2026/08/04 by Yuki Nagai
Physics and Astronomy · #cond-mat.mtrl-sci #physics.comp-ph
28 pages, 2 figures, 4 tables
arxiv created 2026/08/04 · arxiv updated 2026/08/05
We present AccelNet, an exact, backward-compatible method for accelerating existing trained aenet and n2p2 neural-network potentials without retraining. For angular terms with separable one-neighbor weights and a finite polynomial dependence on cos θ, the method exploits their hidden finite-rank structure to replace explicit neighbor-pair loops by one-neighbor Cartesian moments. AccelNet reads models trained with either package and reproduces their descriptors, energies, and analytic forces to floating-point roundoff. We verified this equivalence for H2O and TiO2 models and tested the resulting potentials in LAMMPS molecular-dynamics simulations. The implementation, model-conversion tools, and LAMMPS interfaces are released as open-source software.