High accuracy neural network interatomic potential for NiTi shape memory alloy. (1st October 2022)

• Record Type: Journal Article
• Authors: Tang, Hao; Zhang, Yin; Li, Qing-Jie; Xu, Haowei; Wang, Yuchi; Wang, Yunzhi; Li, Ju
• Is Part Of: Acta materialia. Volume 238(2022)
• Publication Date: 2022-10-01
• Subjects: Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2022.118217
• Languages: English
• ISSNs: 1359-6454

Abstract

Graphical abstract: Abstract: 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.