I-Corps: AI for predicting polymer properties for biopolymer films

The broader impact/commercial potential of this I-Corps project is the development of a software platform to predict the properties of renewable materials such as biopolymers. Amid rising environmental concerns, e-commerce leaders such as Amazon, Walmart, and H&M have committed to reducing plastic waste and carbon emissions by 2030. Achieving this goal may depend on sustainable packaging solutions that prioritize biodegradability over investment in recycling processes. In addition, cost considerations necessitate a focus on cost-effective production of biopolymers for packaging. The proposed technology provides a tool enabling the customization of the chemical behavior of biopolymers that may be used to create the technical performance specifications required for their applications. The proposed software platform may have the potential to impact multiple industries by enabling the discovery and prediction of renewable materials' properties including consumer electronics, energy storage, biofuels, food extraction, and pharmaceutical research. This I-Corps project is based on the development of simulator tailored to maximize sampling control, efficiency, and scalability in molecular dynamics simulations. The proposed model employs reinforcement learning control policies as an inductive bias, optimizing objective functions like generating specific conformations, which minimizes computational cost. Applied to the realm of polymer chains, the proposed technology has shown a 40% improvement in sampling efficiency over studied chemistry polymer chain conformations with a target radius of gyration, illustrating the power of machine learning control policies in optimizing complex simulations. The initial goal is to apply this model to packaging films made from biopolymers. Unlike similar technologies for property prediction, this model is physics-based, which means empirical data are not required, and it runs in conventional computers, making it accessible to a wider audience. RL-based control policies will be leveraged to explore the configuration space thoroughly to enhance control over the simulation's conformations for mechanical property prediction, which is a key aspect in designing effective packaging materials. The proposed technology may be used to predict and adjust specific properties of biopolymers, such as mechanical strength, flexibility, or chemical non-reactivity that are crucial for creating tailored packaging solutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.