Convert SMILES or names into optimized DFT geometries and HOMO/LUMO orbital data.
npx skills add https://github.com/BioKT/Skills-BIOKT --skill dft-homo-lumo
Please help me install this Agent Skill. Skill: dft-homo-lumo Source: https://github.com/BioKT/Skills-BIOKT/tree/main/dft-homo-lumo Command: npx skills add https://github.com/BioKT/Skills-BIOKT --skill dft-homo-lumo
DFT geometry optimisation and frontier orbital analysis for small molecules are typically multi-tool, multi-step tasks. This skill streamlines the workflow by converting SMILES or molecule names into optimised geometries, HOMO/LUMO energies, and cube data for visualisation.
Provide a SMILES string or molecule name to start a DFT HOMO/LUMO workflow.
High-intent search queries and answers about installing and using this skill.
DFT geometry optimisation and frontier orbital analysis start by converting a SMILES string into 3D coordinates, then optimizing the structure using PySCF to output HOMO/LUMO energies and cube files.
HOMO LUMO cube files contain volumetric data of frontier orbitals needed for visualizing electron density in 3D. They are generated after DFT optimisation for use in Jupyter and py3Dmol exploration.
Yes, you can perform DFT geometry optimisation for small molecules using PySCF and GeomeTRIC with user-selectable functionals and basis sets to generate optimized structures and SCF logs.
Generating electron density cubes requires running DFT calculations on an optimized molecular structure. This skill computes the volumetric data and outputs cube files ready for visualization and analysis.
Yes, RDKit handles the initial SMILES to 3D geometry conversion, while py3Dmol is used to visualize the resulting molecular orbitals and electron density cube files in a Jupyter environment.
Using PySCF for frontier orbital analysis is limited to small molecules due to computational scaling. It outputs optimized geometries, HOMO/LUMO energies, and cube data but may not suit large systems.
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