dft-homo-lumo

Convert SMILES or names into optimized DFT geometries and HOMO/LUMO orbital data.

1|1|Updated Apr 16, 2026

Search Tags:#visualization #dft #rdkit #pyscf #homo-lumo #frontier-orbitals #cube-files

Ready to run

npx skills add https://github.com/BioKT/Skills-BIOKT --skill dft-homo-lumo

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyscf, geometric, rdkit, py3Dmol, numpy.

What problem does it solve?

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.

Core Features & Use Cases

SMILES to 3D geometry: Convert input to 3D coordinates suitable for quantum calculations.
DFT optimisation: Perform geometry optimisation using PySCF and GeomeTRIC with user-selectable functionals and basis sets.
HOMO/LUMO & cube data: Compute frontier orbital energies and generate HOMO/LUMO cube files plus electron density cubes for visualization.
Visualization readiness: Output data ready for Jupyter/py3Dmol exploration.

Quick Start

Provide a SMILES string or molecule name to start a DFT HOMO/LUMO workflow.

Frequently Asked Questions about dft-homo-lumo

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform DFT geometry optimisation and calculate frontier orbitals from a SMILES string?▼

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.

What are HOMO LUMO cube files and when do I need them for molecular visualization?▼

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.

Can I use PySCF and GeomeTRIC to optimize small molecules with custom functionals and basis sets?▼

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.

What is the best way to generate electron density cubes for downstream analysis?▼

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.

Does this DFT workflow require RDKit and py3Dmol for visualizing molecular orbitals?▼

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.

What are the limitations of using PySCF for frontier orbital analysis?▼

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.