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 (TBAF) 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. The accuracy of the GEBF forces is further validated, enabling reliable geometry optimizations and spectroscopic predictions with near‑experimental agreement. Large polyamide oligomers with ≈1,500 atoms can be computed within practical timeframes. The method also predicts reaction barriers and reaction energies for enzyme‑catalyzed reactions at the level of electron correlation. This work paves the way for fully automated, scalable, low‑cost, high‑accuracy quantum chemistry, bridging theory and large‑scale real‑world applications.