The Use of Quantum Molecular Calculations to Guide a Genetic Algorithm: A Way to Search for New Chemistry

Abstract

The process of gene-based molecular evolution has been simulated in silico by using massively parallel density functional theory quantum calculations, coupled with a genetic algorithm, to test for fitness with respect to a target chemical reaction in populations of genetically encoded molecules. The goal of this study was the identification of transition-metal complexes capable of mediating a known reaction, namely the cleavage of N(2) to give the metal nitride. Each complex within the search space was uniquely specified by a nanogene consisting of an eight-digit number. Propagation of an individual nanogene into successive generations was determined by the fitness of its phenotypic molecule to perform the target reaction and new generations were created by recombination and mutation of surviving nanogenes. In its simplest implementation, the quantum-directed genetic algorithm (QDGA) quickly located a local minimum on the evolutionary fitness hypersurface, but proved incapable of progressing towards the global minimum. A strategy for progressing beyond local minima consistent with the Darwinian paradigm by the use of environmental variations coupled with mass extinctions was therefore developed. This allowed for the identification of nitriding complexes that are very closely related to known examples from the chemical literature. Examples of mutations that appear to be beneficial at the genetic level but prove to be harmful at the phenotypic level are described. As well as revealing fundamental aspects of molecular evolution, QDGA appears to be a powerful tool for the identification of lead compounds capable of carrying out a target chemical reaction.