TopoMAS: Large Language Model Driven Topological Materials Multiagent System

• URL: http://arxiv.org/abs/2507.04053v1
• Date: Sat, 05 Jul 2025 14:23:12 GMT
• Title: TopoMAS: Large Language Model Driven Topological Materials Multiagent System
• Authors: Baohua Zhang, Xin Li, Huangchao Xu, Zhong Jin, Quansheng Wu, Ce Li,
• Abstract summary: TopoMAS is an interactive human-AI framework that seamlessly orchestrates the materials-discovery pipeline.<n>TopoMAS closes the loop by autonomously integrating computational outcomes into a dynamic knowledge graph.<n>It has already guided the identification of novel topological phases SrSbO3.
• Score: 9.394163894876689
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Topological materials occupy a frontier in condensed-matter physics thanks to their remarkable electronic and quantum properties, yet their cross-scale design remains bottlenecked by inefficient discovery workflows. Here, we introduce TopoMAS (Topological materials Multi-Agent System), an interactive human-AI framework that seamlessly orchestrates the entire materials-discovery pipeline: from user-defined queries and multi-source data retrieval, through theoretical inference and crystal-structure generation, to first-principles validation. Crucially, TopoMAS closes the loop by autonomously integrating computational outcomes into a dynamic knowledge graph, enabling continuous knowledge refinement. In collaboration with human experts, it has already guided the identification of novel topological phases SrSbO3, confirmed by first-principles calculations. Comprehensive benchmarks demonstrate robust adaptability across base Large Language Model, with the lightweight Qwen2.5-72B model achieving 94.55% accuracy while consuming only 74.3-78.4% of tokens required by Qwen3-235B and 83.0% of DeepSeek-V3's usage--delivering responses twice as fast as Qwen3-235B. This efficiency establishes TopoMAS as an accelerator for computation-driven discovery pipelines. By harmonizing rational agent orchestration with a self-evolving knowledge graph, our framework not only delivers immediate advances in topological materials but also establishes a transferable, extensible paradigm for materials-science domain.

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