AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

James P. Horwath (), Xiao-Min Lin, Hongrui He, Qingteng Zhang, Eric M. Dufresne, Miaoqi Chu, Subramanian K.R.S. Sankaranarayanan, Wei Chen, Suresh Narayanan () and Mathew J. Cherukara ()
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James P. Horwath: Argonne National Laboratory
Xiao-Min Lin: Argonne National Laboratory
Hongrui He: Argonne National Laboratory
Qingteng Zhang: Argonne National Laboratory
Eric M. Dufresne: Argonne National Laboratory
Miaoqi Chu: Argonne National Laboratory
Subramanian K.R.S. Sankaranarayanan: Argonne National Laboratory
Wei Chen: Argonne National Laboratory
Suresh Narayanan: Argonne National Laboratory
Mathew J. Cherukara: Argonne National Laboratory

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49381-z

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