Metadata-Version: 2.4
Name: met_al_science
Version: 1.0.0
Summary: MET-AL: Coordination Bond Stability in Transition Metals Under Extreme Environments - A Physics-Informed AI Framework
Author-email: Samir Baladi <gitdeeper@gmail.com>
License: MIT
Project-URL: Homepage, https://met-al-science.netlify.app
Project-URL: GitHub, https://github.com/gitdeeper10/MET-AL
Project-URL: GitLab, https://gitlab.com/gitdeeper07/MET-AL
Project-URL: Bitbucket, https://bitbucket.org/gitdeeper-10/metal
Project-URL: Codeberg, https://codeberg.org/gitdeeper10/metal
Project-URL: DOI, https://doi.org/10.5281/zenodo.19566418
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Materials Science
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE
License-File: AUTHORS.md
Dynamic: license-file

# 🪙 MET-AL

## Coordination Bond Stability in Transition Metals Under Extreme Environments

[![PyPI version](https://badge.fury.io/py/metal.svg)](https://pypi.org/project/metal/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.19566418.svg)](https://doi.org/10.5281/zenodo.19566418)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
[![OSF Registration](https://img.shields.io/badge/OSF-Registration-purple.svg)](https://osf.io/6v4xt)

---

## Overview

MET-AL introduces the first physics-informed AI framework for quantitative characterization of coordination bond stability in transition metal complexes operating under extreme environmental conditions — the Coordination Bond Stability Index (CBSI). Built on seven orthogonal physico-chemical descriptors, MET-AL elevates the study of transition metal behavior from empirical materials testing to rigorous AI-driven predictive science.

### Core Contributions

| Component | Full Name | Role |
|-----------|-----------|------|
| **CBSI** | Coordination Bond Stability Index | Weighted composite of 7 parameters |
| **η_HP** | Hydrostatic Pressure Compression Efficiency | Bond compression under high pressure (19%) |
| **E_a** | Adaptive Structural Resilience Index | Mechanical stability under stress (17%) |
| **ρ_EC** | Electrochemical Signal Density | Electrochemical communication activity (18%) |
| **σ_nav** | Stress-Tensor Navigation Accuracy | Bond rearrangement directional precision (14%) |
| **LXF** | Ligand Exchange Fidelity | Metal-ligand exchange economy (13%) |
| **K_latt** | Topological Lattice Expansion Rate | Fractal geometry of distortion field (11%) |
| **ACI** | Corrosion Propagation Inhibition Index | Passivation electrochemical effect (8%) |

### Validated Results

| Metric | MET-AL | Target |
|--------|--------|--------|
| CBSI Prediction Accuracy | **93.4%** | >90% ✅ |
| Bond Failure Detection | **95.1%** | >90% ✅ |
| False Alert Rate | **3.8%** | <5% ✅ |
| Early Warning Lead Time | **38 days** | >30 days ✅ |
| ρ_EC × K_latt Correlation | **r = +0.924** | >0.85 ✅ |

---

## Installation

```bash
pip install metal
```

---

Quick Start

CBSI Framework

```python
from metal import CBSI, CBSIParameters

# Initialize with 7 parameters
params = CBSIParameters(
    eta_hp=0.74,    # Hydrostatic compression
    ea=0.67,        # Adaptive resilience
    pec=0.57,       # Electrochemical density
    sigma_nav=0.73, # Stress navigation
    lxf=0.91,       # Ligand exchange fidelity
    klatt=1.74,     # Lattice expansion (Df)
    aci=0.43        # Corrosion inhibition
)

# Compute CBSI
cbsi = CBSI.compute(params)
print(f"CBSI: {cbsi:.3f}")
```

AI Prediction

```python
from metal import MetalPredictor

predictor = MetalPredictor()
result = predictor.predict(impedance_data, xrd_data)
print(f"Failure probability: {result.probability:.3f}")
print(f"Early warning: {result.days_to_failure} days")
```

