用于 NiTi 马氏体中有限温度现象的高维神经网络势
A high-dimensional neural network potential for finite-temperature phenomena in NiTi martensite
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中文总结 AI 辅助
研究针对 NiTi 马氏体相提出高维神经网络势（HDNNP），由 DFT 数据训练，经系统验证，能准确描述相稳定性等，揭示堆垛层错能景观，助力有限温度下结构演化研究，为复杂 NiTi系统原子模拟提供基础。
中文摘要 AI 辅助
我们提出了一种针对 NiTi 形状记忆合金马氏体相的高维神经网络势（HDNNP），该势由密度泛函理论（DFT）数据训练得到。这项工作的一个核心方面是针对控制结构演化的关键性质，包括平衡晶体结构、弹性常数、广义堆垛层错能和振动光谱，对该势相对于基础 DFT 参考方法进行系统验证。HDNNP 准确描述了 B19$^\prime$和 B33 相的相对稳定性，包括约 meV/原子量级的细微能量差异。预测的堆垛层错能景观具有强烈的各向异性，并揭示了优先剪切路径，为变形和孪生机制提供了原子层面的见解。有限温度分子动力学模拟进一步能够研究无约束结构随温度的演化。总体而言，所开发的 HDNNP 为在纳秒时间尺度上对包含数十万个原子的马氏体 NiTi 系统的复杂结构和功能行为进行原子模拟提供了坚实基础。
英文摘要
We present a high-dimensional neural network potential (HDNNP) for the martensitic phase of the NiTi shape-memory alloy trained to density functional theory (DFT) data. A central aspect of this work is the systematic validation of the potential with respect to the underlying DFT reference method for key properties governing structural evolution, including equilibrium crystal structures, elastic constants, generalized-stacking fault energies, and vibrational spectra. The HDNNP accurately describes the relative stability of the B19$^\prime$ and B33 phases, including subtle energy differences on the order of meV/atom. The predicted stacking-fault energy landscape is strongly anisotropic and reveals a preferential shear pathway, providing atomistic insight into deformation and twinning mechanisms. Finite-temperature molecular dynamics simulations further enable the investigation of unconstrained structural evolution as a function of temperature. Overall, the developed HDNNP provides a robust basis for atomistic simulations of the complex structural and functional behavior of martensitic NiTi systems containing hundreds of thousands of atoms on nanosecond time scales.
