xTB
Extended Tight Binding
Computing Applications (HPC) LGPL-3.0 (Free)
About
A semi-empirical quantum chemical program suite developed by the Grimme group. It performs fast and accurate calculations of structures, energies, and properties using the GFN-xTB family of methods for large molecular systems.
Key Features
- Methods: GFN0-xTB, GFN1-xTB, GFN2-xTB
- Speed: 1000x faster than DFT
- Applications: conformers, reactions, MD
- Properties: energies, gradients, Hessians
Skills
MCP skills available for xTB, callable from AI workflows and the Paramus chat:
- Analysis Info — [info-only, does not execute] Documentation
- Categories — List xTB calculation categories
- Conformers Info — [info-only, does not execute] Documentation
- Dipole Info — [info-only, does not execute] Documentation
- Dynamics Info — [info-only, does not execute] Documentation
- Frequency — XTB vibrational frequency analysis.
- Functions — List available xTB functions
- Hessian — XTB Hessian/frequency calculation.
- Info — Get xTB module information including version, capabilities, and available methods.
- Interactions Info — [info-only, does not execute] Documentation
- Md — XTB molecular dynamics simulation.
- Optimization Info — [info-only, does not execute] Documentation
- Optimize — XTB geometry optimization.
- Properties Info — [info-only, does not execute] Documentation
- Reactivity Info — [info-only, does not execute] Documentation
- Scan Info — [info-only, does not execute] Documentation
- Singlepoint — Run an xTB single-point energy calculation. Fast semi-empirical tight-binding method for electronic energy, orbital energies, and molecular properties.
- Solvation Info — [info-only, does not execute] Documentation
- Spectra Info — [info-only, does not execute] Documentation
- Spin Info — [info-only, does not execute] Documentation
- Transition State Info — [info-only, does not execute] Documentation
- Version — Get xTB version from Docker container.
Browse the full xTB skill documentation
Citation
Bannwarth, C. et al. GFN2-xTB – An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method. J. Chem. Theory Comput. 15, 1652-1671 (2019). DOI:10.1021/acs.jctc.8b01176
Frequently Asked Questions
What is xTB?
xTB is a computing applications (hpc) application available in the Paramus App Store. A semi-empirical quantum chemical program suite developed by the Grimme group. It performs fast and accurate calculations of structures, energies, and properties using the GFN-xTB family of methods for large molecular systems.
Is xTB free to use?
Yes. xTB is distributed under the LGPL-3.0 (Free) license and is available at no cost through the Paramus App Store.
How do I install xTB?
xTB is installed through Paramus Chemistry OS, an on-premise Windows platform for computational chemistry. Open the Paramus App Store in your local installation and select xTB for one-click deployment.
What are the key features of xTB?
Key features of xTB include: Methods: GFN0-xTB, GFN1-xTB, GFN2-xTB; Speed: 1000x faster than DFT; Applications: conformers, reactions, MD; Properties: energies, gradients, Hessians.
What type of application is xTB?
xTB belongs to the “Computing Applications (HPC)” category in the Paramus App Store. It runs on Paramus Chemistry OS and can also be accessed through Paramus Cloud for supported workflows.
What platform does xTB run on?
xTB runs on Paramus Chemistry OS, a Windows-based on-premise platform that provides local compute power for demanding simulations. It requires a Paramus OS installation with appropriate hardware resources.
Can xTB be automated or integrated with AI workflows?
Yes. xTB is available as part of the Paramus ecosystem which supports MCP (Model Context Protocol) tools for AI-driven automation. This enables integration with large language models and automated research pipelines.
How should I cite xTB in publications?
The recommended citation for xTB is: Bannwarth, C. et al. GFN2-xTB – An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method. J. Chem. Theory Comput. 15, 1652-1671 (2019). DOI:10.1021/acs.jctc.8b01176