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The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomicpotential that expresses the energy of each atom as a linear function ofselected bispectrum components of the neighbor atoms. An extension of the SNAPform is proposed that includes quadratic terms in the bispectrum components.The extension is shown to provide a large increase in accuracy relative to thelinear form, while incurring only a modest increase in computational cost. Themathematical structure of the quadratic SNAP form is similar to the embeddedatom method (EAM), with the SNAP bispectrum components serving as counterpartsto the two-body density functions in EAM. The effectiveness of the new form isdemonstrated using an extensive set of training data for tantalum structures.Similarly to artificial neural network potentials, the quadratic SNAP formrequires substantially more training data in order to prevent overfitting. Thequality of this new potential form is measured through a robustcross-validation analysis.

Extending the Accuracy of the SNAP Interatomic Potential Form