---

Documentation

Resource Link
Website https://met-al-science.netlify.app
Research Paper DOI: 10.5281/zenodo.19566418
API Reference https://metal.readthedocs.io
OSF Registration https://osf.io/6v4xt

---

Project Structure

```
MET-AL/
│
├── metal/
│   ├── __init__.py
│   ├── cbsi.py           # CBSI composite formula
│   ├── parameters.py     # 7 physico-chemical parameters
│   ├── ai_models.py      # 1D-CNN, XGBoost, LSTM, PINN
│   ├── data_loader.py    # Dataset loader (3,847 CCUs)
│   └── utils.py          # Utilities & helpers
│
├── tests/
│   ├── test_cbsi.py
│   ├── test_parameters.py
│   ├── test_ai_models.py
│   └── test_utils.py
│
├── examples/
│   ├── example_cbsi.py
│   ├── example_prediction.py
│   └── example_parameters.py
│
├── results/
│   ├── daily_report_2026-03-xx.txt
│   ├── weekly_report_week12_2026.txt
│   ├── monthly_report_march_2026.txt
│   ├── alerts.log
│   └── coverage_report_2026-03-xx.txt
│
├── docs/
│   ├── conf.py
│   ├── index.rst
│   └── api.rst
│
├── Netlify/
│   ├── index.html
│   ├── dashboard.html
│   ├── reports.html
│   └── documentation.html
│
├── bin/
│   └── run_prediction.py
│
├── scripts/
├── data/
│
├── pyproject.toml
├── requirements.txt
├── requirements-dev.txt
├── Dockerfile
├── Makefile
├── VERSION
├── CITATION.cff
├── AUTHORS.md
├── CHANGELOG.md
├── CONTRIBUTING.md
├── SECURITY.md
├── DEPLOY.md
├── INSTALL.md
└── COMPLETION.md
```

Codebase Statistics

Metric Value
Python modules 6
Test files 4
Dataset 3,847 CCUs
Sites 52
Environment types 5
Time span 14 years (2012–2026)
Governing equations 7+

---

Dataset

Metric Value
Coordination Complexes 3,847 CCUs
Sites 52
Environment Types 5
Time Span 14 years (2012–2026)
Paired Samples 284 intact/damaged pairs
Bond Trajectories 1,840 tracking events

Environment Categories

Environment Sites Pressure Range Temperature Range
Deep-Sea Hydrothermal 11 20–35 MPa 2°C–380°C
Abyssal Plain Cold Water 13 35–110 MPa 1.5°C–4°C
Cryogenic Space Simulation 10 10⁻⁸ Pa vacuum -196°C to -20°C
Radiation-Exposed Orbital 9 Ambient–5 MPa -80°C to +150°C
High-Temperature Autoclave 9 5–30 MPa 300°C–900°C

---

Case Studies

Kermadec Trench (10,900m depth)

· Ni²⁺ maintains Df = 1.88 at 109 MPa
· Fe²⁺ shows higher pressure sensitivity
· CBSI identifies specific engineering interventions

Enceladus Ocean Analog

· 68-hour coordinated impedance burst
· Propagation velocity: 1.8 mm/s
· 22-day warning before visible damage

International Space Station

· Orbital navigation orphaning phenomenon
· σ_nav = 0.62–0.67 (below ground reference)
· Radiation disrupts crystallographic order

---

Citation

```bibtex
@software{baladi2026metal,
  author    = {Samir Baladi},
  title     = {MET-AL: Coordination Bond Stability in Transition Metals
               Under Extreme Environments},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19566418},
  note      = {Physics-Informed AI Framework},
  url       = {https://doi.org/10.5281/zenodo.19566418}
}
```

---

License

MIT License © 2026 Samir Baladi
Ronin Institute / Rite of Renaissance · ORCID 0009-0003-8903-0029

---

"The metal speaks. MET-AL translates. Coordination bond networks are not passive structural elements — they are active information processing systems that sense, integrate, respond to, and transmit information about environmental state across spatial scales from individual bond lengths to macroscopic fracture networks spanning centimeters."

