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Using test particle sum rules to construct accurate functionals in classical density functional theory
Melih Gül1, Roland Roth1, Robert Evans2
1University of Tübingen, Institute for Theoretical Physics, Auf der Morgenstelle 14, 72076 Tübingen, Germany.
Fundamental Measure Theory (FMT) was improved by incorporating statistical mechanical sum rules for the fluid phase. This enhances the accuracy of density functional theory (DFT) predictions for hard-sphere systems.
Area of Science:
- Statistical Mechanics
- Physical Chemistry
- Computational Physics
Background:
- Fundamental Measure Theory (FMT) is a robust approach within classical density functional theory (DFT) for modeling hard-sphere fluids.
- A prior FMT formulation by Lutsko introduced two free parameters requiring external physical constraints for determination.
- Previous work focused on crystalline phase stability, leaving fluid phase constraints less explored.
Purpose of the Study:
- To introduce and apply two statistical mechanical sum rules to refine FMT for the hard-sphere fluid phase.
- To determine the two free parameters in FMT by ensuring consistency with fluid phase sum rules.
- To enhance the predictive accuracy of FMT for hard-sphere fluid properties.
Main Methods:
- Employed two statistical mechanical sum rules relevant to the fluid phase.
- Minimized deviations between different calculation routes for excess chemical potential and isothermal compressibility.
- Determined FMT free parameters by enforcing consistency with the chosen sum rules.
Main Results:
- The application of fluid phase sum rules improved the accuracy of FMT predictions for hard-sphere fluids.
- Consistency with sum rules provides a method for parameter determination within FMT.
- The developed approach offers a way to assess the performance of general DFT approximations.
Conclusions:
- Incorporating fluid phase sum rules enhances the predictive power of FMT for hard-sphere systems.
- The test particle sum rules are applicable across various interparticle potentials.
- This method provides a valuable tool for validating and improving DFT approximations.
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