For all the collected CG-MD trajectories, we computed the SOAP vectors of every citrate ion with a sampling stride of 50 ns, generating a comprehensive dataset of SOAP information associated to the citrates at different temperature (T = 300 and 333 K) and electrostatic drive (E ranging from 0.005 Vnm -1 to 0.11 Vnm -1 ) conditions. To rationalize this high-dimensional dataset, we then applied a clustering algorithm that identifies the most probable molecular states of the anions, allowing the characterization of their structure and dynamics. The dimensionality of the database was first reduced by means of Principal Component Analysis (PCA), retaining only the first three principal components. In this way, we obtained a less demanding data treatment with a contained loss of accuracy. PCA was performed using the Python package Scikit-Learn. We then processed the resulting dataset of three-dimensional descriptors by means of a density-base unsupervised clustering scheme named Probabilistic Analysis of Molecular Motif (PAMM). This step allowed to classify all the sampled configurations of the citrates within the crystal, according to a set of molecular motifs, i.e., the most probable structural arrangements. The identification and definition of such molecular motifs is based completely on the dataset of collected system conformations.