fig6

Figure 6. Diversity and application of constant-potential NNPs. (A) The active learning workflow for training a constant potential reactor NNP, which iteratively refines the potential by sampling high-uncertainty configurations. Reproduced from reference^[89] under CC BY-NC-ND 4.0 License; (B) The potential-dependent free energy profiles for CO₂ adsorption at Au-water interfaces, as revealed by constant-potential MD simulations. Reproduced from reference^[89] under CC BY-NC-ND 4.0 License; (C) PiNNwall framework coupling machine-learned charge-response kernels and base charges with constant-potential molecular dynamics for heterogeneous electrode-electrolyte interfaces. Reproduced from reference^[90] under CC BY 4.0 License; (D) RAZOR framework and RAZOR-MLIP simulations of OH-covered Cu (100), illustrating charge-induced adsorption-site switching and the associated work-function response. Adapted from reference^[91] under CC BY 4.0 License. MLIP: Machine-learning interatomic potential; DFT: density functional theory; MD: molecular dynamics; RAZOR: Response Analysis in z-Orientation.