Chemical Stoichiometry Simulator (화학양론 시뮬레이터)

수리 및 계산화학 화학양론 구현 (Mathematical and Computational Chemistry Stoichiometry Implementation)

A comprehensive educational tool for simulating and analyzing chemical reactions, designed for students and researchers in chemistry and chemical engineering.

Features

• Interactive GUI for easy reaction input and simulation
• Real-time visualization of reaction progress with animated plots
• Support for complex reaction networks and mechanisms
• Temperature-dependent kinetics using the Arrhenius equation
• Equilibrium calculations and ratio analysis
• Data export for further analysis
• Save and load reaction configurations
• Built-in example reactions for learning

Educational Value

• Learn fundamental chemical kinetics principles
• Visualize reaction dynamics in real-time
• Understand temperature effects on reaction rates
• Study equilibrium relationships and constants
• Practice stoichiometric calculations
• Explore reaction mechanisms
• Analyze concentration profiles
• Export data for reports and research

Installation

# Clone the repository
git clone https://github.com/minseo0388/stoichiometry.git
cd stoichiometry

# Install required packages
pip install -r requirements.txt

Getting Started

1. Run main.py to launch the application:

   python main.py

2. Input reaction details in the GUI:
   • Enter chemical equations (e.g., "A + B -> C")
   • Set rate constants (k) and activation energies (Ea)
   • Mark reactions as reversible if needed
3. Set initial conditions and parameters:
   • Initial concentrations
   • Temperature
   • Time step and total simulation time
4. Click "Run Simulation" to visualize results
5. Export data or save configuration for later use

Example Reactions

• Simple decomposition: A → B
• Reversible reaction: A + B ⇌ C
• Sequential reactions: 2A + B → C, C → D
• Complex mechanisms: A + B → C, C + D ⇌ E

Mathematical Background

• Rate equations: r = k[A]ᵃ[B]ᵇ
• Arrhenius equation: k = A*exp(-Ea/RT)
• Equilibrium constant: Keq = [C]/([A][B])
• Numerical integration methods

Technical Details

• Python-based implementation
• Numerical integration of rate equations
• Real-time Matplotlib visualizations
• Pandas for data handling and analysis
• Modular design for extensibility
• Type hints and documentation
• Error handling and validation

For Researchers

• Export data in CSV format
• Customizable reaction parameters
• Support for complex mechanisms
• Temperature dependence studies
• Equilibrium analysis tools

Contributing

Contributions are welcome! Please feel free to submit pull requests.

License

MIT License - See LICENSE file for details.

Contact

• Author: Minseo Choi
  (Department of Chemistry,
  College of Natural Sciences,
  Chungnam National University)
• GitHub: @minseo0388