MIT researchers have developed a novel framework that uses artificial intelligence to autonomously generate and evaluate promising research hypotheses across various scientific fields. This human-AI collaboration framework could help researchers, particularly new Ph.D. candidates, streamline the process of hypothesis development.

In their recent paper published in Advanced Materials, the team describes how their system, known as SciAgents, helped generate evidence-driven hypotheses in the field of biologically inspired materials. The study was co-authored by Alireza Ghafarollahi, a postdoc at the Laboratory for Atomistic and Molecular Mechanics (LAMM), and Markus Buehler, a professor at MIT's departments of Civil and Environmental Engineering and Mechanical Engineering.

SciAgents operates using a multi-agent approach, with each AI agent tasked with specific capabilities and access to data. These agents leverage "graph reasoning" methods, where AI models utilize a knowledge graph to define relationships between various scientific concepts. This multi-agent approach mimics the way biological systems organize themselves into interconnected groups of building blocks.

Buehler explains that this "divide and conquer" approach, seen in nature from insect swarms to human civilizations, allows for a greater collective intelligence than that of any individual. The aim of their work is to simulate the scientific discovery process, accelerating the exploration of new ideas through AI.

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While recent advancements in large language models (LLMs) have shown success in answering questions and summarizing information, they have limitations when it comes to generating original ideas. To overcome this, the researchers created a system capable of conducting a multistep process that extrapolates new knowledge, moving beyond simple information recall.

The foundation of their system is an ontological knowledge graph, which links diverse scientific concepts. This graph is created by feeding a set of scientific papers into a generative AI model, which then structures them into meaningful relationships. In their previous work, Buehler used category theory to help the AI model generate these graphs, allowing for more generalizable scientific concepts.

By focusing AI models on thinking in a principled manner, the researchers hope to explore more creative uses of AI in science. For this paper, the team used about 1,000 studies on biological materials, but they believe the knowledge graph approach can be applied across any scientific field with varying amounts of data.

Once the graph was established, the team created an AI system for scientific discovery, with specialized models to play specific roles within the system. The components were based on OpenAI's ChatGPT-4 series models and used in-context learning, enabling the system to learn from data provided while understanding its role in the broader framework.