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Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills
Mingwei Ding1,2, Chen Huang1,2, Yibo Hu1,2
1School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, P.R. China.
Journal of Chemical Theory and Computation
|June 9, 2026
Summary
This study introduces a new computational chemistry automation framework using OpenClaw for task coordination. It successfully managed complex simulations, recovered from errors, and extracted chemical reaction networks.
Area of Science:
- Computational Chemistry
- Scientific Automation
- High-Performance Computing
Background:
- Multistep computational chemistry workflows are complex and require robust automation.
- Existing frameworks may lack flexibility in coordinating diverse computational tools and handling errors.
Purpose of the Study:
- To present a decoupled framework for automating multistep computational chemistry tasks.
- To demonstrate the framework's capability in task coordination, error recovery, and data extraction.
Main Methods:
- Developed a framework built on OpenClaw for agent-based task coordination.
- Integrated planning skills for task specification and domain skills for computational procedures.
- Utilized DPDispatcher for execution on heterogeneous High-Performance Computing (HPC) environments.
Main Results:
- Successfully coordinated cross-tool execution in a reactive molecular dynamics (MD) simulation of methane oxidation.
- Demonstrated bounded recovery from runtime failures during the simulation.
- Extracted detailed reaction networks from the simulation data.
Conclusions:
- The proposed decoupled framework enhances the automation of complex computational chemistry workflows.
- The framework provides a flexible and resilient approach to managing simulations on HPC resources.
- This automation facilitates the extraction of valuable chemical insights, such as reaction networks.
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