High accuracy neural network interatomic potential for NiTi shape memory alloy
High accuracy neural network interatomic potential for NiTi shape memory alloy
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DOI：
10.1016/j.actamat.2022.118217
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发表时间：
2022-07
期刊：
影响因子：
9.4
通讯作者：
Hao Tang;Yin Zhang;Qingjie Li;Haowei Xu;Yuchi Wang;Yunzhi Wang;Ju Li
中科院分区：
文献类型：
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作者：
Hao Tang;Yin Zhang;Qingjie Li;Haowei Xu;Yuchi Wang;Yunzhi Wang;Ju Li
镍钛（NiTi）形状记忆合金（SMA）应用广泛，但从第一性原理模拟NiTi的马氏体相变仍然具有挑战性。在这项工作中，我们通过基于主动学习的密度泛函理论（DFT）训练数据的获取，开发了近等原子Ni-Ti系统的神经网络原子间势（NNIP），达到了最先进的精度。进行了声子色散和平均力势的温度相关自由能计算。该NNIP通过原子模拟预测温度诱导、应力诱导和缺陷诱导的马氏体转变，与实验结果一致。NNIP可以直接模拟镍钛纳米线的超弹性，为其设计提供了指导工具。
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.