Structurally disordered materials pose fundamental questions^1,2,3,4, including how different disordered phases (‘polyamorphs’) can coexist and transform from one phase to another^5,6,7,8,9. Amorphous silicon has been extensively studied; it forms a fourfold-coordinated, covalent network at ambient conditions and much-higher-coordinated, metallic phases under pressure^10,11,12. 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^13,14,15 but not seen in earlier simulations^11,16,17,18. 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.

结构无序的材料提出了基本问题^1,2,3,4，包括不同的无序相（“多晶型物”）如何共存并从一个相转变为另一个^5,6,7,8,9。非晶硅已被广泛研究；它在环境条件下形成四重配位的共价网络，并在压力下形成更高配位的金属相^10,11,12. 然而，由于即使是最先进的实验和计算技术的内在局限性，例如，在通过模拟可访问的系统尺寸方面，缺乏对无序硅结构转变的详细机械理解。在这里，我们展示了在精确量子力学计算上训练的原子机器学习模型如何帮助描述 100,000 个原子（十纳米长度尺度）系统的液体-非晶态和非晶态-非晶态跃迁，预测结构、稳定性和电子特性。我们的模拟揭示了在外部压力增加的情况下非晶硅的三步转变序列。首先，发现多晶低密度和高密度非晶区域共存，而不是依次出现。然后，我们观察到结构坍塌成独特的超高密度非晶（VHDA）相。最后，我们的模拟表明了这种 VHDA 相的瞬态性质：它迅速使微晶成核，最终导致形成多晶结构，与实验一致^13,14,15但在早期的模拟中没有看到^11,16,17,18。状态电子密度的机器学习模型证实了在 VHDA 形成和随后的结晶过程中金属丰度的开始。这些结果揭示了硅的液态和非晶态，并且在更广泛的背景下，它们举例说明了机器学习驱动的预测材料建模方法。