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Machine-learning-driven advances in modelling amorphous solids

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Machine-learning-driven advances in modelling amorphous solids

Structurally disordered materials continue to pose fundamental research questions. Some of the most central ones concern the atomic-scale structure: how can we quantify an amorphous (non-crystalline) structure at all; how is the structure linked to properties? In this presentation, I will showcase recent advances in the modelling and understanding of amorphous materials that have been enabled by atomistic machine-learning approaches. I will demonstrate how atomistic ML models have given new insight into the complex structural and electronic transitions in amorphous silicon under high pressure [1], and I will discuss initial applications and future perspectives in the area of battery materials modelling [2].

[1] Nature 2021, 589, 59 (https://doi.org/10.1038/s41586-020-03072-z)

[2] J. Phys. Energy 2020, 2, 041003 (https://doi.org/10.1088/2515-7655/abb011)

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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