  Crystal structure prediction (CSP) for inorganic materials is one of the
central and most challenging problems in materials science and computational
chemistry. This problem can be formulated as a global optimization problem in
which global search algorithms such as genetic algorithms (GA) and particle
swarm optimization have been combined with first principle free energy
calculations to predict crystal structures given only a material composition or
only a chemical system. These DFT based ab initio CSP algorithms are
computationally demanding and can only be used to predict crystal structures of
relatively small systems. The vast coordinate space plus the expensive DFT free
energy calculations limits their efficiency and effectiveness. On the other
hand, a similar structure prediction problem has been intensively investigated
in parallel in the protein structure prediction community of bioinformatics, in
which the dominating predictors are knowledge based approaches including
homology modeling and threading that exploit known protein structures. Herein
we explore whether known geometric constraints such as the pairwise atomic
distances of a target crystal material can help predict/reconstruct its
structure given its space group and lattice information. We propose DMCrystal,
a genetic algorithm based crystal structure reconstruction algorithm based on
predicted atomic pairwise distances. Based on extensive experiments, we show
that the predicted distance matrix can dramatically help to reconstruct the
crystal structure and usually achieves much better performance than CMCrystal,
an atomic contact map based crystal structure prediction algorithm. This
implies that knowledge of atomic interaction information learned from existing
materials can be used to significantly improve the crystal structure prediction
in terms of both speed and quality.
