MgCl2熔盐的材料与热性质：从头算与机器学习分子动力学模拟
Material and thermal properties of MgCl2 molten salt by ab initio and machine-learning molecular-dynamics simulations
- CNEA（国家原子能委员会）
- CAC-CNEA（核能应用研究中心）
- CONICET-CNEA（国家科学技术研究委员会-国家原子能委员会）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用从头算和机器学习分子动力学模拟，全面计算了熔融MgCl2在1000-1600K范围内的热物理性质，并评估了多种机器学习势的精度与性能。
AI中文摘要：
我们利用从头算分子动力学（AIMD）和基于机器学习相互作用势（MLIPs）的分子动力学模拟，在宽温度范围内研究了熔融MgCl2的结构、热物理和动力学性质。MLIPs能以极低的计算成本达到接近AIMD的精度，从而能够模拟更大的系统和更长的时间尺度。这为研究非平衡条件下的系统开辟了可能性，进而允许计算诸如热导率或粘度等物理性质，这些性质在AIMD可访问的典型时间和长度尺度下无法可靠获得。我们采用两种互补的方法来开发和评估MLIPs。首先，我们利用不同温度下的AIMD轨迹从头开发了一个深度势（DP）。其次，我们还评估了两个开箱即用的基础模型以及其中一个模型的微调版本。微调使用的是最初用于训练深度势的AIMD轨迹中的构型。我们将训练好的DP、基础模型和AIMD模拟的预测与可用的实验数据进行比较，并对熔融MgCl2的最重要物理量进行了全面计算。我们在1000-1600 K温度范围内计算了密度、径向和角度分布函数、热导率、热容、粘度和扩散系数。该范围与热能储存、第四代核反应堆和聚光太阳能系统等重要技术应用相关。最后，我们简要回顾了工作中使用的不同MLIPs版本的准确性和性能。
英文摘要：
We study the structural, thermophysical and dynamical properties of molten MgCl2 over a wide temperature range using Ab Initio Molecular Dynamics (AIMD) and molecular dynamics simulations based on machine-learning interaction potentials (MLIPs). MLIPs can achieve near-AIMD accuracy at a fraction of the computational cost, enabling simulations of larger systems and longer timescales. This opens up the possibility of studying the system under out-of-equilibrium conditions, which in turn allows for the calculation of physical properties, such as the thermal conductivity or viscosity, that cannot be obtained reliably for the typical time and length scales accessible to AIMD. We follow two complementary approaches to develop and evaluate the MLIPs. Firstly, we develop a Deep Potential (DP) from scratch using AIMD trajectories at different temperatures. Secondly, we also evaluate two out-of-the-box foundation models and a fine-tuned version of one of them. The fine-tuning is performed using configurations from the AIMD trajectories originally used to train the Deep Potential. We compare the predictions of the trained DP, the foundation models and AIMD simulations with the available experimental data and provide a comprehensive calculation of the most important physical quantities of molten MgCl2. We compute densities, radial and angular distribution functions, thermal conductivity, heat capacity, viscosity and diffusion coefficients over the 1000-1600 K temperature range. This range is relevant to important technological applications such as thermal energy storage, generation IV nuclear reactors and concentrated solar power systems. Finally, we briefly review the accuracy and performance of the different MLIPs versions used throughout the work.
