Density functional theory for solvation in molecular solvents
Dr. Shuangliang Zhao
Chemical and Environmental Engineering Department
Solvation is ubiquitous in experiments. In this talk, an accurate classical density functional theory (DFT) is presented for predicting the microscopic structure and thermodynamic properties of an arbitrary molecule solvated in a molecular solvent. The novel free-energy functional is constructed in terms of solvent density \rho(r,\Omega) which depends on position and orientation of solvent molecule. The key input is the inhomogeneous position and orientation dependent solvent direct correlation function, and this direct correlation function is calculated by the homogeneous reference fluid approximation, namely in terms of the direct correlation function of the pure solvent system (the c-function).
Towards precise prediction, we propose the following strategy: we first perform MD simulations of the pure solvent system, and then sample over many solvent configurations so as to compute the position and angle-dependent two-body distribution functions (the h-function). Subsequently applying the so-called molecular Ornstein-Zernike relation, we obtain the corresponding direct correlation function, which serves as input for the free energy functional. In the presence of a given molecular solute, which provides the external potential, this functional can be minimized with respect to water density \rho(r,\Omega), using a 3D Cartesian grid for position and Gauss-Legendre angular grid for orientations, to obtain, at the minimum, the absolute solvation free-energy of the solute and the equilibrium solvent density profile around it.
In comparison with direct MD simulation results, the DFT provides accurate representations of both microscopic structure and thermodynamic properties for a wide variety of solutes dissolved in molecular solvents including acetonitrile, water etc.. Unlike molecular simulations, DFT provides direct information on the free energy from which all thermodynamic properties can be derived.