  Crystal structure prediction is one of the major unsolved problems in
materials science. Traditionally, this problem is formulated as a global
optimization problem for which global search algorithms are combined with first
principle free energy calculations to predict the ground-state crystal
structure given only a material composition or a chemical system. These ab
initio algorithms usually cannot exploit a large amount of implicit
physicochemical rules or geometric constraints (deep knowledge) of atom
configurations embodied in a large number of known crystal structures. Inspired
by the deep learning enabled breakthrough in protein structure prediction,
herein we propose AlphaCrystal, a crystal structure prediction algorithm that
combines a deep residual neural network model that learns deep knowledge to
guide predicting the atomic contact map of a target crystal material followed
by reconstructing its 3D crystal structure using genetic algorithms. Based on
the experiments of a selected set of benchmark crystal materials, we show that
our AlphaCrystal algorithm can predict structures close to the ground truth
structures. It can also speed up the crystal structure prediction process by
predicting and exploiting the predicted contact map so that it has the
potential to handle relatively large systems. We believe that our deep learning
based ab initio crystal structure prediction method that learns from existing
material structures can be used to scale up current crystal structure
prediction practice. To our knowledge, AlphaCrystal is the first neural network
based algorithm for crystal structure contact map prediction and the first
method for directly reconstructing crystal structures from materials
composition, which can be further optimized by DFT calculations.
