On reverse Monte Carlo constraints and model reproduction.

Area of Science:

• Computational materials science
• Chemical physics
• Structural analysis

Background:

• Reverse Monte Carlo (RMC) simulations are widely used for structural analysis of disordered materials.
• Experimentally motivated constraints are crucial for guiding RMC simulations.
• Accurate representation of chemical structural units is vital in multi-elemental systems.

Purpose of the Study:

• To evaluate the effectiveness of various experimentally motivated constraints in RMC simulations.
• To identify potential inaccuracies in RMC-derived structures of multi-elemental systems.
• To propose improved constraint strategies for RMC methodology.

Main Methods:

• Performing Reverse Monte Carlo (RMC) simulations on a ternary ab initio model.
• Testing combinations of five experimentally motivated constraints.
• Analyzing the reproduction of chemical structural unit populations.

Main Results:

• Low energy structures derived from common RMC constraints can inaccurately describe chemical structural unit populations.
• The presence of multiple elements exacerbates inaccuracies when using standard constraints.
• An elemental bond type constraint was identified as a key factor for accurate reproduction.

Conclusions:

• Standard constraints in RMC simulations may lead to erroneous structural descriptions in complex multi-elemental systems.
• The inclusion of elemental bond type constraints significantly enhances the reliability of RMC structural analysis.
• This finding is critical for advancing the accurate modeling of materials.