Multicrossmodal Automated Agent for Integrating Diverse Materials Science Data

TL;DR

This paper presents a multicrossmodal LLM-agent framework that integrates diverse materials science data types, enabling unified reasoning and significantly improving data retrieval and analysis without retraining the core models.

Contribution

It introduces a novel coordinated multi-agent system with domain-adapted prompts and dynamic gating for cross-modal data integration in materials science.

Findings

Achieved 85% retrieval accuracy

Enhanced captioning fidelity

Increased integrated coverage by 35%

Abstract

We introduce a multicrossmodal LLM-agent framework motivated by the growing volume and diversity of materials-science data ranging from high-resolution microscopy and dynamic simulation videos to tabular experiment logs and sprawling literature archives. While recent AI efforts have accelerated individual tasks such as property prediction or image classification, they typically treat each modality in isolation, leaving rich cross-modal correlations unexplored and forcing researchers to perform laborious manual integration. Moreover, existing multimodal foundation models often require expensive retraining or fine-tuning on domain data, and current multi-agent systems in materials informatics address only narrow subtasks. To overcome these obstacles, we design a coordinated team of specialized LLM agents, each equipped with domain-adapted prompts and plugins that project their outputs into a shared embedding space. A dynamic gating mechanism then weights and merges these insights, enabling unified reasoning over heterogeneous inputs without ever modifying the underlying LLM weights. We validate our approach on challenging case studies and demonstrate substantial gains in retrieval accuracy (85%), captioning fidelity, and integrated coverage (35%) compared to single-modality and zero-shot baselines. Our work paves the way for AI digital researchers capable of bridging data silos and accelerating the materials-discovery cycle. The code is available at https://github.com/adibgpt/Multicrossmodal-Autonomous-Materials-Science-Agent.