基于机器学习原子间势的稀土高熵氧化物离子扩散特性
Ionic Diffusion Properties of Rare-Earth High-Entropy Oxides from a Machine-Learned Interatomic Potential
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中文总结 AI 辅助
研究利用CHGNet机器学习原子间势驱动分子动力学模拟，探究铈基稀土高熵氧化物中氧扩散。通过对三种CHGNet变体基准测试，揭示氧传输受空位浓度和阳离子环境影响，明确成分调整增强氧离子电导率的途径。
中文摘要 AI 辅助
稀土高熵氧化物（RE-HEOs）因其化学复杂性、结构可调性及快速氧离子传输潜力，成为固态电化学应用中有前景的功能陶瓷。本文利用由晶体哈密顿图神经网络（CHGNet）机器学习原子间势驱动的经典分子动力学模拟，研究Ce$_x$(YLaPrSm)$_{1 - x}$O$_{2 - \delta}$形式的铈基RE-HEOs中的氧扩散。为提高对含镧系元素体系的预测准确性，针对明确包含f价电子的目标密度泛函理论（DFT）数据，对三种CHGNet变体（包括微调的r$^2$SCAN训练模型）进行基准测试。跨温度、Ce含量、氧空位浓度以及萤石和方铁锰矿结构的模拟表明，RE-HEOs中的氧传输受两个因素相互作用支配：移动空位浓度和它们跳跃所经过的局部阳离子环境。在固定组成下，离子电导率对空位浓度呈非单调依赖，中等空位水平时扩散最佳，较高浓度时迁移率降低。增加Ce含量降低迁移活化能并通过由Ce-Ce和Ce-Y边缘构建的低势垒扩散网络增强扩散率。对单个氧跳跃事件的分析提供了原子层面的见解，即局部化学环境和短程阳离子有序如何控制高熵氧化物中的传输。总体而言，这项工作表明机器学习原子间势可以解析化学复杂氧化物中的组成-结构-传输关系，并确定通过成分调整增强RE-HEO材料中氧离子电导率的有效途径。
英文摘要
Rare-earth high-entropy oxides (RE-HEOs) have emerged as a promising class of functional ceramics for solid-state electrochemical applications due to their chemical complexity, structural tunability, and potential for fast oxygen-ion transport. In this work, we investigate oxygen diffusion in ceria-based RE-HEOs of the form Ce$_x$(YLaPrSm)$_{1-x}$O$_{2-δ}$ using classical molecular dynamics simulations driven by the Crystal Hamiltonian Graph Neural Network (CHGNet) machine-learned interatomic potential. To improve predictive accuracy for lanthanide-containing systems, we benchmark three CHGNet variants, including a fine-tuned r$^2$SCAN-trained model, against targeted density functional theory (DFT) data that explicitly include f-valence electrons. Simulations across temperature, Ce content, oxygen vacancy concentration, and both fluorite and bixbyite structures reveal that oxygen transport in RE-HEOs is governed by the interplay of two factors: the concentration of mobile vacancies and the local cation environment through which they hop. At fixed composition, ionic conductivity exhibits a non-monotonic dependence on vacancy concentration, with optimal diffusion occurring at moderate vacancy levels and reduced mobility at higher concentrations. Increasing Ce content lowers migration activation energies and enhances diffusivity through low-barrier diffusion networks built from Ce-Ce and Ce-Y edges. Analysis of individual oxygen hopping events provides atomistic insight into how local chemical environments and short-range cation ordering govern transport in high-entropy oxides. Overall, this work demonstrates that machine-learned interatomic potentials can resolve composition-structure-transport relationships in chemically complex oxides, and identifies active pathways through which compositional tuning can enhance oxygen-ion conductivity in RE-HEO materials.
