Predicting the Solubility of Inorganic Ion Pairs in Water

Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali‐counterions can inform natural and synthetic aqueous processes. In the study of POMs ([Nb24O72H9]15−, Nb24) we discovered an unusual solubility trend (termed anomalous solubility) of alkali‐POMs, in which Nb24 is most soluble with the smallest (Li+) and largest (Rb/Cs+) alkalis, and least soluble with Na/K+. Via computation, we define a descriptor (σ‐profile) and use an artificial neural network (ANN) to predict all three described alkali‐anion solubility trends: amphoteric, normal (Li+>Na+>K+>Rb+>Cs+), and anomalous (Cs+>Rb+>K+>Na+>Li+). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution‐phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti6Nb14O54]14−, and provides guidelines to exploit alkali–POM interactions for new POMs discovery. Using alkali‐polyoxometalate salts, a previously‐undescribed solubility trend in polyoxometalate chemistry was discovered, termed anomalous solubility. Mapping charge‐density on inorganic polyatomic anions along with an artificial neural network accurately describes and predicts solubility trends of alkali‐anion pairs. Solubility experiments to validate computation lead to new POMs discovery.