用AI多智能体系统实现高通量聚合物设计与性质预测，精度高且成本低。
Autonomous Multi-Agent AI for High-Throughput Polymer Informatics: From Property Prediction to Generative Design Across Synthetic and Bio-Polymers
- 集成大模型与计算工具的多智能体协同工作框架
- 对1251种聚合物预测准确率达R²=0.89（Tg）至0.91（密度）
- 可自监控优化，适合材料研发与自动化实验设计
我们提出一个整合多智能体AI的聚合物发现生态系统，统一高通量材料流程、人工智能与计算建模，形成单一聚合物研究生命周期（PRL）管道。该系统通过先进大语言模型（DeepSeek-V2和DeepSeek-Coder）驱动专业智能体，实现科学资源检索与推理、外部工具调用、领域代码执行及元认知自我评估，保障端到端任务鲁棒性。验证了三项能力：高保真聚合物性质预测与生成设计流水线、生物聚合物结构表征的全自动多模态工作流，以及可动态优化策略的元认知智能体框架。在包含1251种聚合物的独立测试集上，PolyGNN智能体对玻璃化转变温度（Tg）预测达到R²=0.89，拉伸强度为R²=0.82，断裂伸长率为R²=0.75，密度为R²=0.91。系统通过多智能体共识提供不确定性估计，计算复杂度线性增长，支持至少10,000种聚合物的高通量筛选，单次推理仅需16.3秒、约2GB内存与0.1 GPU小时，成本约0.08美元。在专用Tg基准测试中，本方法达R²=0.78，优于单大模型（R²=0.67）、基团贡献法（R²=0.71）和ChemCrow（R²=0.66）。此外，在聚苯乙烯案例研究中，系统不仅生成科学输出，还通过战术、战略与元战略级自我评估持续优化自身行为。
原文摘要 · Abstract (English)
We present an integrated multiagent AI ecosystem for polymer discovery that unifies high-throughput materials workflows, artificial intelligence, and computational modeling within a single Polymer Research Lifecycle (PRL) pipeline. The system orchestrates specialized agents powered by state-of-the-art large language models (DeepSeek-V2 and DeepSeek-Coder) to retrieve and reason over scientific resources, invoke external tools, execute domain-specific code, and perform metacognitive self-assessment for robust end-to-end task execution. We demonstrate three practical capabilities: a high-fidelity polymer property prediction and generative design pipeline, a fully automated multimodal workflow for biopolymer structure characterization, and a metacognitive agent framework that can monitor performance and improve execution strategies over time. On a held-out test set of 1,251 polymers, our PolyGNN agent achieves strong predictive accuracy, reaching R2 = 0.89 for glass-transition temperature (Tg ), R2 = 0.82 for tensile strength, R2 = 0.75 for elongation, and R2 = 0.91 for density. The framework also provides uncertainty estimates via multiagent consensus and scales with linear complexity to at least 10,000 polymers, enabling high-throughput screening at low computational cost. For a representative workload, the system completes inference in 16.3 s using about 2 GB of memory and 0.1 GPU hours, at an estimated cost of about $0.08. On a dedicated Tg benchmark, our approach attains R2 = 0.78, outperforming strong baselines including single-LLM prediction (R2 = 0.67), group-contribution methods (R2 = 0.71), and ChemCrow (R2 = 0.66). We further demonstrate metacognitive control in a polystyrene case study, where the system not only produces domain-level scientific outputs but continually monitors and optimizes its own behavior through tactical, strategic, and meta-strategic self-assessment.
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