A gradient-directed Monte Carlo approach to molecular design

TL;DR

This paper introduces a gradient-directed Monte Carlo method combined with the LCAP approach to efficiently optimize molecules for targeted properties, overcoming local optima on complex property surfaces.

Contribution

It presents a novel GDMC strategy that enhances the LCAP method by integrating gradient information and stochastic moves for improved molecular design.

Findings

Identified a porphyrin molecule with higher hyperpolarizability.

Demonstrated effectiveness in optimizing nonlinear optical properties.

Enhanced exploration of molecular space beyond local optima.

Abstract

The recently developed linear combination of atomic potentials (LCAP) approach [M.Wang et al., J. Am. Chem. Soc., 128, 3228 (2006)] allows continuous optimization in discrete chemical space and thus is quite useful in the design of molecules for targeted properties. To address further challenges arising from the rugged, continuous property surfaces in the LCAP approach, we develop a gradient-directed Monte Carlo (GDMC) strategy as an augmentation to the original LCAP optimization method. The GDMC method retains the power of exploring molecular space by utilizing local gradient information computed from the LCAP approach to jump between discrete molecular structures. It also allows random Monte Carlo moves to overcome barriers between local optima on property surfaces. The combined GDMC and LCAP approach is demonstrated here for optimizing nonlinear optical (NLO) properties in a class of donor-acceptor substituted benzene and porphyrin frameworks. Specifically, one molecule with four nitrogen atoms in the porphyrin ring was found to have a larger first hyperpolarizability than structures with the conventional porphyrin motif. 1