Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors,Journal of the American Chemical Society

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Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors
Journal of the American Chemical Society ( IF 14.4 ) Pub Date : 2024-09-18 , DOI: 10.1021/jacs.4c07812
Willis O'Leary ₁ , Manuel Grumet ₂ , Waldemar Kaiser ₂ , Tomáš Bučko _{3,

4} , Jennifer L M Rupp _{1,

5} , David A Egger _{2,

6}

Affiliation

The successful design of solid-state photo- and electrochemical devices depends on the careful engineering of point defects in solid-state ion conductors. Characterization of point defects is critical to these efforts, but the best-developed techniques are difficult and time-consuming. Raman spectroscopy─with its exceptional speed, flexibility, and accessibility─is a promising alternative. Raman signatures arise from point defects due to local symmetry breaking and structural distortions. Unfortunately, the assignment of these signatures is often hampered by a shortage of reference compounds and corresponding reference spectra. This issue can be circumvented by calculation of defect-induced Raman signatures from first principles, but this is computationally demanding. Here, we introduce an efficient computational procedure for the prediction of point defect Raman signatures in solid-state ion conductors. Our method leverages machine-learning force fields and “atomic Raman tensors”, i.e., polarizability fluctuations due to motions of individual atoms. We find that our procedure reduces computational cost by up to 80% compared to existing first-principles frozen-phonon approaches. These efficiency gains enable synergistic computational–experimental investigations, in our case allowing us to precisely interpret the Raman spectra of Sr(Ti_0.94Ni_0.06)O_3-δ, a model oxygen ion conductor. By predicting Raman signatures of specific point defects, we determine the nature of dominant defects and unravel impacts of temperature and quenching on in situ and ex situ Raman spectra. Specifically, our findings reveal the temperature-dependent distribution and association behavior of oxygen vacancies and nickel substitutional defects. Overall, our approach enables rapid Raman-based characterization of point defects to support defect engineering in novel solid-state ion conductors.

中文翻译：

使用拉曼光谱、机器学习力场和原子拉曼张量快速表征固态离子导体中的点缺陷

固态光电和电化学装置的成功设计取决于固态离子导体中点缺陷的精心设计。点缺陷的表征对于这些努力至关重要，但最先进的技术既困难又耗时。拉曼光谱以其卓越的速度、灵活性和可访问性，是一种有前途的替代方案。拉曼特征是由局部对称性破缺和结构扭曲导致的点缺陷产生的。不幸的是，这些特征的分配常常因缺乏参考化合物和相应的参考光谱而受到阻碍。这个问题可以通过根据第一原理计算缺陷引起的拉曼特征来规避，但这对计算要求很高。在这里，我们介绍了一种用于预测固态离子导体中点缺陷拉曼特征的有效计算程序。我们的方法利用机器学习力场和“原子拉曼张量”，即单个原子运动引起的极化率波动。我们发现，与现有的第一原理冻结声子方法相比，我们的程序可降低高达 80% 的计算成本。这些效率的提高使得协同计算实验研究成为可能，在我们的例子中，我们能够精确解释模型氧离子导体Sr(Ti _0.94 Ni _0.06 )O _3-δ的拉曼光谱。通过预测特定点缺陷的拉曼特征，我们确定了主要缺陷的性质，并揭示了温度和淬火对原位和异位拉曼光谱的影响。具体来说，我们的研究结果揭示了氧空位和镍替代缺陷的温度依赖性分布和关联行为。总体而言，我们的方法能够对点缺陷进行基于拉曼的快速表征，以支持新型固态离子导体中的缺陷工程。

更新日期：2024-09-18

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