  One endeavour of modern physical chemistry is to use bottom-up approaches to
design materials and drugs with desired properties. Here we introduce an
atomistic structure learning algorithm (ASLA) that utilizes a convolutional
neural network to build 2D compounds and layered structures atom by atom. The
algorithm takes no prior data or knowledge on atomic interactions but inquires
a first-principles quantum mechanical program for physical properties. Using
reinforcement learning, the algorithm accumulates knowledge of chemical
compound space for a given number and type of atoms and stores this in the
neural network, ultimately learning the blueprint for the optimal structural
arrangement of the atoms for a given target property. ASLA is demonstrated to
work on diverse problems, including grain boundaries in graphene sheets,
organic compound formation and a surface oxide structure. This approach to
structure prediction is a first step toward direct manipulation of atoms with
artificially intelligent first principles computer codes.
