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

AI 中文解读

塑料垃圾回收一直是难题，但科学家最近搞了个“智能军师”。这项新研究开发了一套名为PC-MG-MoE的AI系统，能巧妙处理实验数据不完整、标准不统一的老大难问题。过去，面对乱七八糟的科研记录，AI要么删掉大部分数据，要么瞎猜补全，导致结论不可靠。新系统却能把“数据缺失”本身当成线索，从大量零散实验中直接学习，还能给出物理上说得通的预测结果，而不是纯粹瞎蒙。实测下来，它预测塑料转化效果的准确性在各类模型中最高，还通过了跨实验室的严格检验。最实用的是，这套系统已经做成了网页工具，科研人员可以输入自己的条件，让它推荐最佳反应参数，做实验前先“模拟跑一遍”，省时省料少走弯路。对普通人来说，这意味着未来塑料瓶、塑料袋可能更高效地变成燃料或新材料，垃圾不再是垃圾，而是可以循环利用的资源。这项AI技术不仅助力塑料升级，也为其他化学领域的文献挖掘和数据利用提供了新思路。

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.