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Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles
Janis Timoshenko1, Deyu Lu2, Yuewei Lin3
1Department of Material Science and Chemical Engineering, Stony Brook University , Stony Brook, New York 11794, United States.
The Journal of Physical Chemistry Letters
|September 30, 2017
Summary
This study introduces a new method combining X-ray absorption near-edge structure (XANES) spectroscopy and supervised machine learning (SML) to determine the 3D structure of metal catalysts. This approach successfully reconstructs the size, shape, and morphology of platinum nanoparticles.
Area of Science:
- Materials Science
- Catalysis
- Spectroscopy
- Machine Learning
Background:
- Characterizing heterogeneous catalyst structure under operando conditions is difficult due to limited experimental techniques.
- Atomic-level structural information for catalytic metal species is crucial for understanding reaction mechanisms.
Purpose of the Study:
- To develop a novel method for refining the 3D geometry of metal catalysts using X-ray absorption near-edge structure (XANES) spectroscopy and supervised machine learning (SML).
- To establish a link between XANES spectral features and catalyst geometry through SML.
Main Methods:
- Utilized ab initio XANES simulations to train the SML model.
- Applied SML to experimental XANES data to reconstruct catalyst structure.
- Demonstrated the method on well-defined platinum nanoparticles.
Main Results:
- Successfully reconstructed the average size, shape, and morphology of platinum nanoparticles from experimental XANES data.
- Validated the capability of SML to unravel the relationship between XANES features and catalyst geometry.
- Showcased the potential for on-the-fly XANES analysis.
Conclusions:
- The combined XANES and SML approach provides a powerful tool for determining nanoparticle structure, even under operando conditions.
- This method is applicable to various nanoscale systems and facilitates high-throughput and time-dependent studies.
- Offers a promising avenue for advancing catalyst characterization and design.
