ChemGraph: An Agentic Framework for Computational Chemistry Workflows

• URL: http://arxiv.org/abs/2506.06363v1
• Date: Tue, 03 Jun 2025 21:11:56 GMT
• Title: ChemGraph: An Agentic Framework for Computational Chemistry Workflows
• Authors: Thang D. Pham, Aditya Tanikanti, Murat Keçeli,
• Abstract summary: ChemGraph is an agentic framework powered by artificial intelligence and state-of-the-art simulation tools.<n>Users can perform tasks such as molecular structure generation, single-point energy, geometry optimization, vibrational analysis, and thermochemistry calculations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model [6.107266553770076]
  We develop a molecular inference framework based on mol-infer, namely mol-infer-GNN, that utilizes GNN as the learning method.<n>Our proposed GNN model can obtain satisfying learning performances for some properties despite its simple structure.
  arXiv  Detail & Related papers  (2025-07-05T06:57:37Z)
• ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data [53.78763789036172]
  We present ChemActor, a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences.<n>This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input.<n>Experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor achieves state-of-the-art performance, outperforming the baseline model by 10%.
  arXiv  Detail & Related papers  (2025-06-30T05:11:19Z)
• ChemAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning [12.745398618084474]
  Large language models (LLMs) have recently demonstrated promising capabilities in chemistry tasks.<n>We propose an LLM-based agent that integrates 137 external chemical tools created ranging from basic information retrieval to complex reaction predictions.
  arXiv  Detail & Related papers  (2025-06-09T08:41:39Z)
• AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams [2.5875933818780363]
  Current AI methods cannot auto-generate PFDs or PIDs, despite their critical role in scaling chemical processes.<n>We present a closed loop, aware physics framework for the automated generation of industrially viable PFDs and PIDs.
  arXiv  Detail & Related papers  (2025-05-30T13:32:00Z)
• ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning [64.2106664137118]
  ChemAgent is a novel framework designed to improve the performance of large language models (LLMs)<n>It is developed by decomposing chemical tasks into sub-tasks and compiling these sub-tasks into a structured collection that can be referenced for future queries.<n>When presented with a new problem, ChemAgent retrieves and refines pertinent information from the library, which we call memory.
  arXiv  Detail & Related papers  (2025-01-11T17:10:30Z)
• Improving Molecular Modeling with Geometric GNNs: an Empirical Study [56.52346265722167]
  This paper focuses on the impact of different canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks.
  arXiv  Detail & Related papers  (2024-07-11T09:04:12Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
  Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis.<n>The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions.<n>Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent [79.58680275615752]
  We propose an energy-efficient federated meta-learning framework. We assume each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model.
  arXiv  Detail & Related papers  (2021-05-31T08:15:44Z)
• A Universal Framework for Featurization of Atomistic Systems [0.0]
  Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales. We introduce the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms. We demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements.
  arXiv  Detail & Related papers  (2021-02-04T03:11:00Z)

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.