ArticleAdvanced materials (Deerfield Beach, Fla.)2025
SciAgents: Automating Scientific Discovery Through Bioinspired Multi-Agent Intelligent Graph Reasoning.
Article in Advanced materials (Deerfield Beach, Fla.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Unifying AI-assisted scientific discovery around exploration, hypothesis generation, and testing.Patterns (New York, N.Y.) · 2026Review
- MR-MAS: an end-to-end multi-agent system for autonomous Mendelian randomization analysis.Bioinformatics (Oxford, England) · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- TeLLAgent: a dual-agent framework for reliable scientific discovery with tool-enhanced LLMs.Chemical science · 2026Article
- Practical Use of Advanced AI Frameworks on Real-Life Scientific Problems: Three Case Studies.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking and behavioral characterization of LLM agents for protein design.bioRxiv : the preprint server for biology · 2026Article
- AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation.Scientific reports · 2026Article
- Review
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.Journal of chemical information and modeling · 2026Article
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- Agentic Finance: An Adaptive Inference Framework for Bounded-Rational Investing Agents.Entropy (Basel, Switzerland) · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Recent Advances in Integrating Graphene into Polymeric Nanocomposite Hydrogels for Biomedical Applications.Macromolecular bioscience · 2026Review
- Accelerating primer design for amplicon sequencing using large language model-powered agents.Nature biomedical engineering · 2026Article
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- What are the limits to biomedical research acceleration through general-purpose AI?Scientific reports · 2026Review
- LLM-driven design of physics-constrained constitutive models: Two agents are better than one.Machine learning for computational science and engineering · 2026Article
- A Process-Centric Survey of AI for Scientific Discovery Through the EXHYTE Framework.Research square · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.