  Data-driven approaches are particularly useful for computational materials
discovery and design as they can be used for rapidly screening over a very
large number of materials, thus suggesting lead candidates for further in-depth
investigations. A central challenge of such approaches is to develop a
numerical representation, often referred to as a fingerprint, of the materials.
Inspired by recent developments in chem-informatics, we propose a class of
hierarchical motif-based topological fingerprints for materials composed of
elements such as C, O, H, N, F, etc., whose coordination preferences are well
understood. We show that these fingerprints, when representing either molecules
or crystals, may be effectively mapped onto a variety of properties using a
similarity-based learning model and hence can be used to predict relevant
properties of a material, given that its fingerprint can be defined. Two simple
procedures are introduced to demonstrate that the learning model can be
inverted to identify the desired fingerprints and then, to reconstruct
molecules which possess a set of targeted properties.
