2DModel

Official Resources

• Homepage: https://github.com/UllrichDFT/2DModel
• Source Repository: https://github.com/UllrichDFT/2DModel
• License: Open Source
• Author: Prof. Carsten Ullrich (University of Missouri)

Overview

2DModel is a Python code for performing DFT and TDDFT simulations on 2D model solids. Developed by Prof. Carsten Ullrich's group (a leading expert in TDDFT theory), it provides a testbed for exploring fundamental aspects of density functional theory and time-dependent DFT in reduced dimensionality. The code is valuable for methodology development, testing new XC functionals, and understanding TDDFT physics in a controlled setting.

Scientific domain: 2D model systems, TDDFT methodology development, linear response theory
Target user community: TDDFT methodology developers, students learning DFT/TDDFT, theoretical physicists

Theoretical Methods

• Density Functional Theory (DFT)
• Time-Dependent DFT (TDDFT)
• Linear Response TDDFT
• 2D periodic systems
• Model Hamiltonians
• XC functional testing

Capabilities

• Ground-state DFT for 2D models
• Linear-response TDDFT
• Real-time TDDFT propagation
• Optical response calculations
• Model solid simulations
• Parameter studies
• Methodology benchmarking

Key Strengths

Methodology Development:

• Simplified 2D test cases
• Controlled parameter space
• Exact solutions for comparison
• XC functional testing ground

Expert Authorship:

• Carsten Ullrich (TDDFT textbook author)
• Rigorous theoretical foundation
• Research-grade implementation

Educational Value:

• Clear Python implementation
• Reduced complexity vs 3D
• Direct theory-to-code correspondence
• Ideal for learning TDDFT

Flexible Input:

• Configurable parameters
• Multiple calculation modes
• Print output control
• Python-based configuration

Inputs & Outputs

• Input formats:

  • Python input file (infile.py)
  • Configurable parameters:
    • PrintOut: Screen output control
    • Ground state parameters
    • Linear response parameters

• Output data types:

  • Ground state energies
  • Response functions
  • Optical properties
  • Screen or file output

Interfaces & Ecosystem

• Dependencies:

  • Python standard libraries
  • NumPy/SciPy (typical)

• Related work:

  • Ullrich TDDFT textbook
  • Working_TDDFT tutorials

Theoretical Background

The code implements TDDFT for 2D periodic models, which serve as simplified test systems for understanding:

• Exchange-correlation effects in reduced dimensions
• Linear response formalism
• Collective excitations in 2D
• XC kernel behavior

This follows the pedagogical approach of Prof. Ullrich's textbook "Time-Dependent Density-Functional Theory: Concepts and Applications" (Oxford University Press).

Performance Characteristics

• Speed: Fast (2D models are computationally light)
• System size: Model parameters, not atom count
• Memory: Minimal requirements
• Purpose: Methodology development and teaching

Limitations & Known Constraints

• Dimensionality: 2D only (by design)
• Real materials: Model systems, not realistic materials
• Features: Focused on fundamental TDDFT
• Documentation: Minimal, research-oriented

Comparison with Other Codes

• vs production TDDFT codes: 2DModel for methodology, others for real systems
• vs 1D model codes: 2DModel captures additional physics
• vs full 3D codes: Much faster, complementary purpose
• Unique strength: 2D TDDFT testbed from leading theorist

Application Areas

Method Development:

• XC functional benchmarking
• Kernel approximation testing
• Memory effects in TDDFT
• Beyond-adiabatic methods

Education:

• Teaching TDDFT concepts
• Graduate course projects
• Understanding linear response
• Theory implementation practice

Fundamental Research:

• 2D material physics insights
• Collective mode studies
• Excitonic effects in 2D
• Correlation in low dimensions

Best Practices

• Use for understanding, not production calculations
• Compare with analytical limits when available
• Test new XC approximations systematically
• Document parameter choices

Community and Support

• Open-source on GitHub (UllrichDFT)
• Academic development
• Associated with TDDFT research community
• Python implementation

Verification & Sources

Primary sources:

1. GitHub repository: https://github.com/UllrichDFT/2DModel
2. C.A. Ullrich, "Time-Dependent Density-Functional Theory" (OUP, 2012)
3. Associated research publications

Confidence: VERIFIED - From established TDDFT research group

Verification status: ✅ VERIFIED

• Source code: OPEN (GitHub)
• Author: Prof. C.A. Ullrich (leading TDDFT expert)
• Purpose: Methodology development and education
• Language: Python