Materials design has traditionally evolved through trial-error approaches,
mainly due to the non-local relationship between microstructures and properties
such as strength and toughness. We propose 'alloy informatics' as a machine
learning based prototype predictive approach for alloys and compounds, using
electron charge density profiles derived from first-principle calculations. We
demonstrate this framework in the case of hydrogen interstitials in
face-centered cubic crystals, showing that their differential electron charge
density profiles capture crystal properties and defect-crystal interaction
properties. Radial Distribution Functions (RDFs) of defect-induced differential
charge density perturbations highlight the resulting screening effect, and,
together with hydrogen Bader charges, strongly correlate to a large set of
atomic properties of the metal species forming the bulk crystal. We observe the
spontaneous emergence of classes of charge responses while coarse-graining over
crystal compositions. Nudge-Elastic-Band calculations show that RDFs and charge
features also connect to hydrogen migration energy barriers between
interstitial sites. Unsupervised machine-learning on RDFs supports
classification, unveiling compositional and configurational non-localities in
the similarities of the perturbed densities. Electron charge density
perturbations may be considered as bias-free descriptors for a large variety of
defects