Machine Intelligence-Accelerated Discovery and Design of Sustainable Functional Materials

Abstract

Developing advanced sustainable materials – such as biodegradable plastic alternatives, high-performance biobased packaging, and multifunctional aerogels – is critical, yet remains hindered by slow trial-and-error methods and one-variable-at-a-time experimentation. Traditional approaches struggle to navigate large, multidimensional design spaces and meet complex, multi-objective performance targets. This dissertation introduces a data-driven, closed-loop framework that integrates collaborative robotics, machine learning prediction, and simulation tools to accelerate the discovery and optimization of sustainable materials with tailored functionality. The robotics/ML-integrated workflow is demonstrated across three distinct material systems. First, a machine intelligence–accelerated approach—combining collaborative robotics and ML prediction—enabled the discovery of all-natural plastic substitutes using four GRAS (Generally Recognized As Safe) components. The workflow, integrating active learning and predictive modeling, supported two-way design tasks by enabling both forward prediction and inverse design of biodegradable and biocompatible materials that match the optical, thermal, and mechanical properties of conventional plastics. Molecular dynamics simulations further revealed key strengthening mechanisms within the optimized formulations.

Second, the robotics/ML-integrated workflow is scaled to a broader formulation space of 23 natural/GRAS components to develop sustainable biobased packaging aimed at improving postharvest preservation. By integrating robotic automation, active learning, ML predictions, Density Functional Theory simulations, and life cycle assessment, the workflow enables the discovery of a library of biobased nanocomposites with enhanced mechanical resilience, tunable transparency, antimicrobial functionality, and environmentally benign compositions.

Third, the robotics/ML-integrated workflow incorporates generative modeling to further accelerate the development of mixed-dimensional aerogels with customizable structural and mechanical properties. Robotic automation of aerogel fabrication, mechanical testing, and SEM imaging is used to generate a high-quality dataset. Predictive models estimate key mechanical and microstructural features, while a diffusion-based generative model synthesizes realistic SEM-like images. These synthetic structures informed finite element simulations, enabling multi-physics performance evaluation and multi-objective property optimization.

Collectively, this work establishes a scalable and unconventional ML-driven materials development platform that significantly shortens the discovery-to-optimization cycle, paving the way for rapid innovation in sustainable functional materials.