OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials

2026-03-17

AI Summary

This work addresses the challenge of integrating high-precision detection and dynamic physical reasoning in the pipeline from optical identification to device fabrication of two-dimensional quantum materials, where existing approaches suffer from verbose outputs and poor interactivity. The authors propose an agent-based collaborative system centered on a large language model that orchestrates multimodal domain-expert models—specifically QuPAINT—to decouple visual recognition from physical reasoning while enabling natural language interaction and context-aware analysis. The system incorporates a persistent memory mechanism to store scale calibration and fabrication protocols, and leverages the lightweight NanoBot framework with spatial data dynamic querying to perform scale-aware computations, generate standalone visual annotations, and seamlessly integrate into laboratory environments. This approach significantly enhances throughput in device fabrication and improves the practicality of scientific interaction.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The transition from optical identification of 2D quantum materials to practical device fabrication requires dynamic reasoning beyond the detection accuracy. While recent domain-specific Multimodal Large Language Models (MLLMs) successfully ground visual features using physics-informed reasoning, their outputs are optimized for step-by-step cognitive transparency. This yields verbose candidate enumerations followed by dense reasoning that, while accurate, may induce cognitive overload and lack immediate utility for real-world interaction with researchers. To address this challenge, we introduce OpenQlaw, an agentic orchestration system for analyzing 2D materials. The architecture is built upon NanoBot, a lightweight agentic framework inspired by OpenClaw, and QuPAINT, one of the first Physics-Aware Instruction Multi-modal platforms for Quantum Material Discovery. This allows accessibility to the lab floor via a variety of messaging channels. OpenQlaw allows the core Large Language Model (LLM) agent to orchestrate a domain-expert MLLM,with QuPAINT, as a specialized node, successfully decoupling visual identification from reasoning and deterministic image rendering. By parsing spatial data from the expert, the agent can dynamically process user queries, such as performing scale-aware physical computation or generating isolated visual annotations, and answer in a naturalistic manner. Crucially, the system features a persistent memory that enables the agent to save physical scale ratios (e.g., 1 pixel = 0.25 μm) for area computations and store sample preparation methods for efficacy comparison. The application of an agentic architecture, together with the extension that uses the core agent as an orchestrator for domain-specific experts, transforms isolated inferences into a context-aware assistant capable of accelerating high-throughput device fabrication.

Problem

• 2D quantum materials
• Multimodal Large Language Models
• cognitive overload
• real-world interaction
• device fabrication

Innovation

• Agentic AI
• Multimodal LLM
• Physics-Aware Reasoning
• Persistent Memory
• 2D Quantum Materials