Direct Boltzmann inversion method from particle configurations at arbitrary state points.

Area of Science:

• Computational physics
• Statistical mechanics
• Materials science

Background:

• Inferring interaction potentials is crucial for understanding particle systems.
• Traditional iterative Boltzmann inversion methods can be computationally expensive and limited in applicability.
• Existing methods may struggle with high-density systems.

Purpose of the Study:

• To introduce a novel direct Boltzmann inversion method for inferring interaction potentials.
• To provide a computationally inexpensive and straightforward alternative to iterative methods.
• To develop a method applicable to any state point, including high-density regimes.

Main Methods:

• The method enforces consistency between two independent estimates of the pair correlation function.
• Estimates are derived from interparticle distances and pairwise forces.
• No iterative Monte Carlo simulations are required at each step.

Main Results:

• The direct Boltzmann inversion method is computationally inexpensive and easy to implement.
• The approach is applicable to any state point, overcoming limitations of existing methods.
• Benchmarking on diverse test potentials demonstrates the method's performance.

Conclusions:

• The proposed method offers a simple and general approach for inferring interaction potentials.
• It is broadly applicable, from coarse-grained potentials to effective interactions in non-equilibrium systems.
• This technique provides a valuable tool for computational and statistical physics research.