  We present a new interatomic potential for solids and liquids called Spectral
Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form
and uses machine-learning techniques to reproduce the energies, forces, and
stress tensors of a large set of small configurations of atoms, which are
obtained using high-accuracy quantum electronic structure (QM) calculations.
The local environment of each atom is characterized by a set of bispectrum
components of the local neighbor density projected on to a basis of
hyperspherical harmonics in four dimensions. The bispectrum components are the
same bond-orientational order parameters employed by the GAP potential
[arXiv:0910.1019]. The SNAP potential, unlike GAP, assumes a linear
relationship between atom energy and bispectrum components. The linear SNAP
coefficients are determined using weighted least-squares linear regression
against the full QM training set. This allows the SNAP potential to be fit in a
robust, automated manner to large QM data sets using many bispectrum
coefficients.
