  Crystal structure prediction (CSP) has emerged as one of the most important
approaches for discovering new materials. CSP algorithms based on evolutionary
algorithms and particle swarm optimization have discovered a great number of
new materials. However, these algorithms based on ab initio calculation of free
energy are inefficient. Moreover, they have severe limitations in terms of
scalability. We recently proposed a promising crystal structure prediction
method based on atomic contact maps, using global optimization algorithms to
search for the Wyckoff positions by maximizing the match between the contact
map of the predicted structure and the contact map of the true crystal
structure. However, our previous contact map based CSP algorithms have two
major limitations: (1) the loss of search capability due to getting trapped in
local optima; (2) it only uses the connection of atoms in the unit cell to
predict the crystal structure, ignoring the chemical environment outside the
unit cell, which may lead to unreasonable coordination environments. Herein we
propose a novel multi-objective genetic algorithms for contact map-based
crystal structure prediction by optimizing three objectives, including contact
map match accuracy, the individual age, and the coordination number match.
Furthermore, we assign the age values to all the individuals of the GA and try
to minimize the age aiming to avoid the premature convergence problem. Our
experimental results show that compared to our previous CMCrystal algorithm,
our multi-objective crystal structure prediction algorithm (CMCrystalMOO) can
reconstruct the crystal structure with higher quality and alleviate the problem
of premature convergence.
