Abstract: We will discuss efforts to develop generative machine learning approaches that can predict properties from structural information, but more importantly can also tackle the ‘inverse problem’ deducing structural information given desired properties. To do this, we need to develop information-rich encoding decoding techniques for three-dimensional and hierarchical structures. Our efforts are centered around marginalized graph kernel approaches and autoencoders, and utilizing tensor field networks to discover new scientific knowledge about structure-function relationships in chemical sciences.
