Liao Jiangwen, Pei Jiajing, Zhang Guikai, An Pengfei, Chu Shengqi, Ji Yuanyuan, Huang Huan, Zhang Jing, Dong Juncai
Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
J Phys Condens Matter. 2024 Feb 14;36(19). doi: 10.1088/1361-648X/ad2589.
Pressure-induced structural phase transitions play a pivotal role in unlocking novel material functionalities and facilitating innovations in materials science. Nonetheless, unveiling the mechanisms of densification, which relies heavily on precise and comprehensive structural analysis, remains a challenge. Herein, we investigated the archetypal4 →1 phase transition pathway in ZnO by combining x-ray absorption fine structure (XAFS) spectroscopy with machine learning. Specifically, we developed an artificial neural network (NN) to decipher the extended-XAFS spectra by reconstructing the partial radial distribution functions of Zn-O/Zn pairs. This provided us with access to the evolution of the structural statistics for all the coordination shells in condensed ZnO, enabling us to accurately track the changes in the internal structural parameterand the anharmonic effect. We observed a clear decrease inand an increased anharmonicity near the onset of the4 →1 phase transition, indicating a preference for the iT phase as the intermediate state to initiate the phase transition that can arise from the softening of shear phonon modes. This study suggests that NN-based approach can facilitate a more comprehensive and efficient interpretation of XAFS under complexconditions, which paves the way for highly automated data processing pipelines for high-throughput and real-time characterizations in next-generation synchrotron photon sources.
压力诱导的结构相变在揭示新型材料功能和推动材料科学创新方面起着关键作用。然而，揭示致密化机制仍然是一项挑战，因为这在很大程度上依赖于精确而全面的结构分析。在此，我们通过将X射线吸收精细结构（XAFS）光谱与机器学习相结合，研究了ZnO中典型的4→1相变路径。具体而言，我们开发了一种人工神经网络（NN），通过重建Zn-O/Zn对的部分径向分布函数来解读扩展XAFS光谱。这使我们能够了解凝聚态ZnO中所有配位壳层的结构统计信息的演变，从而能够准确跟踪内部结构参数的变化和非谐效应。我们观察到在4→1相变开始时，[此处原文缺失相关内容]明显下降，非谐性增加，这表明iT相作为中间态更受青睐，可引发由剪切声子模式软化引起的相变。这项研究表明，基于NN的方法可以促进在复杂条件下对XAFS进行更全面、高效的解读，为下一代同步加速器光子源的高通量和实时表征的高度自动化数据处理管道铺平了道路。
