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Maximum-entropy Monte Carlo method for the inversion of the structure factor in simple classical systems
1Institute for Complex Systems, National Research Council, Via del Fosso del Cavaliere 100, IT-00133 Rome, Italy.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 21, 2011
Summary
This study introduces a novel method to determine interaction potentials from partial structure factor data using maximum entropy principles. The approach successfully reconstructs potentials for Lennard-Jones fluids and liquid sodium.
Area of Science:
- Statistical Mechanics
- Computational Physics
- Information Theory
Background:
- Determining interatomic potentials is crucial for understanding material properties.
- Existing methods often require complete structural information, which is not always available.
- The structure factor provides valuable insights into atomic arrangements.
Purpose of the Study:
- To develop a robust method for evaluating interaction potentials from partial structure factor data.
- To leverage the maximum entropy principle for reconstructing radial distribution functions.
- To apply the developed method to both simulated and experimental systems.
Main Methods:
- Utilizing the maximum entropy principle in two phases.
- Estimating the radial distribution function from the structure factor.
- Inverting the pair function to extract the interaction potential.
- Testing the method on Lennard-Jones fluids and experimental liquid sodium data.
Main Results:
- The method successfully reconstructs interaction potentials from partial structure factor information.
- The reliability of the method is assessed concerning missing structural data.
- The approach is validated using both simulated (Lennard-Jones fluid) and experimental (liquid sodium) data.
- Accurate interaction potentials were derived for liquid sodium at 100 °C.
Conclusions:
- The maximum entropy-based approach provides a reliable way to determine interaction potentials.
- This method is effective even with incomplete structure factor data.
- The technique offers a valuable tool for analyzing experimental data of liquids.
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