Learning from fine-level fingerprints. a A schematic portrayal of the sub-Angstrom-level atomic environment fingerprinting scheme adopted by Behler and co-workers. η j s denote the widths of Gaussians, indexed by j, placed at the reference atom i whose environment needs to be fingerprinted. The histograms in the right represent the integrated number of atoms within each Gaussian sphere; b Schematic of a typical workflow for the construction of machine learning force fields; c Prediction of atomic forces in the neighborhood of an edge dislocation in bulk Al using the atomic force-learning scheme AGNI and the embedded atom method (EAM), and comparison with the corresponding DFT results (adapted with permission from ref. 105 Copyright (2017) American Chemical Society); d Classifying atomic environments in Si using the SOAP fingerprinting scheme and the Sketch Map program for dimensionality reduction.







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