Functional, topologically complex organic molecules are rising stars in modern materials science due to their biocompatibility and wealth of physico-chemical properties. Their practical applications often involve interactions with small molecular targets (e.g. gases, environmental pollutants, and drugs) via relatively weak non-covalent forces. Moreover, functional organic materials of today typically possess diverse and complex topologies, made possible by the nearly infinite structural variability of organic chemical motifs. This simultaneously presents a great benefit and an enormous challenge for finding an optimal host for a specific molecular guest in conjunction with a targeted practical use. Serendipitous trial-and-error experimentation becomes prohibitively expensive, prompting the need for theoretical insights to guide rational design and in silico testing of candidate materials. These theoretical insights can be obtained by elucidating and rationalising the pertinent structure-property relationships, for which the structures can be encoded by machine-readable representations, and properties – evaluated by high-throughput workflows or predicted by artificial intelligence. However, existing chemical representations generally reduce the dimensionality of atomic composition and connectivity but do not capture the intricacies of shape and topology, while evaluating materials properties comes at a high computational cost and requires careful benchmarking of the methodology.
The overarching aim of PATTERNCHEM is to enable the rational design and facile pre-screening of functional organic materials for applications involving their non-covalent interactions with molecular targets in silico. Several families of functional organic materials – graphene derivatives, covalent-organic frameworks, and hyperbranched polymers – provide a unique foundation for developing application-oriented fingerprints of their topological and non-covalent interaction features. After elucidating diverse structural descriptors of atomistic arrangement, substitution patterns, and two- and three-dimensional shapes of these materials, we will establish a scheme for quantifying the propensity for non-covalent interactions and assessing the host-guest complementarity. Using this scheme, chemical and physical performance indicators relevant to targeted applications (e.g. as sensors, filters, and nanocarriers) can be computed. Finally, structure-property relationships between computed performance indicators and developed descriptors will be established and implemented into predictive frameworks for functional organic materials. The key deliverable of PATTERNCHEM will be a unified, all-encompassing framework for designing new candidate architectures and evaluating host-guest complementarity, which will require only basic structural information as an input and will predict the ultimate performance in the targeted application as its output.
