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

• URL: http://arxiv.org/abs/2603.17043v1
• Date: Tue, 17 Mar 2026 18:25:37 GMT
• Title: OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials
• Abstract summary: We introduce OpenQlaw, an agentic orchestration system for analyzing 2D materials.<n>The architecture is built upon NanoBot and QuPAINT, one of the first Physics-Aware Instruction Multi-modal platforms for Quantum Material Discovery.
• Score: 12.888415301529891
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• An LLM System for Autonomous Variational Quantum Circuit Design [0.0]
  We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs.<n>We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry.
  arXiv  Detail & Related papers  (2026-06-11T14:08:00Z)
• Experiments in Agentic AI for Science [0.005439329219803859]
  This paper details two novel frameworks for developing autonomous, agentic AI in scientific research.<n>The first agent, DeepTS/DeepCollector, automates the large-scale, extraction, and deduplication of time-series datasets.<n>The second, DeepScribe, is an autonomous presentation that converts visually dense, mathematically complex physics lectures into structured scientific reports.
  arXiv  Detail & Related papers  (2026-05-25T19:57:57Z)
• Automating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories: A Framework for Multi-Agent Procedural Knowledge Extraction [20.467661664708086]
  This report investigates a framework for automated acquisition of high-quality agent skills through mining of open-source repositories.<n>We focus on the extraction of visualization and educational capabilities from state-of-the-art systems including TheoremExplainAgent and Code2Video.<n>We demonstrate that systematic extraction from agentic repositories, combined with rigorous security governance and multi-dimensional evaluation metrics, enables scalable acquisition of procedural knowledge.
  arXiv  Detail & Related papers  (2026-03-12T11:10:13Z)
• Exploring Physical Intelligence Emergence via Omni-Modal Architecture and Physical Data Engine [50.62040226184694]
  We present OmniFysics, a compact omni-modal model that unifies understanding across images, audio, video, and text.<n>To inject explicit physical knowledge, we build a physical data engine with two components.<n>Experiments show competitive performance on standard multimodal benchmarks and improved results on physics-oriented evaluations.
  arXiv  Detail & Related papers  (2026-02-05T14:04:51Z)
• An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
  Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
  arXiv  Detail & Related papers  (2025-12-22T15:03:57Z)
• Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection [59.04089915447622]
  ForenAgent is an interactive IFD framework that enables MLLMs to autonomously generate, execute, and refine Python-based low-level tools around the detection objective.<n>Inspired by human reasoning, we design a dynamic reasoning loop comprising global perception, local focusing, iterative probing, and holistic adjudication.<n>Experiments show that ForenAgent exhibits emergent tool-use competence and reflective reasoning on challenging IFD tasks.
  arXiv  Detail & Related papers  (2025-12-18T08:38:44Z)
• Seismology modeling agent: A smart assistant for geophysical researchers [14.28965530601497]
  This paper proposes an intelligent, interactive workflow powered by Large Language Models (LLMs)<n>We introduce the first Model Context Protocol (MCP) server suite for SPECFEM.<n>The framework supports both fully automated execution and human-in-the-loop collaboration.
  arXiv  Detail & Related papers  (2025-12-16T14:18:26Z)
• IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection [70.02774285130238]
  This paper explores the combination of rich text semantics with both image-level and pixel-level information from images.<n>We propose IAD-GPT, a novel paradigm based on MLLMs for Industrial Anomaly Detection.<n>Experiments on MVTec-AD and VisA datasets demonstrate our state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-10-16T02:48:05Z)
• El Agente: An Autonomous Agent for Quantum Chemistry [3.6593051631801106]
  El Agente Q is a multi-agent system that generates and executes quantum chemistry from natural language user prompts.<n>El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance.
  arXiv  Detail & Related papers  (2025-05-05T09:07:22Z)
• Optical Quantum Sensing for Agnostic Environments via Deep Learning [59.088205627308]
  We introduce an innovative Deep Learning-based Quantum Sensing scheme. It enables optical quantum sensors to attain Heisenberg limit (HL) in agnostic environments. Our findings offer a new lens through which to accelerate optical quantum sensing tasks.
  arXiv  Detail & Related papers  (2023-11-13T09:46:05Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.