Artificial neural network for deciphering the structural transformation of condensed ZnO by extended X-ray absorption fine structure spectroscopy.

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 archetypal B4→B1 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 parameter u and the anharmonic effect. We observed a clear decrease in u and an increased anharmonicity near the onset of the B4→B1 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 complex in-situ conditions, which paves the way for highly automated data processing pipelines for high-throughput and real-time characterizations in next-generation synchrotron photon sources.