Machine learning molecular dynamics reveals the structural origin of the first sharp diffraction peak in high-density silica glasses

• Published in: Scientific reports Vol. 13; no. 1; p. 18721
• Main Authors: Kobayashi, Keita, Okumura, Masahiko, Nakamura, Hiroki, Itakura, Mitsuhiro, Machida, Masahiko, Urata, Shingo, Suzuya, Kentaro
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034; 639/638/563/606; 639/638/563/981; 639/766/94; Accuracy; Compression; Diffraction; Glass; Humanities and Social Sciences; Investigations; Learning algorithms; Machine learning; multidisciplinary; Periodicity; Science; Science (multidisciplinary); Silica; Simulation; Temperature
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-44732-0

The first sharp diffraction peak (FSDP) in the total structure factor has long been regarded as a characteristic feature of medium-range order (MRO) in amorphous materials with a polyhedron network, and its underlying structural origin is a subject of ongoing debate. In this study, we utilized machine learning molecular dynamics (MLMD) simulations to explore the origin of FSDP in two typical high-density silica glasses: silica glass under pressure and permanently densified glass. Our MLMD simulations accurately reproduce the structural properties of high-density silica glasses observed in experiments, including changes in the FSDP intensity depending on the compression temperature. By analyzing the simulated silica glass structures, we uncover the structural origin responsible for the changes in the MRO at high density in terms of the periodicity between the ring centers and the shape of the rings. The reduction or enhancement of MRO in the high-density silica glasses can be attributed to how the rings deform under compression.