Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning

LC Erhard and J Rohrer and K Albe and VL Deringer, NATURE COMMUNICATIONS, 15, 1927 (2024).

DOI: 10.1038/s41467-024-45840-9

Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few- nanometre length scales can be captured with active machine learning. Understanding the silicon-oxygen system is crucial for various applications. Here, the authors present an interatomic potential covering a wide range of the Si-O configurational space and showcase applications to silica and Si-SiO2 interfaces.

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