biostudies-literatureZhang CU.S. Department of EnergyU.S. Department of Energy (DOE)DOE | SC | Oak Ridge National LaboratoryNational Energy Research Scientific Computing CenterNational Energy Research Scientific Computing Center (NERSC)DOE | SC | Oak Ridge National Laboratory (ORNL)e2505929122https://www.ebi.ac.uk/biostudies/studies/S-EPMC12403145biostudies-literatureUnknown122(34)Understanding how electric fields influence water dissociation at heterogeneous interfaces is crucial for controlling interfacial chemical reactions and advancing next-generation energy technologies. Herein, ab initio-based machine learning simulations show that even small electric field changes can significantly alter the water dissociation fraction at planar TiO₂-electrolyte interfaces. The resulting free energy difference between undissociated and dissociated interfacial water exhibits a linear dependence on the field change with a slope of 1.97 <i>e</i>Å, which far exceeds the dissociation-induced dipole change of a water molecule. Employing a machine-learned collective variable to investigate the reaction statistics of thousands of water dissociation/recombination events, wProceedings of the National Academy of Sciences of the United States of AmericaTuning water dissociation at oxide-electrolyte interfaces with electric fields.PMC12403145DE-AC05-00OR22725DE-AC02-05CH11231DE-SC0019394Car RSelloni AZhang CYu ZfalseTuning water dissociation at oxide-electrolyte interfaces with electric fields.Understanding how electric fields influence water dissociation at heterogeneous interfaces is crucial for controlling interfacial chemical reactions and advancing next-generation energy technologies. Herein, ab initio-based machine learning simulations show that even small electric field changes can significantly alter the water dissociation fraction at planar TiO₂-electrolyte interfaces. The resulting free energy difference between undissociated and dissociated interfacial water exhibits a linear dependence on the field change with a slope of 1.97 <i>e</i>Å, which far exceeds the dissociation-induced dipole change of a water molecule. Employing a machine-learned collective variable to investigate the reaction statistics of thousands of water dissociation/recombination events, w2025-01-01T00:00:00Z2025 Aug2026-07-16T09:22:49.446Z2026-07-09T10:46:30.051ZS-EPMC124031454083341610.1073/pnas.2505929122