DF-Fit: A Robust Algorithm for Detection of Crystallographic Information in Atom Probe Tomography Data

Area of Science:

• Materials Science
• Crystallography
• Data Analysis

Background:

• Atom Probe Tomography (APT) is a powerful technique for 3D material analysis.
• Detecting crystallographic information in APT data is crucial for understanding material properties.
• Existing algorithms for crystallographic detection in APT have limitations in robustness and performance.

Purpose of the Study:

• To develop a novel algorithm for improved crystallographic information detection in APT data.
• To enhance the robustness and signal detection performance of existing methods.
• To provide a freely available tool for the scientific community.

Main Methods:

• The algorithm utilizes one-dimensional distribution functions (DFs) in real space.
• An error metric based on expected values for random distributions is employed for crystallography detection.
• Fourier transform approaches are avoided to minimize artifacts.

Main Results:

• The new algorithm demonstrates improved robustness and signal detection performance.
• It successfully obtains orientation information even with high levels of background noise.
• Comparison with Fourier transform methods shows fewer artifacts in simulated datasets.
• An extension aids in automatically identifying high-quality data regions within APT datasets.

Conclusions:

• The developed algorithm offers a significant advancement in detecting crystallographic information from APT data.
• Its real-space approach and error metric provide reliable results under noisy conditions.
• The algorithm and its extension are valuable tools for materials characterization and data quality assessment.