Chemistry-ID

Information dynamics provides a unified mathematical language for the subdisciplines of chemistry.

This repository contains complete implementations of the information dynamics framework for chemical reaction simulations. Through three prototypical systems (NaI photodissociation, Cl₂ photodissociation, and H₂ formation), it demonstrates the universality of the framework. All code is original and aims to validate the “virtual space – real space – coupling matrix” three‑element framework for different reaction types (non‑adiabatic/adiabatic, dissociation/formation, two‑state/one‑state surfaces). The simulations reproduce the classic results of Zewail’s femtochemistry experiments (NaI dissociation yield ~65%) and provide a full numerical implementation.

Table of Contents

• Background: Information Dynamics for Chemical Reactions
• Repository Structure
• Requirements
• Quick Start
• Examples and Results
• Citation
• License

Background: Information Dynamics for Chemical Reactions

The core axiom of information dynamics states that the evolution of a system is determined by the virtual space (absolute ordered rules), the real space (variable observational data), and the coupling matrix (dynamic projection mechanism). In chemical reactions, this axiom maps as follows:

• Virtual space (potential energy surface rules) – defines the absolute allowed regions for nuclear motion. For example, the ionic state (Morse well) and covalent state (repulsive/descending channel) of NaI.
• Real space (initial wave packet data) – provides the observed distribution of initial positions and momenta (Franck‑Condon Gaussian distribution).
• Coupling matrix (projection mechanism) – drives the system toward the steady‑state product. For a single PES it reduces to gradient flow (Newtonian mechanics); for two crossing PESs it becomes the Landau‑Zener non‑adiabatic transition probability.

The code in this repository strictly follows these mappings, turning the abstract concept of “information flow” into executable Python.

Repository Structure

Chemistry-ID/
├── NaI_photodissociation/            # NaI photodissociation (non‑adiabatic, two crossing surfaces)
│   ├── nai_photodissociation.py      # main simulation script
│   ├── nai_photodissociation_sensitivityscan.py # parameter scanning script
│   ├── nai_potentials.png            # virtual space potential energy surfaces
│   ├── nai_trajectories.png          # example trajectories (red=ionic, blue=covalent)
│   ├── 2d_scan_heatmap.png           # 2D parameter heatmap
│   ├── 2d_scan_contour.png           # 2D parameter contour plot
│   ├── 2d_scan_results.txt           # detailed scanning results
│   └── *.log                         # archived run logs
│
├── Cl2_photodissociation/            # Cl₂ photodissociation (adiabatic, single repulsive surface)
│   ├── cl2_photodissociation.py      # main simulation script
│   ├── cl2_potential.png             # virtual space potential curve
│   ├── cl2_kinetic_energy.png        # fragment kinetic energy distribution
│   └── *.log                         # archived run logs
│
├── H2_formation/                     # H₂ formation (attractive potential, bound state formation)
│   ├── h2_formation.py               # main simulation script
│   ├── h2_formation_prob.png         # binding probability vs. initial bond length
│   └── *.log                         # archived run logs
│
└── README.md                         # this file

Requirements

All code is written in Python 3.7+ and depends on the following standard libraries:

• numpy (numerical calculations and random number generation)
• matplotlib (plotting and visualisation)
• itertools, time, random

Install dependencies with:

pip install numpy matplotlib

Quick Start

1. Clone the repository:

   git clone https://github.com/hkaiopen/Chemistry-ID.git
   cd Chemistry-ID

2. Run any simulation (take NaI as example):

   cd NaI_photodissociation
   python nai_photodissociation.py

   • The script runs 5 independent ensembles (2000 trajectories each) and outputs the mean dissociation yield, standard deviation, and 95% confidence interval.
   • It also generates nai_potentials.png (PES plot) and nai_trajectories.png (trajectory plot).
3. Parameter scan (NaI):

   python nai_photodissociation_sensitivityscan.py

   • This script performs a 2D grid scan over De_ionic and coupling, automatically finds the parameter combination that gives a yield closest to 65%, and produces heatmaps and contour plots.
4. Run Cl₂ or H₂ simulations:

   cd ../Cl2_photodissociation
   python cl2_photodissociation.py

Examples and Results

1. NaI Photodissociation (non‑adiabatic, two crossing surfaces)

• Virtual space: ionic state (Morse, De = 3.0 eV) + covalent state (linear descent)
• Coupling matrix: Landau‑Zener probability
• Result: dissociation yield 64.7% ± 1.71%, in excellent agreement with Zewail’s experiment (~65%).

Virtual space potential energy surfaces:
Example trajectories (red=ionic, blue=covalent):

2. Cl₂ Photodissociation (adiabatic, single repulsive surface)

• Virtual space: exponential repulsive potential ( V(R) = A e^{-\beta (R-R_0)} )
• Coupling matrix: gradient flow (Newtonian mechanics)
• Result: dissociation yield 100%, mean fragment kinetic energy 3.84 eV (consistent with literature).

Potential curve:
Kinetic energy distribution:

3. H₂ Formation (attractive potential, bound state formation)

• Virtual space: Morse potential
• Coupling matrix: gradient flow
• Result: binding probability close to 1 when total energy is below the dissociation threshold, and drops to 0 above it.

Binding probability vs. initial bond length:

Citation

If you use this code or the information dynamics framework in your academic work, please cite the following papers (adapt as needed):

@article{Huang2026ChemistryID,
  title   = {Information Dynamics Explains Chemical Reactions: A Unified Framework from NaI Photodissociation to H₂ Formation},
  author  = {Kai Huang, Hongkui Liu, Ziwei Huang},
  year    = {2026},
  note    = {GitHub repository: \url{https://github.com/hkaiopen/Chemistry-ID}},
  doi     = {10.5281/zenodo.XXXXXXX}   % optional
}

Earlier work in other domains:

• DNA assembly and RNA inverse folding: Liu, H., & Huang, K. (2026). Validation of the Real-Imaginary Duality Principle in Core Challenges of Computational Biology. Zenodo. DOI:10.5281/zenodo.20057469
• Protein‑templated DNA synthesis: Huang, K., Liu, H., & Huang, Z. (2026). An Information Dynamics Model of Protein-Templated DNA Synthesis. Zenodo. DOI:10.5281/zenodo.20496890

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License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

You are free to share and adapt the material under the following terms:

• Attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.
• NonCommercial – You may not use the material for commercial purposes.
• ShareAlike – If you remix, transform, or build upon the material, you must distribute your contributions under the same license.

For commercial use, please contact the authors.

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Tribute: We thank you for your attention and future collaboration, which advance the frontiers of information science and inspire our exploration across disciplines. 🙏