聚合物信息学超级智能路线图
A roadmap for polymer informatics super-intelligence
- Matmerize, Inc.（Matmerize公司）
- School of Materials Science and Engineering, Georgia Institute of Technology（佐治亚理工学院材料科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出聚合物信息学超级智能的路线图，基于模块化智能体架构，实现从分子设计到产品性能的全链条自动化，并迈向闭环自主实验。
AI中文摘要：
聚合物信息学已从孤立的性能预测研究发展为一门集成学科，该学科在聚合物设计周期中将数据、模型和决策制定相结合。然而，它仍未能达到一个真正的智能系统，该系统能够按需进行逆向设计，在化学、加工和性能之间进行因果推理，并实现闭环自主实验。本文基于开发两个互补的智能体和信息学平台的经验，描绘了实现这一目标的路线图。这一愿景的核心是一个模块化的、智能体导向的架构，其中聚合物超级智能层以自然语言解读研究者的设计问题，并协调领域专业工具，根据可用数据，针对纯聚合物、复合材料和配方、溶剂以及合成和加工进行匹配。由此产生的系统涵盖了从分子设计到加工再到产品级性能和人类感知的完整链条。通过协同编排，其生成式设计、合成可行性推理和实用性评估已构成自动驾驶聚合物实验室的决策核心，而自主、闭环实验则是剩余的主要步骤。我们调查了这条路线图上的新兴能力，包括从文献中自动提取性能数据、化学感知表示、膜和可持续塑料的性能预测、溶解度和绿色溶剂推荐，以及计算机引导的逆合成规划，揭示了剩余的差距以及从当今编排的工具生态系统迈向真正超级智能的聚合物设计伙伴所需的研究和基础设施投资。
英文摘要：
Polymer informatics has matured from isolated property-prediction studies into an integrated discipline that couples data, models, and decision-making across the polymer design cycle. Yet it still falls short of a true intelligent system capable of inverse design on demand, causal reasoning across chemistry, processing, and performance, and closed-loop autonomous experimentation. This article traces a roadmap toward that goal, grounded in experience developing two complementary agentic and informatics platforms. Central to this vision is a modular, agent-directed architecture in which a polymer super-intelligence layer interprets a researcher's design question in natural language and coordinates domain-specialized tools, matched to the available data, for neat polymers, composites and formulations, solvents, and synthesis and processing. The resulting system spans the full chain from molecular design through processing to product-level performance and human perception. Orchestrated together, its generative design, synthesis-feasibility reasoning, and practicality assessment already form the decision-making core of a self-driving polymer laboratory, leaving autonomous, closed-loop experimentation as the principal step that remains. We survey emerging capabilities along this roadmap, including automated extraction of property data from the literature, chemistry-aware representation, property prediction for membranes and sustainable plastics, solubility and green-solvent recommendation, and computer-guided retrosynthetic planning, exposing the remaining gaps and the research and infrastructure investments needed to move from today's orchestrated tool ecosystem toward a genuinely super-intelligent polymer design partner.
