  Availability of affordable and widely applicable interatomic potentials is
the key needed to unlock the riches of modern materials modelling. Artificial
neural network based approaches for generating potentials are promising;
however neural network training requires large amounts of data, sampled
adequately from an often unknown potential energy surface. Here we propose a
self-consistent approach that is based on crystal structure prediction
formalism and is guided by unsupervised data analysis, to construct an
accurate, inexpensive and transferable artificial neural network potential.
Using this approach, we construct an interatomic potential for Carbon and
demonstrate its ability to reproduce first principles results on elastic and
vibrational properties for diamond, graphite and graphene, as well as energy
ordering and structural properties of a wide range of crystalline and amorphous
phases.

Materials Science