This paper presents a method for the systematic and automated design of flexible organic linkers for construction of metal organic-frameworks (MOFs) in which flexibility, compliance, or other mechanically exotic properties originate at the linker level rather than from the framework kinematics. Our method couples a graph grammar method for systematically generating linker like molecules with molecular dynamics modeling of linkers' mechanical response. Using this approach we have generated a candidate pool of >59,000 hypothetical linkers. We screen linker candidates according to their mechanical behaviors under large deformation, and extract fragments common to the most performant candidate materials. To demonstrate the general approach to MOF design we apply our system to designing linkers for pressure switching MOFs-MOFs that undergo reversible structural collapse after a stress threshold is exceeded.

Metal Organic Responsive Frameworks (MORFs) are a proposed new class of smart materials consisting of a Metal Organic Framework (MOF) with photoisomerizing beams (also known as linkers) that fold in response to light. Within a device these new light responsive materials could provide the capabilities such as photo-actuation, photo-tunable rigidity, and photo-tunable porosity. However, conventional MOF architectures are too rigid to allow isomerization of photoactive sub-molecules. We propose a new computational approach for designing MOF linkers to have the required mechanical properties to allow the photoisomer to fold by borrowing concepts from de novo molecular design and graph synthesis. Here we show how this approach can be used to design compliant linkers with the necessary flexibility to be actuated by photoisomerization and used to design MORFs with desired functionality.

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.

Current computational Design Synthesis approaches have had trouble generating components with higher kinematic pairs and have instead relied on libraries of predefined components. However, higher kinematic pairs are ubiquitous in many mechanical devices such as ratchets, latches, locks, trigger mechanisms, clock escapements, and materials handling systems. In many cases there is a need to synthesize new higher kinematic pair devices. To address this problem, we develop a new representation for mechanical systems that extends the capabilities of configuration spaces to consider arbitrary energy storing mechanical devices. The key idea underlying this representation is the use of potential energy surfaces as a generalization of configuration spaces. This generalization enables modelling of mechanical systems in a physics independent manner and captures behaviors such as dynamics. By modeling a device through the lens of a potential energy surface, we demonstrate that differentiable simulation is possible. Differentiable simulation enables efficient calculation of gradients of potential energy surface parameters with respect to an objective function that depends on trajectories taken on the potential energy surface. This allows synthesis of mechanical devices with desired kinematic and dynamic behavior through gradient descent. We demonstrate this through several synthesis examples including positioning devices (e.g., a funnel) and timing devices (e.g., an oscillator).