PyTDDFT

Official Resources

• Homepage: https://github.com/f-fathurrahman/PyTDDFT
• Documentation: GitHub README and code comments
• Source Repository: https://github.com/f-fathurrahman/PyTDDFT
• License: Not clearly specified (research prototype)

Overview

PyTDDFT is a research/educational Python implementation of time-dependent density functional theory for learning and prototyping TDDFT methods. Developed by Fadjar Fathurrahman, PyTDDFT provides a transparent, readable implementation of TDDFT in Python, prioritizing code clarity and educational value over production performance. It serves as a platform for understanding TDDFT algorithms and experimenting with method development.

Scientific domain: TDDFT, Python implementation, educational quantum chemistry
Target user community: Students, educators, method developers, Python users

Theoretical Methods

• Time-Dependent Density Functional Theory
• Linear-response TDDFT
• Python implementation
• Educational formalism
• Prototype algorithms

Capabilities (CRITICAL)

Note: Research prototype, not for production.

• TDDFT excitation energies (basic)
• Educational demonstrations
• Method development
• Algorithm prototyping
• Python-based learning
• Transparent implementation
• Small system calculations

Sources: GitHub repository

Key Characteristics

Research Prototype:

• Experimental code
• Method development
• Algorithm testing
• Not production-ready
• Educational focus

Python Implementation:

• Pure Python
• Readable code
• NumPy-based
• Educational clarity
• Easy modification

Educational Value:

• Transparent algorithms
• Learning TDDFT
• Code walkthroughs
• Understanding methods
• Interactive exploration

Limitations & Known Constraints

• Status: Research prototype

• Performance: Not optimized

• Features: Basic TDDFT only

• System size: Very small

• Input formats:

  • Python script (.py) defining molecule and parameters
  • Direct NumPy array interfaces
  • Standard XYZ coordinates within Python strings

• Output data types:

  • Standard output log (energies, convergence)
  • NumPy arrays of excitation energies
  • Oscillator strength arrays

• Support: Research-level

• Purpose: Educational/prototyping

Recommendation

For production TDDFT calculations, use established codes:

• Octopus: Real-space TDDFT
• Quantum ESPRESSO: Plane-wave TDDFT (turbo modules)
• NWChem: Comprehensive TDDFT
• PySCF: Python-based production code

Educational Alternative

For learning TDDFT in Python with production quality:

• PySCF: Mature Python quantum chemistry with TDDFT

Verification & Sources

Primary sources:

1. GitHub: https://github.com/f-fathurrahman/PyTDDFT
2. Source code

Secondary sources:

1. TDDFT literature
2. Educational quantum chemistry

Confidence: UNCERTAIN - Research prototype

Verification status: ⚠️ RESEARCH PROTOTYPE

• GitHub: ACCESSIBLE
• Status: RESEARCH/EDUCATIONAL PROTOTYPE
• Not for production use
• Recommendation: Use established TDDFT codes (Octopus, QE turbo modules, NWChem) or PySCF for Python-based production calculations