Molecular Dynamics Force Field Genetic Optimization for Tri-n-butyl Phosphate Liquid

TL;DR

This paper develops an iterative optimization algorithm integrating molecular dynamics simulations and genetic algorithms to optimize Lennard-Jones parameters for tri-n-butyl phosphate, improving property predictions.

Contribution

It introduces a neural network-accelerated multi-objective optimization framework for force field parameterization of TBP, enhancing accuracy over previous models.

Findings

Optimized LJ parameters reduce deviation from experimental data to 23%.

Using neural networks allows larger populations and more generations in genetic algorithms.

Transport property predictions remain challenging due to conflicting optimization objectives.

Abstract

An iterative optimization algorithm with MD simulations in the loop is developed and applied to optimize Lennard-Jones (LJ) parameters specific for liquid tri-n-butyl phosphate (TBP). The optimization loop uses non-dominated sorting genetic algorithms to obtain LJ parameters that reproduce key properties such as mass density, electric dipole moment, heat of vaporization, self-diffusion coefficient (SDC), and shear viscosity. Errors relative to experimentally measured properties lead to a multi-objective function optimization problem stated in terms of a Pareto-optimal set. A systematic application of the optimization algorithm to cases involving single- and multi-objective functions was carried out in this work, establishing a framework for atomistic TBP property predictions. We demonstrate the use of a neural network property model to amortize the high cost of MD simulations in the optimization loop and to allow for large populations and more generations to be used in the genetic algorithms. In our previous study of finding the best force field for TBP property predictions as judged by the aforementioned thermophysical properties, we found the Polarized AMBER-MNDO force field to be the best overall showing a \num{74}\% relative deviation from experimental values. However, in this study, we show optimized values of the LJ parameters that improve the overall deviation from experimental data to \num{23}\% when using the NN NSGA-III algorithm. Despite this large improvement, the accurate prediction of the transport properties, SDC and shear viscosity, remains difficult since improvements in one of them worsen the other, and vice versa.