Liu K L, Xiao R L, Ruan Y, Wei B
MOE Key Laboratory of Materials Physics and Chemistry under Extraordinary Conditions, School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
Phys Rev E. 2023 Nov;108(5-2):055310. doi: 10.1103/PhysRevE.108.055310.
The determination of liquid atomic structure and thermophysical properties is essential for investigating the physical characteristics and phase transitions of refractory alloys. However, due to the stringent experimental requirements and underdeveloped interatomic potentials, acquiring such information through experimentation or simulation remains challenging. Here, an active learning method incorporating a deep neural network was established to generate the interatomic potential of the Hf_{76}W_{24} refractory alloy. Then the achieved potential was applied to investigate the liquid atomic structure and thermophysical properties of this alloy over a wide temperature range. The simulation results revealed the distinctive bonding preferences among atoms, that is, Hf atoms exhibited a strong tendency for conspecific bonding, while W atoms preferred to form an interspecific bonding. The analysis of short-range order (SRO) in the liquid alloy revealed a significant proportion of icosahedral (ICO) and distorted ICO structures, which even exceeded 30% in the undercooled state. As temperature decreased, SRO structures demonstrated an increase in larger coordination number (CN) clusters and a decrease in smaller CNs. The alterations of the atomic structure indicated that the liquid alloy becomes more ordered, densely packed, and energetically favorable with decreasing temperature, consistent with the obtained fact: Both density and surface tension increase linearly. The simulated thermophysical properties were close to experimental values with minor deviations of 2.8% for density and 3.4% for surface tension. The consistency of the thermophysical properties further attested to the accuracy and reliability of active learning simulation.
确定液态原子结构和热物理性质对于研究难熔合金的物理特性和相变至关重要。然而，由于严格的实验要求和原子间势的不完善，通过实验或模拟获取此类信息仍然具有挑战性。在此，建立了一种结合深度神经网络的主动学习方法来生成Hf₇₆W₂₄难熔合金的原子间势。然后将所获得的势应用于研究该合金在很宽温度范围内的液态原子结构和热物理性质。模拟结果揭示了原子之间独特的键合偏好，即Hf原子表现出强烈的同种键合倾向，而W原子则倾向于形成异种键合。对液态合金中短程有序（SRO）的分析揭示了相当比例的二十面体（ICO）和扭曲的ICO结构，在过冷状态下甚至超过30%。随着温度降低，SRO结构显示出较大配位数（CN）簇的增加和较小CNs的减少。原子结构的变化表明，液态合金随着温度降低变得更加有序、紧密堆积且能量上更有利，这与所获得的事实一致：密度和表面张力均呈线性增加。模拟的热物理性质与实验值接近，密度的偏差较小，为2.8%，表面张力的偏差为3.4%。热物理性质的一致性进一步证明了主动学习模拟的准确性和可靠性。
