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 ([Nb24 O72 H9 ]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 [Ti6 Nb14 O54 ]14- , and provides guidelines to exploit alkali-POM interactions for new POMs discovery.
Keywords: Ion-Pairing; Machine Learning; Polyoxometalate; Polyoxoniobate; SAXS; Solubility.
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