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Updated: Aug 15, 2025
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Correction: Molecular cluster analysis using local order parameters selected by machine learning
1Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology (AIST), Central 2, 1-1-1 Umezono, Tsukuba, 305-8568, Ibaraki, Japan. kazu.takahashi@aist.go.jp.
Physical Chemistry Chemical Physics : PCCP
|January 5, 2023
Summary
This correction clarifies molecular cluster analysis methods. It refines the use of local order parameters selected by machine learning for better accuracy in materials science.
Area of Science:
- Computational materials science
- Machine learning applications
- Chemical physics
Context:
- Accurate characterization of molecular clusters is crucial for understanding material properties.
- Previous studies utilized local order parameters (LOPs) for cluster analysis.
- Machine learning (ML) offers potential for optimizing LOP selection.
Purpose:
- To provide a correction to the original article concerning molecular cluster analysis.
- To refine the methodology for selecting local order parameters using machine learning.
- To enhance the accuracy and efficiency of identifying and analyzing molecular clusters.
Summary:
- The correction addresses the selection process of local order parameters (LOPs) in molecular cluster analysis.
- It clarifies the application of machine learning (ML) algorithms for identifying the most relevant LOPs.
- This ensures more robust and reliable cluster identification in materials simulations.
Impact:
- Improved accuracy in computational materials science simulations.
- Enhanced understanding of molecular structures and dynamics.
- Facilitates the discovery of new materials with desired properties through refined analysis techniques.
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