Density-functional-theory approach to the Hamiltonian adaptive resolution simulation method

LA Baptista and RC Dutta and M Sevilla and M Heidari and R Potestio and K Kremer and R Cortes-Huerto, JOURNAL OF PHYSICS-CONDENSED MATTER, 33, 184003 (2021).

DOI: 10.1088/1361-648X/abed1d

In the Hamiltonian adaptive resolution simulation method (H-AdResS) it is possible to simulate coexisting atomistic (AT) and ideal gas representations of a physical system that belong to different subdomains within the simulation box. The Hamiltonian includes a field that bridges both models by smoothly switching on (off) the intermolecular potential as particles enter (leave) the AT region. In practice, external one-body forces are calculated and applied to enforce a reference density throughout the simulation box, and the resulting external potential adds up to the Hamiltonian. This procedure suggests an apparent dependence of the final Hamiltonian on the system's thermodynamic state that challenges the method's statistical mechanics consistency. In this paper, we explicitly include an external potential that depends on the switching function. Hence, we build a grand canonical potential for this inhomogeneous system to find the equivalence between H-AdResS and density functional theory (DFT). We thus verify that the external potential inducing a constant density profile is equal to the system's excess chemical potential. Given DFT's one-to-one correspondence between external potential and equilibrium density, we find that a Hamiltonian description of the system is compatible with the numerical implementation based on enforcing the reference density across the simulation box. In the second part of the manuscript, we focus on assessing our approach's convergence and computing efficiency concerning various model parameters, including sample size and solute concentrations. To this aim, we compute the excess chemical potential of water, aqueous urea solutions and Lennard-Jones (LJ) mixtures. The results' convergence and accuracy are convincing in all cases, thus emphasising the method's robustness and capabilities.

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