Crystal Prediction via Genetic Algorithms in a Model Chiral System

Abstract

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.

Aggregation of amphiphilic nanocubes in equilibrium and under shear

“…Our complementary modeling approach, combining KMC for rapid parameter space exploration with MD for microscopic analysis, is highly promising for systematically studying the effect of various particle compositions, shapes, and sizes. Further, by combining our model with machine learning methods such as evolutionary algorithms, 65,66 it can also be used to address the inverse problem, namely identifying and optimizing the nanoparticle properties for achieving desired superstructures.…”