Structurally disordered materials continue to pose fundamental questions [1][2][3][4] , including that of how different disordered phases ("polyamorphs") can coexist and transform from one to another 5-9 . As a widely studied case, amorphous silicon (a-Si) forms a fourfold-coordinated, covalent network at ambient conditions and much higher-coordinated, metalliclike phases under pressure 10-12 . However, a detailed mechanistic understanding of the structural transitions in disordered silicon has been lacking, due to intrinsic limitations of even the most advanced experimental and computational techniques. Here, we show how atomistic machine-learning (ML) models can break through this long-standing barrier, describing liquid-amorphous and amorphous-amorphous transitions with quantum-mechanical accuracy for a system of 100,000 atoms (ten-nanometre length scale). Our simulations reveal a three-step transformation sequence for a-Si under increasing external pressure. First, polyamorphic low-and high-density amorphous (LDA and HDA) 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 poly-crystalline structure, consistent with experiments [13][14][15] but not seen in earlier simulations 11,[16][17][18] . An ML model for electronic densities of states (DOS) confirms the onset of metallicity during VHDA formation and subsequent crystallisation. These results shed new light on liquid and amorphous states of silicon, and, in a wider context, they exemplify a holistic, ML-driven approach to predictive materials mod-

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