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O: Fachverband Oberflächenphysik

O 43: Frontiers of Electronic Structure Theory: Focus on Artificial Intelligence Applied to Real Materials 1

O 43.1: Vortrag

Mittwoch, 7. September 2022, 10:30–10:45, S054

Structure of Amorphous Phosphorus from Machine Learning-Driven Simulations — •Yuxing Zhou, William Kirkpatrick, and Volker L. Deringer — Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford Oxford OX1 3QR, UK

Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. In this talk, we show that a general-purpose Gaussian approximation potential (GAP) model for phosphorus can be created by machine learning (ML) from a suitably chosen ensemble of quantum-mechanical results. Its accuracy in describing the amorphous phase is demonstrated via large-scale molecular-dynamics simulations on the atomic structure of a-P: the calculated structure factors yield good agreement with earlier experimental evidence. Abundant five-membered rings are found in the structural model, which are the building block of more complex clusters. We provide new insights into the cluster fragments under pressure: an analysis of cluster fragments, large rings, and voids suggests that moderate pressure (up to about 5 GPa) does not break the connectivity of clusters, but higher pressure does. Changes in the simulated first sharp diffraction peak during compression and decompression indicate a hysteresis in the recovery of medium-range order. Our work provides a starting point for further computational studies of a-P, and more generally it exemplifies how ML-driven modeling can accelerate the understanding of disordered functional materials.