Ghafarollahi Alireza, Buehler Markus J
Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
Laboratory for Atomistic and Molecular Mechanics (LAMM), Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
Adv Mater. 2025 Jun;37(22):e2413523. doi: 10.1002/adma.202413523. Epub 2024 Dec 18.
A key challenge in artificial intelligence (AI) is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, SciAgents, an approach that leverages three core concepts is presented: (1) large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses human research methods. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular fashion, the system yields material discoveries, critiques and improves existing hypotheses, retrieves up-to-date data about existing research, and highlights strengths and limitations. This is achieved by harnessing a "swarm of intelligence" similar to biological systems, providing new avenues for discovery. How this model accelerates the development of advanced materials by unlocking Nature's design principles, resulting in a new biocomposite with enhanced mechanical properties and improved sustainability through energy-efficient production is shown.
人工智能(AI)面临的一个关键挑战是创建能够通过探索新领域、识别复杂模式以及在海量科学数据中发现前所未见的联系来自主推进科学理解的系统。在这项工作中,提出了一种名为SciAgents的方法,该方法利用了三个核心概念:(1)大规模本体知识图谱,用于组织和互连各种科学概念;(2)一套大语言模型(LLMs)和数据检索工具;(3)具有原位学习能力的多智能体系统。应用于受生物启发的材料时,SciAgents揭示了以前被认为不相关的隐藏跨学科关系,实现了超越人类研究方法的规模、精度和探索能力。该框架自主生成并完善研究假设,阐明潜在机制、设计原则和意外的材料特性。通过以模块化方式整合这些能力,该系统产生材料发现、批判并改进现有假设、检索有关现有研究的最新数据,并突出优势和局限性。这是通过利用类似于生物系统的“群体智能”来实现的,为发现提供了新途径。展示了该模型如何通过揭示自然的设计原则来加速先进材料的开发,从而通过节能生产产生一种具有增强机械性能和更高可持续性的新型生物复合材料。