Large‐Scale Calculations by Integrating the Fragmentation Approach With Neural Network Potentials

A fragmentation method is introduced to enable large‐scale molecular simulations using neural network potentials (NNPs). The method partitions a system into cube‐shaped fragments and reconstructs the total energy using a many‐body expansion formalism with a distance‐based cut‐off approximation. Validation with Au, NaCl, diamond, H 2 O, and graphite crystals demonstrated that including three‐body interactions with 26 neighboring fragments reduces per‐atom energy error to within 0.04 eV. This approach enables simulations of systems with up to 1 million atoms, surpassing conventional NNP limits. The scaling exponent for three‐body calculations remains below 1.64, suggesting feasibility for even larger‐scale applications.