Figure 1 (IMAGE)
Caption
This framework depicts a hierarchically integrated workflow that underpins data-driven polymer innovation. Starting from the Polymeric Materials Databases, which provide multiple types of data and regression models, which serve as decision engines to enable rapid structure-property prediction. MLIPs act as the physical foundation, bridging quantum and mesoscale material behaviors to expand cross-scale predictive capability. LLMs and intelligent agents further orchestrate scientific reasoning, inverse design, and full experimental workflows. The cycle is completed by AI-driven automated synthesis, where polymer candidates are synthesized and characterized, and real-time experimental data is fed back to update the databases and computational models. This iterative loop enables continuous self-optimization.
Credit
Hao Li et al.
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