From fragmented data to actionable design: Physics-calibrated learning for plastic upcycling

• URL: http://arxiv.org/abs/2608.02402v1
• Date: Mon, 03 Aug 2026 15:47:49 GMT
• Title: From fragmented data to actionable design: Physics-calibrated learning for plastic upcycling
• Abstract summary: The PC-MG-MoE framework converts structured missingness into an informative learning signal.<n>It learns directly from partially observed experiments without target imputation.<n>It provides interpretable model behaviour rather than black-box prediction alone.
• Score: 3.859212984948451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thermochemical upgrading of plastic waste is a key upcycling pathway, yet the experimental literature is fragmented by heterogeneous conditions and incomplete reporting. Complete-case learning would retain only 10.99% of the curated experiments, while target imputation can introduce biased supervision. Here we develop a Physics-Calibrated, Missingness-Gated, and Load-Balanced Mixture-of-Experts (PC-MG-MoE) framework that converts structured missingness into an informative learning signal. PC-MG-MoE learns directly from partially observed experiments without target imputation, reconstructs physically consistent product distributions, accommodates cross-laboratory heterogeneity, and provides interpretable model behaviour rather than black-box prediction alone. Under stringent source-grouped validation, it achieved the lowest aggregate absolute error among the evaluated models, supporting engineering screening under cross-laboratory heterogeneity. Wet-lab experiments provide an external comparison, showing key composition-dependent trends. Implemented as an interactive web-based workflow, PC-MG-MoE enables forward screening, physics-grounded constrained inverse design, targeted experimental planning that supports reduced experimental workload and trial-and-error, and laboratory-specific adaptation with new platform-specific data. This work establishes a transferable framework for converting fragmented literature data into experimentally actionable guidance for model-guided plastic upcycling and broader thermochemical systems.

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