AccelNet: Exact backward-compatible acceleration of polynomial angular descriptors through Cartesian moment factorization

Yuki Nagai

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arXiv · 2608.03280

AccelNet: Exact backward-compatible acceleration of polynomial angular descriptors through Cartesian moment factorization

Yuki Nagai

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Abstract

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 $\cos \theta$, 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 H$_2$O and TiO$_2$ 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.

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Yuki Nagai. 2026-08-04. AccelNet: Exact backward-compatible acceleration of polynomial angular descriptors through Cartesian moment factorization. https://arxiv.org/abs/2608.03280

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