📖 Overview
Coordination Bond Stability Index (CBSI)
"The metal speaks. MET-AL translates." — Samir Baladi, March 2026
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 spanning hydrostatic bond compression efficiency, adaptive structural resilience, electrochemical signal density, thermodynamic navigation accuracy, ligand exchange fidelity, topological lattice expansion rate, and corrosion propagation stability.
93.4%
CBSI Accuracy
52-site cross-validation
95.1%
Failure Detection
False alert: 3.8%
38 days
Early Warning
Mean lead time
3,847
CCUs
14 years · 52 sites
CBSI
Coordination Bond Stability Index
CBSI = 0.19·η_HP* + 0.17·E_a* + 0.18·ρ_EC* + 0.14·σ_nav* + 0.13·LXF* + 0.11·K_latt* + 0.08·ACI*
CBSI_adj = σ(CBSI_raw + β_env + β_thermal)
from met_al_science import CBSI, CBSIParameters
params = CBSIParameters(
eta_hp=0.74, ea=0.67, pec=0.57,
sigma_nav=0.73, lxf=0.91, klatt=1.74, aci=0.43
)
cbsi = CBSI.compute(params)
7 Parameters
Seven Physico-Chemical Descriptors
| Parameter | Description | Weight | Domain |
|---|
| η_HP | Hydrostatic Pressure Compression Efficiency | 19% | High-Pressure Chemistry |
| E_a | Adaptive Structural Resilience Index | 17% | Mechanical Dynamics |
| ρ_EC | Electrochemical Signal Density | 18% | Electrochemistry |
| σ_nav | Stress-Tensor Navigation Accuracy | 14% | Tensor Mechanics |
| LXF | Ligand Exchange Fidelity | 13% | Coordination Chemistry |
| K_latt | Topological Lattice Expansion Rate | 11% | Fractal Crystallography |
| ACI | Corrosion Propagation Inhibition Index | 8% | Materials Degradation |
AI Architecture
Physics-Informed Neural Network
u_i(t) = -α_i·ρ_eff,i(t)·tanh(β_i·Δ_i(t))·φ_i(t)
// • Thermodynamic consistency (ΔG < 0)
// • Mass conservation (dissolution = measured loss)
// • Symmetry preservation (crystallographic point group)
from met_al_science import MetalPredictor
predictor = MetalPredictor()
result = predictor.predict_from_parameters(params)
Validation Scope
Five Extreme Environments
95.2%
Deep-Sea Hydrothermal
20–35 MPa · 2–380°C · 11 sites
94.6%
Abyssal Plain Cold
35–110 MPa · 1.5–4°C · 13 sites
92.1%
Cryogenic Space
10⁻⁸ Pa · -196 to -20°C · 10 sites
91.4%
Radiation Orbital
Ambient–5 MPa · -80 to +150°C · 9 sites
93.8%
High-Temp Industrial
5–30 MPa · 300–900°C · 9 sites
📦 Installation
Quick setup
pip install met_al_science
git clone https://github.com/gitdeeper10/MET-AL.git
cd MET-AL
pip install -e .
python -c "from met_al_science import __version__; print(__version__)"
🔧 API Reference
Python interface
CBSIParameters
Seven physico-chemical descriptor container
from met_al_science import CBSIParameters
params = CBSIParameters(
eta_hp=0.74, ea=0.67, pec=0.57,
sigma_nav=0.73, lxf=0.91, klatt=1.74, aci=0.43
)
CBSI
Coordination Bond Stability Index computation
from met_al_science import CBSI
cbsi = CBSI.compute(params, env_type='deep_sea_hydrothermal')
MetalPredictor
AI predictor with PINN constraints
from met_al_science import MetalPredictor
predictor = MetalPredictor()
result = predictor.predict_from_parameters(params)
🧩 Core Modules
MET-AL architecture
parameters.py
7 Parameters
η_HP, E_a, ρ_EC, σ_nav, LXF, K_latt, ACI
cbsi.py
CBSI
Composite formula + corrections
ai_models.py
PINN
Physics-Informed Neural Network
data_loader.py
Data
3,847 CCU dataset loader
thresholds.py
Thresholds
Adaptive CBSI thresholds
utils.py
Utils
Statistics & helpers
👤 Author
Principal investigator
⚙
Samir Baladi
Interdisciplinary AI Researcher — Transition Metal Systems & Computational Materials Science Division
Ronin Institute / Rite of Renaissance
Samir Baladi is an independent researcher affiliated with the Ronin Institute, developing the Rite of Renaissance interdisciplinary research program. MET-AL is a physics-informed AI framework for coordination bond stability in transition metals under extreme environments, integrating high-pressure crystallography, electrochemical impedance spectroscopy, DFT computation, and PINN architecture.
No conflicts of interest declared. All code and data are open-source under MIT License.
📝 Citation
How to cite
@software{baladi2026metal,
author = {Samir Baladi},
title = {MET-AL: Coordination Bond Stability in Transition Metals
Under Extreme Environments},
year = {2026},
version = {1.0.0},
publisher = {Zenodo},
doi = {10.5281/zenodo.19566418},
url = {https://doi.org/10.5281/zenodo.19566418},
note = {Physics-Informed AI Framework}
}
"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."