用AI自动完成从材料模拟到发现的全流程，无需人工干预。
QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities
- 构建通用自主系统，自动调度多种原子级模拟方法。
- 在三类任务中实现从常规到前沿研究的自主执行。
- 适合需要高效材料研发的科研团队和计算化学从业者。
将大语言模型（LLMs）融入材料科学可显著优化计算流程，但现有智能体系统受限于特定领域工具调用范式和狭隘的任务设计。本文提出QUASAR——一个面向原子级模拟的通用自主系统，旨在推动生产级科学发现。该系统可自主协调跨多尺度、多方法的复杂工作流，涵盖密度泛函理论、机器学习势函数、分子动力学与蒙特卡洛模拟。通过自适应规划、上下文高效的内存管理及混合知识检索机制，支持在真实科研场景中无须人工介入的运行。我们在一系列三级任务上对QUASAR进行基准测试，涵盖常规操作至前沿挑战如光催化剂筛选与新材料评估。结果表明，QUASAR可作为通用原子级推理系统，而非单一任务自动化框架。研究初步验证了智能体AI在计算化学工作流中的部署潜力，同时指出了需进一步改进的方向。
原文摘要 · Abstract (English)
The integration of large language models (LLMs) into materials science offers a transformative opportunity to streamline computational workflows, yet current agentic systems remain constrained by rigid, carefully crafted domain-specific tool-calling paradigms and narrowly scoped agents. In this work, we introduce QUASAR, a universal autonomous system for atomistic simulation designed to facilitate production-grade scientific discovery. QUASAR autonomously orchestrates complex multi-scale workflows across diverse methods, including density functional theory, machine learning potentials, molecular dynamics, and Monte Carlo simulations. The system incorporates robust mechanisms for adaptive planning, context-efficient memory management, and hybrid knowledge retrieval to navigate real-world research scenarios without human intervention. We benchmark QUASAR against a series of three-tiered tasks, progressing from routine tasks to frontier research challenges such as photocatalyst screening and novel material assessment. These results suggest that QUASAR can function as a general atomistic reasoning system rather than a task-specific automation framework. They also provide initial evidence supporting the potential deployment of agentic AI as a component of computational chemistry research workflows, while identifying areas requiring further development.
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