An Improved High-Throughput Data Processing Based on Combinatorial Materials Chip Approach for Rapid Construction of...

Area of Science:

• Materials Science
• Computational Materials Science
• Crystallography

Background:

• Combinatorial materials chips offer efficient composition-phase map construction compared to conventional methods.
• High-throughput characterization and automated mapping face challenges from experimental limitations and imperfect analyses.
• Effective data preprocessing and refined automated analysis are crucial for accurate processing of large experimental datasets.

Purpose of the Study:

• To develop and validate an automated data processing pipeline for Fe-Cr-Ni combinatorial materials chips.
• To improve the accuracy and efficiency of composition-phase mapping using high-throughput characterization data.
• To address limitations in automated analysis for accurate phase identification and mapping.

Main Methods:

• Pixel-by-pixel structural and compositional characterization using microbeam X-ray diffraction and electron probe microanalysis.
• A three-step automated preprocessing technique (baseline drift removal, noise elimination, baseline correction) for X-ray diffraction patterns.
• Hierarchy clustering analysis with simplified vectorization of preprocessed data, incorporating human experience.

Main Results:

• Successful elimination of baseline drift and system noise in X-ray diffraction patterns.
• Achieved a 91.15% correct rate for automated composition-phase map construction compared to manual mapping.
• Demonstrated feasibility and improved accuracy for automated composition-phase mapping.

Conclusions:

• The proposed automated data processing method significantly enhances the accuracy and speed of composition-phase mapping.
• The method is effective in handling large datasets from combinatorial materials experiments.
• The findings support the use of automated processing for accelerating materials discovery and development.