An atomistic fingerprint algorithm for learning <i>ab initio</i> molecular force fields
An atomistic fingerprint algorithm for learning <i>ab initio</i> molecular force fields
Molecular fingerprints, i.e., feature vectors describing atomistic neighborhood configurations, is an important abstraction and a key ingredient for data-driven modeling of potential energy surface and interatomic force. In this paper, we present the density-encoded canonically aligned fingerprint algorithm, which is robust and efficient, for fitting per-atom scalar and vector quantities. …