QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

• URL: http://arxiv.org/abs/2602.00185v1
• Date: Fri, 30 Jan 2026 05:29:44 GMT
• Title: QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities
• Authors: Fengxu Yang, Jack D. Evans,
• Abstract summary: QUASAR is a universal autonomous system for atomistic simulation designed to facilitate production-grade scientific discovery.<n>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.<n>Results suggest that QUASAR can function as a general atomistic reasoning system rather than a task-specific automation framework.
• Score: 0.7519872646378835
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 tool-calling approaches 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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