Building upon the groundwork laid by NAISS 2023/22-477, our research aims to delve deeper into the intricate relationship between optically relevant properties (such as dipoles and polarizabilities) and the structural dynamics of condensed materials. We are particularly intrigued by the behavior of ionic systems dissolved in water. It has been experimentally measure that the conductivity of certain ions in water changes when optical fields couple to the system [https://doi.org/10.1039/D3SC03364C]. Predicting such phenomena presents unique challenges due to the extreme anisotropy, local order, long-range disorder, and highly diffusive motion -- requiring the simulation of large numbers of atoms (large supercells). Predicting the optical characteristics of such systems is traditionally arduous and resource-intensive when relying solely on established density functional theory (DFT) methodologies. We anticipate that a well-trained Tensorial Neuro-Evolution Potential (TNEP) could resolve this challenge and deliver insight about the microscopic mechanism. In this project, we utilize a machine learning approach based on Tensorial Neuro-Evolution Potential (TNEP), a type of neural network-based machine learning model, to learn and predict the dipole moments and polarizability of a ions dissolved in water. In combination with nuclear forces obtained via the same NEP approach, it provides a way to estimate the interplay between ionic conductivity and optical characteristic. The success of NAISS 2023/22-477 illustrated the strength of the TNEP approach. We will first collect the necessary data for training the TNEP models. Large chunks of this data can be reused from previous publications in which supercells of water have been calculated. We then extend this dataset using the VASP code and modern theory of polarization. We then train the TNEP models using an active learning approach, i.e., continuous inference combined with additional data acquisition and retraining. In conclusion, the project requires TNEP models for large ion-water systems and an active learning approach (iterative evaluation and retraining of the TNEP models). More resources are required to train meaningful models as the underlying system has highly an-isotropic and multiple species of ions should be considered.