Predictive discovery of sustainable biopolymers for circularity by design

Transitioning to a circular economy via biopolymer nanocomposites requires systemic change and a fundamental overhaul of material design processes. This shift demands consideration of expansive design variables and the simultaneous optimization of multiple properties, rendering one-factor-at-a-time workflows and trial-and-error strategies insufficient. Progress remains constrained by interrelated challenges spanning material selection, data consistency, design-space representation, multi-objective optimization, and circularity assessment. To address these challenges, we outline a transformative data-driven framework that integrates robotics-enabled high-throughput experimentation, artificial intelligence- and machine learning-driven predictive modeling, advanced simulation tools, and interoperable data infrastructure. This framework enables an accelerated discovery workflow that supports the construction of standardized experimental databases unifying formulation parameters, material characterization data, and life-cycle-assessment-derived environmental metrics. These databases enable the development of predictive discovery engines for accelerated, bidirectional, automated design of biopolymer nanocomposites. Furthermore, through an open-access data-sharing infrastructure, the workflow facilitates coordination across diverse stakeholders, promoting the widespread adoption of biopolymer nanocomposites.