The structure and migration of twin boundaries in tetragonal β-Sn: An application of machine learning based interatomic potentials

Chesser, Ian Wallace; Nitol, Mashroor Shafat; Hessong, Esther C.; Joshi, Himanshu; Admal, Nikhil Chandra; Runnels, Brandon; Blaschke, Daniel N.; Dang, Khanh Quoc; Hunter, Abigail; Fensin, Saryu Jindal

doi:10.1016/j.actamat.2025.121390

The structure and migration of twin boundaries in tetragonal β-Sn: An application of machine learning based interatomic potentials

Journal Article · Mon Aug 18 00:00:00 EDT 2025 · Acta Materialia

DOI:https://doi.org/10.1016/j.actamat.2025.121390· OSTI ID:2584689

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Univ. of California, Irvine, CA (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States)
Iowa State Univ., Ames, IA (United States)

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, β-Sn, comparing high resolution Transmission Electron Microscopy (TEM) images of deformation twins in β-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 bicrystallographic methods in combination with atomistic simulations to analyze the structure, energy and shear coupled migration of observed twin facets in β-Sn. In analogy to Prismatic-Basal (PB/BP) interfaces in HCP metals, we discover low energy asymmetric Prismatic-A-plane (PA/AP) interfaces important to twin growth in β-Sn. Finally, a Moment Tensor Potential (MTP) and Rapid Artificial Neural Network (RANN) interatomic potential suitable for studying twinning and phase transformations in Sn are made publicly available as part of this work.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2584689

Report Number(s):: LA-UR--25-24558; 10.1016/j.actamat.2025.121390

Journal Information:: Acta Materialia, Journal Name: Acta Materialia Vol. 299; ISSN 1359-6454

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
Sn alloys
atomistic simulation
grain boundary energy
grain boundary migration
grand canonical optimization
high strain rate
interatomic potential
machine learning
tin
twinning

The structure and migration of twin boundaries in tetragonal β-Sn: An application of machine learning based interatomic potentials

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