# ChemGraph: An Agentic Framework for Computational Chemistry Workflows

arXiv: https://arxiv.org/abs/2506.06363 · HTML: https://mlsanity.com/paper/2506.06363/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 9 of 9)

- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields](https://mlsanity.com/paper/2206.07697/) (2022)
- [MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback](https://mlsanity.com/paper/2309.10691/) (2023)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials](https://mlsanity.com/paper/2205.06643/) (2022)
- [CACTUS: Chemistry Agent Connecting Tool-Usage to Science](https://mlsanity.com/paper/2405.00972/) (2024)
- [FlowMind: Automatic Workflow Generation with LLMs](https://mlsanity.com/paper/2404.13050/) (2024)
- [MDCrow: Automating Molecular Dynamics Workflows with Large Language Models](https://mlsanity.com/paper/2502.09565/) (2025)
- [Agent-based Learning of Materials Datasets from Scientific Literature](https://mlsanity.com/paper/2312.11690/) (2023)

## Built on by (most cited first, 16 of 16)

- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025)
- [K-Dense Analyst: Towards Fully Automated Scientific Analysis](https://mlsanity.com/paper/2508.07043/) (2025)
- [QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities](https://mlsanity.com/paper/2602.00185/) (2026)
- [Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials](https://mlsanity.com/paper/2509.21647/) (2025)
- [El Agente Estructural: An Artificially Intelligent Molecular Editor](https://mlsanity.com/paper/2602.04849/) (2026)
- [SimMOF: AI agent for Automated MOF Simulations](https://mlsanity.com/paper/2603.29152/) (2026)
- [From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization](https://mlsanity.com/paper/2510.02389/) (2025)
- [Towards LLM-enabled autonomous combustion research: A literature-aware agent for self-corrective modeling workflows](https://mlsanity.com/paper/2601.01357/) (2026)
- [Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents](https://mlsanity.com/paper/2607.09195/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System](https://mlsanity.com/paper/2604.07681/) (2026)
- [El Agente Forjador: Task-Driven Agent Generation for Quantum Simulation](https://mlsanity.com/paper/2604.14609/) (2026)
- [MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflows](https://mlsanity.com/paper/2608.02642/) (2026)
- [From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics](https://mlsanity.com/paper/2603.13191/) (2026)
- [From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2605.03205/) (2026)
- [Science Done on a Machine by a Machine: AI Agents in Computational Chemistry](https://mlsanity.com/paper/2608.18508/) (2026)

## Abstract (arXiv)

Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains challenging due to the wide range of computational methods, diverse software ecosystems, and the need for expert knowledge and manual effort for the setup, execution, and validation stages. In this work, we present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. Users can perform tasks such as molecular structure generation, single-point energy, geometry optimization, vibrational analysis, and thermochemistry calculations with methods ranging from tight-binding and machine learning interatomic potentials to density functional theory or wave function theory-based methods. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models like GPT-4o. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables smaller LLM models to match or exceed GPT-4o's performance in specific scenarios.