VirtualLab_CC: An LLM-augmented virtual laboratory for automated computational chemistry research

Abstract

Recent advances in large language models (LLMs) have opened new avenues for automating scientific research workflows. Systems like the AI Scientist [1] demonstrate fully autonomous endto-end research pipelines for machine learning, but their applicability to domains requiring expensive high-performance computing (HPC) resources, specialized quantum chemistry software, and rigorous thermochemical accuracy remains unexplored. Here we present VirtualLab_CC, a hybrid LLM-augmented virtual laboratory for computational chemistry that integrates Claude (Anthropic) as an orchestration layer with a multi-agent architecture, gated lifecycle management, and bidirectional HPC cluster synchronization. Unlike fully autonomous systems, VirtualLab_CC enforces human-in-the-loop quality gates at every critical decision point while automating routine operations: conformer searches, input generation, job submission and monitoring, output parsing, failure diagnosis, and manuscript preparation. The system manages 21 specialized skills spanning literature review, cluster operations, result extraction, novelty verification, AI-assisted peer review, and citation validation. We demonstrate VirtualLab_CC on a prototype case study of 1,3-dipolar cycloadditions between stabilized Criegee intermediates and biogenic terpene dipolarophiles, involving 17 molecular species, 34 conformers, and over 100 SLURM jobs across multiple calculation stages. The system successfully identified and autocorrected cluster failures, detected a methodological anomaly (a suspiciously low dioxirane barrier requiring IRC verification), and coordinated multi-method validation across three DFT functionals. VirtualLab_CC bridges the gap between fully autonomous AI research and the rigorous quality control demands of computational chemistry, providing a practical framework for LLM-augmented scientific workflows in resource-constrained HPC environments.