Open access
Jul 2026
Incorporating a neural network into the AMOEBA polarizable force field for ligand field effects of Cu2+ ions
AMOEBA + NN, a machine-learning-augmented polarizable potential, is presented, to investigate Cu2+ solvation in NH3 and H2O and mixed environments and provides a unified, computationally efficient framework for describing the structural, dynamic, and thermodynamic observables of transition metal coordination.
Zhecheng He, Yanxing Wang, Shubham Chatterjee et al.
· Chemical Science · 0 citations