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. 2024 Sep 11;2(11):570–586. doi: 10.1021/prechem.4c00051

Figure 2.

Figure 2

(a) The schematic of standard HDNNPs. (b) Radial distribution function (RDF) of a silicon melt at 3000 K was obtained using a cubic 64-atom cell from BPN, other neural network (NN) potentials, and density-functional theory (DFT) (Left). The difference between the energies predicted by the BPNN and the recalculated energies obtained from density-functional theory (DFT) for the initial and final structures in each step of a metadynamics simulation of bulk silicon (Right). Reproduced from ref (24). Copyright 2007 American Physical Society. (c) The schematic of SingleNet. Reproduced from ref (57). Copyright 2020 American Chemical Society. (d) Scheme for LASP implementation of the Behler-Parinello-type NN. Reproduced from ref (59). Copyright 2017 The Royal Society of Chemistry.