A Template‑Based Automatic Fragmentation Algorithm for Complex and Large Systems in the Generalized Energy‑Based Fragmentation Framework

A major bottleneck in low‑scaling energy‑based fragmentation methods is the need for manual intervention in the fragmentation step, which is time‑consuming, inconsistent, and hard to generalize across diverse molecular system. To address this challenge, we develop a template‑based automatic fragmentation algorithm that extends the generalized energy‑based fragmentation (GEBF) approach to a wide range of large and complex molecules. A hierarchical SMILES‑encoded GEBF template library for both cyclic and acyclic functional groups enables chemically meaningful and efficient partitioning via structure conversion, macrocycle detection, substructure matching, and small‑fragment merging. Controlling fragment sizes ensures a balance between accuracy and computational cost, while user‑defined templates offer enhanced flexibility. Benchmarks on biomacromolecules, macrocycles, porous organic cages, polyamide oligomers, and ionic liquids reproduce conventional quantum‑chemistry results within a few kcal·mol⁻¹ (or sub‑meV/atom), while reducing the largest subsystem basis size to less than one‑third of the full system. Experimental‑level agreement is achieved in structural and spectroscopic predictions, and systems with ≈1,500 atoms are computed within practical timeframes. This work paves the way for fully automated, scalable, low‑cost, high‑accuracy quantum chemistry, bridging theory and large‑scale real‑world applications.