  Determining the stability of molecules and condensed phases is the
cornerstone of atomistic modelling, underpinning our understanding of chemical
and materials properties and transformations. Here we show that a machine
learning model, based on a local description of chemical environments and
Bayesian statistical learning, provides a unified framework to predict
atomic-scale properties. It captures the quantum mechanical effects governing
the complex surface reconstructions of silicon, predicts the stability of
different classes of molecules with chemical accuracy, and distinguishes active
and inactive protein ligands with more than 99% reliability. The universality
and the systematic nature of our framework provides new insight into the
potential energy surface of materials and molecules.
