Solid-angle based nearest-neighbor algorithm adapted for systems with low coordination number

Area of Science:

• Condensed Matter Physics
• Materials Science
• Computational Materials Science

Background:

• Nearest-neighbor identification is crucial for understanding local structure in condensed matter.
• The solid-angle-based nearest-neighbor (SANN) algorithm is a popular, efficient, parameter-free method.
• Original SANN struggles with overcounting neighbors in low-coordination number systems.

Purpose of the Study:

• To address the overcounting issue in the SANN algorithm for low-coordination systems.
• To introduce a geometric modification that enhances SANN accuracy without new parameters.
• To provide a robust and computationally efficient neighbor identification method.

Main Methods:

• Proposed a geometric modification: the "inscribed circle modification" to the SANN algorithm.
• Benchmarked the modified SANN against Voronoi and original SANN algorithms.
• Tested the algorithms in crystalline, quasicrystalline, and heterogeneous systems in 2D and 3D.

Main Results:

• The inscribed circle modification successfully resolves systematic overcounting in low-coordination lattices.
• The modified SANN algorithm demonstrates robust neighbor identification across diverse system types.
• Maintained computational efficiency and parameter-free nature of the original SANN algorithm.

Conclusions:

• The inscribed circle modification offers a significant improvement to SANN for low-coordination systems.
• This enhanced SANN provides accurate and efficient nearest-neighbor identification in various material structures.
• The method is a valuable tool for local structure analysis in condensed matter research.