A Divide-and-Conquer Embedded Correlated Wavefunction Transfer Learning Approach for Chemically Accurate Condensed-Phase Interatomic Potentials: Liquid Water as a Test Case

In our previous work [J. Chem. Theory Comput. 2026, 22(10), 5174−5184, 10.1021/acs.jctc.6c00403], we introduced an embedded correlated wavefunction transfer learning (ECW-TL) approach, which efficiently refines a machine-learned potential to correlated-wavefunction accuracy within a predefined region containing the chemistry of interest.

Although effective for localized processes such as ion pairing in solution, this formulation is not directly applicable to systems whose behavior and properties emerge collectively and cannot be confined to a selected region. Here, we overcome this limitation by developing a divide-and-conquer formulation of ECW-TL that partitions the system into embedded subsystems, enabling correlated-wavefunction corrections to be learned throughout the entire simulation cell.