The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials
Ian Chesser, Mashroor Nitol, Esther C. Hessong, Himanshu Joshi, Nikhil Admal, Brandon Runnels, Daniel N. Blaschke, Khanh Dang, Abigail Hunter, Saryu Fensin

TL;DR
This study combines machine learning-based interatomic potentials with experimental TEM data to analyze twin boundary structures and migration mechanisms in tetragonal $eta$-Sn, revealing new low-energy interface types.
Contribution
It introduces ML-based interatomic potentials specifically developed for $eta$-Sn, enabling detailed atomistic simulations of twinning in this low-symmetry material.
Findings
ML potentials agree with TEM observations
Identification of faceted twin boundaries
Discovery of low-energy asymmetric PA/AP interfaces
Abstract
Although atomistic simulations have contributed significantly to our understanding of twin boundary structure and migration in metals and alloys with hexagonal close packed (HCP) crystal structures, few direct atomistic studies of twinning have been conducted for other types of low symmetry materials, in large part due to a lack of reliable interatomic potentials. In this work, we examine twin boundary structure and migration in a tetragonal material, -Sn, comparing high resolution Transmission Electron Microscopy (TEM) images of deformation twins in -Sn to the results of direct atomistic simulations using multiple interatomic potentials. ML-based potentials developed in this work are found to give results consistent with our experimental data, revealing faceted twin boundary structures formed by the nucleation and motion of twinning disconnections. We use…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
