA形成期间金属性的发作和随后的结晶。这些结果揭示了硅和无定形状态的硅，并且在更广泛的背景下，他们举例说明了一种机器学习驱动的预测材料建模方法。

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  • 来源
    《Nature》 |2021年第7840期|59-64|共6页
  • 作者

    Deringer Volker L.; Bernstein Noam; Csanyi Gabor; Ben Mahmoud Chiheb; Ceriotti Michele; Wilson Mark; Drabold David A.; Elliott Stephen R.;

  • 作者单位

    Univ Oxford Dept Chem Inorgan Chem Lab Oxford England;

    US Naval Res Lab Ctr Mat Phys & Technol Washington DC USA;

    Univ Cambridge Engn Lab Cambridge England;

    Ecole Polytech Fed Lausanne Lab Computat Sci & Modeling IMX Lausanne Switzerland|Ecole Polytech Fed Lausanne Natl Ctr Computat Design & Discovery Novel Mat MA Lausanne Switzerland;

    Ecole Polytech Fed Lausanne Lab Computat Sci & Modeling IMX Lausanne Switzerland|Ecole Polytech Fed Lausanne Natl Ctr Computat Design & Discovery Novel Mat MA Lausanne Switzerland;

    Univ Oxford Dept Chem Phys & Theoret Chem Lab Oxford England;

    Ohio Univ Dept Phys & Astron Athens OH 45701 USA;

    Univ Cambridge Dept Chem Cambridge England|Trinity Coll Cambridge England;

  • 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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