@article{TEXTUAL,
      recid = {12822},
      author = {Horwath, James P. and Lin, Xiao-Min and He, Hongrui and  Zhang, Qingteng and Dufresne, Eric M. and Chu, Miaoqi and  Sankaranarayanan, Subramanian K. R. S. and Chen, Wei and  Narayanan, Suresh and Cherukara, Mathew J.},
      title = {AI-NERD: Elucidation of relaxation dynamics beyond  equilibrium through AI-informed X-ray photon correlation  spectroscopy},
      journal = {Nature Communications},
      address = {2024-07-15},
      number = {TEXTUAL},
      abstract = {Understanding and interpreting dynamics of functional  materials in situ is a grand challenge in physics and  materials science due to the difficulty of experimentally  probing materials at varied length and time scales. X-ray  photon correlation spectroscopy (XPCS) is uniquely  well-suited for characterizing materials dynamics over  wide-ranging time scales. However, spatial and temporal  heterogeneity in material behavior can make interpretation  of experimental XPCS data difficult. In this work, we have  developed an unsupervised deep learning (DL) framework for  automated classification of relaxation dynamics from  experimental data without requiring any prior physical  knowledge of the system. We demonstrate how this method can  be used to accelerate exploration of large datasets to  identify samples of interest, and we apply this approach to  directly correlate microscopic dynamics with macroscopic  properties of a model system. Importantly, this DL  framework is material and process agnostic, marking a  concrete step towards autonomous materials discovery.},
      url = {http://knowledge.uchicago.edu/record/12822},
}