Crystal Prediction via Genetic Algorithms in a Model Chiral System

Chiral crystals and their constituent molecules play a prominent role in theories about the origin of biological homochirality and in drug discovery, design, and stability. Although the prediction and identification of stable chiral crystal structures is crucial for numerous technologies, including separation processes and polymorph selection and control, predictive ability is often complicated by a combination of many body interactions and molecular complexity and handedness. In this work, we address these challenges by applying genetic algorithms to predict the ground-state crystal lattices formed by a chiral tetramer molecular model, which we have previously shown to exhibit complex fluid-phase behavior. Using this approach, we explore the relative stability and structures of the model's conglomerate and racemic crystals, and present a structural phase diagram for the stable Bravais crystal types in the zero-temperature limit.

Automated summary

Chiral crystals and their constituent molecules are important in theories about the origin of biological homochirality and in drug discovery, design, and stability. The prediction and identification of stable chiral crystal structures is complicated by many body interactions and molecular complexity. This study uses genetic algorithms to predict crystal lattices formed by a chiral tetramer molecular model and explores the stability and structures of conglomerate and racemic crystals.