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2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Extending the accuracy of the SNAP interatomic potential form

   Wood and Thompson · The Journal of Chemical Physics · 2018

   The paper proposes adding quadratic terms to the SNAP interatomic potential, tests it on tantalum training data, and reports better accuracy, a similarity to the embedded atom method, and a need for more training data.

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   1. UncheckedAdding quadratic terms to the SNAP interatomic potential is shown to raise accuracy a lot over the linear form, at only modestly higher computational cost.“The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost.”
   2. UncheckedThe quadratic form of the SNAP interatomic potential needs substantially more training data than simpler forms to avoid overfitting, as neural network potentials do.“Similarly to artificial neural network potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting.”