IonHopper

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

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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

2. Prepare files:

python -m ionhopper.setup.prepare_calculation

3. 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.