Configure ORCA functional, basis, charge, and multiplicity for molecular geometry optimization.
npx skills add https://github.com/Hello-QM/catgo-LRG --skill orca-opt
Please help me install this Agent Skill. Skill: orca-opt Source: https://github.com/Hello-QM/catgo-LRG/tree/main/server/catgo/workflow/skills/orca/opt Command: npx skills add https://github.com/Hello-QM/catgo-LRG --skill orca-opt
ORCA geometry optimization automates the setup of quantum-chemical structure optimizations, reducing manual configuration and potential errors in functional/basis selection, dispersion, solvent effects, and convergence criteria.
Load a molecule, configure ORCA options (method, basis, dispersion, solvent, and convergence), then create and execute a geo_opt task in the workflow.
High-intent search queries and answers about installing and using this skill.
ORCA geometry optimization is configured by specifying the functional, basis set, charge, multiplicity, and optional D3BJ or CPCM solvent settings within a geo_opt task. Submitting this task in the CatGo workflow automates the molecular structure refinement.
Dispersion corrections like D3BJ or D4 are applied during geometry optimization to accurately capture non-covalent interactions in molecules like benzene or radical complexes. Configuring these parameters in the geo_opt task ensures a reliable minimum-energy structure.
To balance speed and accuracy during geometry optimization, configure the ORCA workflow to progress from the SVP basis set to TZVP. This methodical progression optimizes molecular geometries efficiently while converging on a reliable structure.
Yes, CPCM solvent effects can be included in the ORCA workflow by configuring the solvent settings within the geo_opt task. This allows you to optimize molecular geometries for radical complexes or solvated systems directly through the CatGo interface.
Yes, the CatGo workflow supports creating, submitting, monitoring, and retrieving results for ORCA geo_opt tasks. This automation reduces manual configuration errors and streamlines the entire quantum-chemical structure optimization process.
Automated ORCA geometry optimization reduces manual configuration errors in functional, basis, dispersion, and convergence criteria selection. It streamlines the setup of quantum-chemical structure optimizations to reliably obtain minimum-energy molecular geometries.
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