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- Title
AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy.
- Authors
Horwath, James P.; Lin, Xiao-Min; He, Hongrui; Zhang, Qingteng; Dufresne, Eric M.; Chu, Miaoqi; Sankaranarayanan, Subramanian K.R.S.; Chen, Wei; Narayanan, Suresh; Cherukara, Mathew J.
- 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. Editorial summary: Understanding non-equilibrium dynamics in materials is hindered by the difficulty of collecting and analyzing experimental data. Here, authors develop an machine learning framework for categorizing and tracking dynamics using time-resolved XPCS.
- Subjects
LIGHT beating spectroscopy; MATERIALS science; DEEP learning; MACHINE learning; MANUFACTURING processes
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-49381-z