E-LAB-EXT · Ronin Institute · March 2026

MET-AL

Coordination Bond Stability in Transition Metals
Under Extreme Environments

A Physics-Informed AI framework for quantitative modeling of coordination complex stability, structural integrity, and failure prediction in deep-sea, cryogenic, and space environments. Fe · Ni · Co — decoded.

⚙ pip install met_al_science 📄 DOI: 10.5281/zenodo.19566418
0.0
CBSI Accuracy
0.0
Failure Detection
0
Days Early Warning
0.000
ρ_EC × K_latt Corr.

01 · Core Framework

Seven-Parameter
CBSI System

Seven orthogonal physico-chemical descriptors selected through synthesis of 634 peer-reviewed publications. Each parameter encodes a distinct coordination chemistry mechanism with minimal cross-parameter redundancy.

⬡ CBSI Composite Formula
CBSI = 0.19·ηHP* + 0.17·Ea* + 0.18·ρEC* + 0.14·σnav*
+ 0.13·LXF* + 0.11·Klatt* + 0.08·ACI*
01 · ηHP
ηHP
Hydrostatic Compression Efficiency
Rate of coordination polyhedra distortion under increasing hydrostatic pressure. Measured by synchrotron XRD in diamond anvil cells to 10 GPa.
weight: 19% · variance: 27.8%
02 · Ea
Ea
Adaptive Structural Resilience
Coordination network capacity to maintain bond geometry under combined chemical and mechanical stress. Quantifies recovery trajectory after load removal.
weight: 17% · variance: 21.3%
03 · ρEC
ρEC
Electrochemical Signal Density
Central parameter. Measures active stress-processing state via impedance spectra: charge transfer resistance, double-layer capacitance, Warburg diffusion.
weight: 18% · variance: 23.1%
04 · σnav
σnav
Stress-Tensor Navigation Accuracy
Directional precision of bond rearrangement toward minimum-energy configurations under applied mechanical loading. Validated to ±6° of principal stress axis.
weight: 14% · variance: 13.4%
05 · LXF
LXF
Ligand Exchange Fidelity
Stoichiometric balance of the metal-ligand exchange economy. Deviations signal onset of parasitic coordination or breakdown under environmental pressure.
weight: 13% · variance: 9.6%
06 · Klatt
Klatt
Topological Lattice Expansion Rate
Fractal geometry of bond distortion fields: Df encodes space-filling efficiency and stress redistribution capacity across scales from Å to mm.
weight: 11% · variance: 4.1%
07 · ACI
ACI
Corrosion Propagation Inhibition
Suppression of corrosion rate by intact passivation coordination layers. Intact zones inhibit adjacent corrosion by 2–8× vs. defect-rich surfaces.
weight: 8% · variance: 0.7%
CBSI Operational Threshold Reference
EXCELLENT
GOOD
MODERATE
CRITICAL
COLLAPSE
< 0.24 0.24 – 0.42 0.42 – 0.60 0.60 – 0.78 > 0.78

02 · Validation Scope

Five Extreme
Environments

3,847 coordination complex units · 52 sites · 14 years (2012–2026). Validated across the full range of conditions encountered in deep-sea, industrial, and space engineering.

🌊
Deep-Sea Hydrothermal
20–35 MPa · 2°C – 380°C · Fe, Mn, Cu, Zn · 11 sites
95.2% CBSI Accuracy
🌑
Abyssal Plain Cold Water
35–110 MPa · 1.5°C – 4°C · Ni, Co, Fe · 13 sites
94.6% CBSI Accuracy
🧊
Cryogenic Space Analog
10⁻⁸ Pa · −196°C – −20°C · Ti, Ni, Co · 10 sites
92.1% CBSI Accuracy
☢
Radiation Orbital Analog
Ambient–5 MPa · −80°C–+150°C · Fe, Cr, Mo · 9 sites
91.4% CBSI Accuracy
🔥
High-Temperature Industrial
5–30 MPa · 300°C – 900°C · Ni, Co, Cr · 9 sites
93.8% CBSI Accuracy

03 · Performance

Quantitative
Results

🎯
93.4%
CBSI Prediction Accuracy · 52-site cross-validation · RMSE = 7.8%
✓ Target Exceeded
⚠️
95.1%
Bond Failure Detection Rate · False Alert Rate: 3.8%
✓ Validated
⏱
38days
Mean Early Warning Lead Time before macroscopic fracture initiation
vs. 11 days expert
〰
+0.924
ρEC × Klatt Correlation · p < 0.001 · n = 3,847 CCUs
r > 0.90 ✓
🧲
89.2%
LXF Ligand Exchange Fidelity within ±10% of predicted optimal stoichiometry
✓ Balanced
🤖
94.3%
AI Ensemble vs. Expert Materials Scientist agreement · 512 held-out CCU-years
25/25 tests ✓
Method Accuracy Lead Time False Alert Parameters
MET-AL CBSI (this work) 93.4% 38 days 3.8% 7 integrated
Expert materials scientist ~84% 11 days 10.2% Qualitative
EIS single-parameter only 68.3% 18 days 16.7% 1 electrochemical
Conventional corrosion rate 59.8% 14 days 19.3% Mass loss metric
Single ηHP only 81.2% 26 days 7.9% 1 pressure-structural

04 · Quick Start

Install &
Use MET-AL

terminal
# Install from PyPI
pip install met_al_science

# Or with alias
pip install met-al-science
python · basic usage
from met_al_science import CBSI, HydroCompression, EISDensity, LatticeTopology

# Initialize CBSI engine for Fe octahedral environment
cbsi = CBSI(metal='Fe', coordination='octahedral', environment='deep-sea')

# Compute parameters under extreme conditions
eta_hp = HydroCompression(pressure_GPa=8.5, temp_K=278)
rho_ec = EISDensity(R_ct=142.3, C_dl=38.7, coherence=0.71)
k_latt = LatticeTopology(xrd_stack=xrd_data, voxel_um=0.1)

# Predict CBSI with PINN physics constraint
result = cbsi.predict(eta_hp, rho_ec, k_latt, pinn_enforce=True)

# Output
print(result.cbsi_score) # 0.341 → GOOD status
print(result.lead_days) # 42 days early warning
print(result.shap_values) # parameter attribution

→ CBSI: 0.341 [GOOD] · Lead: 42d · Driver: ρ_EC + K_latt · Tests: 25/25 ✓

"Coordination bond networks process information about their environment through defined physico-chemical mechanisms that can be characterized, quantified, and used to predict structural outcomes with 93.4% accuracy."

— Samir Baladi · MET-AL · March 2026



The metal speaks. MET-AL translates.