Optimization of Transferable Site–Site Potentials Using a Combination of Stochastic and Gradient Search Algorithms

Abstract

Discontinuous molecular dynamics (DMD) simulation and thermodynamic perturbation theory (TPT) have been used to study thermodynamic properties for organic compounds. The aim is to infer transferable intermolecular potential models based on correlating the vapor pressure and liquid density. The combination of DMD/TPT generates a straightforward global optimization problem for the attractive potential, instead of facing an iterative optimization–simulation type problem. This global optimization problem is then formulated as a black-box optimization problem and solved using a combination of random recursive search (RRS) and Levenberg–Marquardt (LM) optimization. RRS is suitable for black-box optimization problems since its algorithm is robust to the effect of random noises in the objective function and is advantageous in optimizing the objective function with negligible parameters. LM is efficient local to an optimum with a smooth response surface. The local response surface is shown to be smooth but very flat along valleys with a high degree of coupling between the potential parameters. The algorithm is demonstrated with discretized versions of the Lennard-Jones (LJ) potential and a linear step potential using a database of 231 hydrocarbons, alcohols, aldehydes, amines, amides, esters, ethers, ketones, phenols, sulfides, and thiols. A correspondence is established between the discretized LJ potential and the TraPPE model, demonstrating the manner of improving density estimates and a way of expediting improvement of continuous transferable potentials.