New machine learning potentials for beta-tin reproduce the experimentally observed structure and growth morphology of deformation twins, showing that twin growth proceeds via double-layer twinning disconnections and low-energy PA/AP facets.
Microstructure characterization and elastic-plastic self- consistent simulation studies of anisotropic deformation of β-tin
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The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials
New machine learning potentials for beta-tin reproduce the experimentally observed structure and growth morphology of deformation twins, showing that twin growth proceeds via double-layer twinning disconnections and low-energy PA/AP facets.