Using Molecular Fingerprinting to Infer Functional Similarity in Engineered Systems

Abstract

Design of new and advanced materials with shape-shifting or origami-like capabilities is an area that bears a strong similarity to the design of electromechanical products yet has not leveraged such systematic approaches. In this paper, computational methods to design Metal Organic Responsive Frameworks (MORFs) — which are a theoretical type of material that can change their shape and porosity in response to light — are investigated. However, it is a significant challenge to computationally identify MORFs that are both feasible and useful, i.e., systemic invention (as opposed to discovery) of new MORFs. The proposed framework utilizes the typical product design process to iteratively generate new candidates, evaluate their properties, and then guide the generation of the next set of candidates. A materials designer could then leverage this knowledge to generate structures or substructures with specific functional goals in mind. In this paper an approach to inferring functional similarity of systems using structural information — based on both drug design and database-driven product design — is evaluated. The results demonstrate an observable correlation between structural fingerprints of electromechanical products and electromechanical function. This evidence, combined with the well-established similar property principle in drug design, supports the usage of molecular fingerprinting for providing high-level functional guidance in a MORF design framework based on purely structural information.