Abstract
ABSTRACT
Charge‐aware molecular dynamics provides access to dynamically evolving effective atomic charges alongside conventional structural quantities. Here, we introduce a charge‐derived polarization fingerprint framework for characterizing local polarization‐active environments in using trajectories generated with a charge‐aware neuroevolution potential (‐NEP). Local environments are described using structural quantities, including Ti off‐centering and Ti–O bond asymmetry, together with charge‐derived descriptors such as the local dipole magnitude, charge asymmetry, and oxygen‐charge fluctuations. Correlation analysis, density mapping, event‐aligned trajectory analysis, and principal component analysis reveal coupled variations of structural and charge‐derived descriptors across the sampled local environments. In particular, oxygen‐charge fluctuations and local dipole magnitudes provide charge‐response signatures that complement geometric descriptors in distinguishing transient polarization‐active configurations. Independent /Bader calculations for representative configurations further show that the ‐NEP effective charges capture aspects of the configuration‐dependent charge response, while emphasizing that they should not be interpreted as direct equivalents of electronic‐structure partitioned charges. These results demonstrate that charge‐aware machine‐learning potentials can provide useful model‐derived descriptors for characterizing coupled structural and charge‐response behavior in complex materials.