A parameter-free, solid-angle based, nearest-neighbor algorithm.

Area of Science:

• Computational physics
• Materials science
• Image analysis

Background:

• Identifying nearest neighbors is crucial in many scientific fields.
• Existing algorithms often require parameter tuning or have high computational costs.

Purpose of the Study:

• To present a novel, parameter-free algorithm for nearest neighbor identification.
• To demonstrate the algorithm's advantages in terms of ease of use and computational efficiency.

Main Methods:

• The proposed algorithm, Solid-Angle Based Nearest Neighbor (SANN), assigns a solid angle to each potential neighbor.
• The cutoff radius is determined by ensuring the sum of solid angles equals 4π.
• The algorithm's performance is evaluated by comparing it to fixed-distance cutoff and Voronoi methods.

Main Results:

• SANN demonstrates effective nearest neighbor identification across various systems, including bulk phases and interfaces.
• The algorithm exhibits low computational cost, enabling its use in simulations.
• SANN offers advantages over existing methods in terms of parameter independence and ease of application.

Conclusions:

• The solid-angle based nearest-neighbor algorithm (SANN) provides a robust and efficient parameter-free approach for neighbor identification.
• SANN is versatile, applicable to 3D image analysis and real-time simulations.
• This method offers a valuable alternative to traditional nearest neighbor search techniques.