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铀铌合金在不同实验环境中呈现出复杂的晶体相和独特的力学性能，但原子尺度的相析出和形变损伤机制尚不清楚，其根本原因是缺乏支撑大尺度分子动力学模拟的精确铀铌合金原子相互作用势。本工作基于自主开发的神经网络势能函数及随机搜索方法，构建了覆盖全化学空间的铀铌合金第一性原理计算数据库，并基于神经网络框架建立了具有较高泛化性能和精度的铀铌二元体系机器学习势函数，其能量和力的测试平均绝对误差分别为5.6 meV/atom和0.095 eV/Å，可以精确地描述不同化学成分铀铌合金的晶体空间结构，状态方程及热力学参量。基于该势函数，我们实现了低温时效下铀铌合金相失稳分解过程的原子尺度模拟，初步阐明了Nb析出相对其合金力学性能的影响及原子响应机制。Uranium-niobium alloys exhibit complex crystal phases and unique mechanical behaviors under various thermodynamic states and external loadings. However, the lack of accurate interatomic potentials hinders people’s understanding of the atomic-scale phase behaviors and dynamical processes in this important alloy. In recent years, the development of machine-learning-based force fields has provided a systematic way to generate accurate interatomic potentials on large and complex first-principle-based datasets. However, this crucial nuclear material has received limited attention from researchers in the field of machine-learning potentials.
In this work, based on our previous development of the neural-network potential training and evaluation framework, which we called NNAP, a new neural network potential is constructed for the uranium-niobium alloy system. We employ a combination of random structure search and active learning algorithms to enhance coverage of the chemical and structural space of the alloy system. Testing of the generated potential demonstrates high generalization performance and accuracy. The mean absolute errors in energy and force are 5.6 meV/atom and 0.095 eV/Å on the testing set, respectively. Further calculation results of crystal structure parameters, equation of state and phonon dispersions coincide well with the first-principle or experimental references.
Based on the newly trained potential, we investigated the atomic-scale evolution of the spinodal decomposition process in the U-Nb alloys. We show that the atom-swapping hybrid Monte Carlo can be a powerful tool to understand the thermodynamic evolution of the systems. By employ the atom-swapping hybrid Monte Carlo method, the potential energy reduce due to phase segregation is captured within 5000 steps, while no significant energy reduction is found after 3 ns MD simulation. Finally, we calculate the stress-strain curves under shear loading for different initial states. We found that the Nb precipitation generated strengthened phases in the alloy and significantly changed the deformation behavior of U-Nb alloys, where a disorder shear band emerges in the deformation path of the γ phase alloys. Our work lays a new foundation to understand the mechanical processes in this important alloy system.-
Keywords:
- Uranium-niobium alloys /
- Neural network potential /
- Molecular dynamics /
- Mechanical properties
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