High accuracy neural network interatomic potential for NiTi shape memory alloy
H Tang and Y Zhang and QJ Li and HW Xu and YC Wang and YZ Wang and J Li, ACTA MATERIALIA, 238, 118217 (2022).
Nickel-titanium (NiTi) shape memory alloys (SMA) are widely used, however simulating the martensitic transformation of NiTi from first principles remains challenging. In this work, we developed a neural network interatomic potential (NNIP) for near-equiatomic Ni-Ti system through active-learning based acquisitions of density functional theory (DFT) training data, which achieves state-of-the-art accuracy. Phonon dispersion and potential-of-mean-force calculations of the temperature- dependent free energy have been carried out. This NNIP predicts temperature-induced, stress-induced, and defect-induced martensitic transformations from atomic simulations, in significant agreement with experiments. The NNIP can directly simulate the superelasticity of NiTi nanowires, providing a tool to guide their design. (C) 2022 Published by Elsevier Ltd on behalf of Acta Materialia Inc.
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