2026-10-13T11:05:17Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34027373 2026-09-29T19:03:33Z category_1 category_146 category_21 category_873 category_734 category_931 portal_63 item_type_6 month_year_09_2026

A Divide-and-Conquer Embedded Correlated Wavefunction Transfer Learning Approach for Chemically Accurate Condensed-Phase Interatomic Potentials: Liquid Water as a Test Case Xuezhi Bian (12219568) Emily A. Carter (1283184) Biophysics Space Science Biotechnology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified underlying dft models entire simulation cell collectively fluctuating hydrogen systems whose behavior resulting potential fine properties emerge collectively predefined region containing chemically accurate condensed phase interatomic potentials test case combining liquid water relative wavefunction accuracy within correlated wavefunction theory test case liquid water phase systems wavefunction corrections theory comput selected region learned potential dynamical properties complex condensed phase dynamics results suggest previous work localized processes level improves learned throughout ion pairing enabling correlated efficiently refines directly applicable bonding environments although effective 6c00403 ], Combining the accuracy of correlated wavefunction theory with the efficiency of machine-learned interatomic potentials offers a promising route toward predictive condensed-phase dynamics. 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. We demonstrate the method using ambient liquid water as a test case because of its diverse, collectively fluctuating hydrogen-bonding environments. The resulting potential fine-tuned at the embedded-CCSD(T) level improves the structural, thermodynamic, and dynamical properties of liquid water relative to underlying DFT models, bringing calculated observables closer to experiment. These results suggest that divide-and-conquer ECW-TL provides a promising, scalable route toward accurate simulations of complex condensed-phase systems. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acs.jpclett.6c02897.s001 https://figshare.com/articles/journal_contribution/A_Divide-and-Conquer_Embedded_Correlated_Wavefunction_Transfer_Learning_Approach_for_Chemically_Accurate_Condensed-Phase_Interatomic_Potentials_Liquid_Water_as_a_Test_Case/34027373 CC BY-NC 4.0