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

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.