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The structure of amorphous silicon is widely thought of as afourfold-connected random network, and yet it is defective atoms, with fewer ormore than four bonds, that make it particularly interesting. Despite manyattempts to explain such "dangling-bond" and "floating-bond" defects,respectively, a unified understanding is still missing. Here, we show thatatomistic machine-learning methods can reveal the complex structural andenergetic landscape of defects in amorphous silicon. We study anultra-large-scale, quantum-accurate structural model containing a millionatoms, and more than ten thousand defects, allowing reliable defect-relatedstatistics to be obtained. We combine structural descriptors andmachine-learned local atomic energies to develop a universal classification ofthe different types of defects in amorphous silicon. The results suggest arevision of the established floating-bond model by showing thatfivefold-coordinated atoms in amorphous silicon exhibit a wide range of localenvironments, and it is shown that fivefold (but not threefold) coordinationdefects tend to cluster together. Our study provides new insights into one ofthe most widely studied amorphous solids, and has general implications formodelling and understanding defects in disordered materials beyond siliconalone.

Understanding defects in amorphous silicon with million-atom simulations and machine learning