  High-throughput computational materials design promises to greatly accelerate
the process of discovering new materials and compounds, and of optimizing their
properties. The large databases of structures and properties that result from
computational searches, as well as the agglomeration of data of heterogeneous
provenance leads to considerable challenges when it comes to navigating the
database, representing its structure at a glance, understanding
structure-property relations, eliminating duplicates and identifying
inconsistencies. Here we present a case study, based on a data set of
conformers of amino acids and dipeptides, of how machine-learning techniques
can help addressing these issues. We will exploit a recently developed strategy
to define a metric between structures, and use it as the basis of both
clustering and dimensionality reduction techniques showing how these can help
reveal structure-property relations, identify outliers and inconsistent
structures, and rationalise how perturbations (e.g. binding of ions to the
molecule) affect the stability of different conformers.
