Accelerating material discovery and design through integrated robotics and machine intelligence

Abstract

Conventional material discovery and design is labor-, time-, and cost-intensive. To address these challenges, this work presents a robotics- and machine learning-integrated workflow designed to accelerate the material design process, maximize experimental throughput, minimize time and labor expenses, and lead to improved outcomes. This multi-stage framework replaces labor-intensive wet lab experiments with collaborative robots that can autonomously perform sample fabrication and testing, utilizes a feasibility constrained design space to minimize experimental failures, replaces inefficient trial-and-error cycles with active learning loops to efficiently navigate complex design spaces, and incorporates virtual synthesis and screening to effectively manage multi-objective optimization tasks, enabling tunable design requirements. This framework is enabled by the development and integration of autonomous robotic platforms for high-throughput sample preparation and characterization. The efficacy of this integrated approach is demonstrated through two distinct projects titled the predictive and generative modeling of mixed-dimensional aerogels with programmable properties, and the predictive design of sustainable biobased packaging for improved postharvest preparation. This research highlights the transformative potential of combining robotics and machine intelligence to significantly accelerate the discovery and design of advanced materials with tailored property requirements.