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IonHopper

IonHopper

A Python framework implementing Basin Hopping Monte Carlo algorithm coupled with VASP for studying ion insertion in materials.

License: MIT Python 3.8+

Scientific Background

The investigation of ion insertion mechanisms in materials is fundamental to the development of next-generation energy storage systems. Understanding the structural evolution, phase stability, and thermodynamic properties during ion insertion/extraction processes requires accurate computational methods. Density functional theory (DFT) calculations have emerged as a powerful tool for studying these phenomena at the atomic scale.

However, traditional DFT approaches face significant challenges when exploring the vast configuration space of possible ion arrangements:

  1. Energy Landscape Complexity

    • Multiple local minima in potential energy surface
    • Metastable configurations trap local optimization
    • Complex structural reorganization during ion insertion

  2. Configuration Space

    • Exponential growth with system size
    • Manual structure generation introduces bias
    • Limited sampling of possible arrangements

  3. Phase Stability

    • Multiple competing phases at each composition
    • Formation energy calculations require global minima
    • Voltage profiles depend on accurate phase identification

Methodology

IonHopper implements the Basin Hopping Monte Carlo algorithm coupled with DFT calculations to address these challenges. The method:

  1. Energy Surface Transformation

    • Maps continuous potential energy surface to discrete basins
    • Each basin corresponds to local minimum
    • Simplifies global optimization problem

  2. Monte Carlo Sampling

    • Temperature-controlled acceptance criterion
    • Efficient exploration of configuration space
    • Escape from local minima

  3. DFT Integration

    • Accurate energy evaluation using VASP
    • Structure optimization at each step
    • Electronic structure analysis

  4. Thermodynamic Analysis

    • Convex hull construction
    • Phase stability determination
    • Voltage profile calculation

Installation

git clone https://github.com/nabkh/ionhopper.git
cd ionhopper
pip install -e .

Requirements:

  • Python ≥ 3.8
  • ASE ≥ 3.22.1
  • NumPy ≥ 1.21.0
  • PyYAML ≥ 5.4.1
  • VASP 5.4+ (not included)

Quick Start

  1. Configure calculation:
cp config.yaml.template config.yaml
# Edit settings
  1. Prepare files:
python -m ionhopper.setup.prepare_calculation
  1. Run BHMC:
python -m ionhopper.workflow.run_compositions --compositions 0.25 0.5 0.75 1.0

Citation

If you use this code in your research, please cite:

@article{khossossi2022revealing,
  title={Revealing the superlative electrochemical properties of o-B2N2 monolayer in Lithium/Sodium-ion batteries},
  author={Khossossi, Nabil and Luo, Wei and Haman, Zakaryae and Singh, Deobrat and Essaoudi, Ismail and Ainane, Abdelmajid and Ahuja, Rajeev},
  journal={Nano Energy},
  volume={96},
  pages={107066},
  year={2022},
  publisher={Elsevier}
}

@article{khossossi2022flexible,
  title={Flexible 3D porous boron nitride interconnected network as a high-performance Li-and Na-ion battery electrodes},
  author={Khossossi, Nabil and Singh, Deobrat and Luo, Wei and Ahuja, Rajeev},
  journal={Electrochimica Acta},
  volume={421},
  pages={140491},
  year={2022},
  publisher={Elsevier}
}

License

MIT License. See LICENSE file.