A Universal Framework for General Prediction of Physicochemical Properties: The Natural Growth Model

Abstract

To precisely and reasonably describe the contribution of interatomic and intermolecular interactions to the physicochemical properties of complex systems, a chemical message passing strategy as driven by graph neural network is proposed. Thus, by distinguishing inherent and environmental features of atoms, as well as proper delivering of these messages upon growth of systems from atoms to bulk level, the evolution of system features affords eventually the target properties like the adsorption wavelength, emission wavelength, solubility, photoluminescence quantum yield, ionization energy, and lipophilicity. Considering that such a model combines chemical principles and natural behavior of atom aggregation crossing multiple scales, most likely, it will be proven to be rational and efficient for more general aims in dealing with complex systems.