PIEZO-X

📖 Overview

Piezoelectric Energy Generation Index (PEGI)

"The domain speaks. PIEZO-X translates." — Samir Baladi, April 2026

PIEZO-X introduces the first physics-informed AI framework for quantitative characterization of electromechanical energy conversion in piezoelectric materials operating under extreme environmental conditions — the Piezoelectric Energy Generation Index (PEGI). Built on seven orthogonal electromechanical descriptors spanning hydrostatic coupling efficiency, adaptive thermal resilience, electroacoustic signal density, stress-tensor navigation fidelity, polarization domain fidelity, depolarization field fractal dimension, and corrosion-induced depolarization inhibition.

91.7%

PEGI Accuracy

48-site cross-validation

93.4%

Failure Detection

False alert: 4.1%

44 days

Early Warning

Mean lead time

4,218

HEUs

12 years · 48 sites

PEGI

Piezoelectric Energy Generation Index

// PEGI Composite Formula (Equation 3.1 from paper) PEGI = 0.21·η_HP* + 0.18·E_a* + 0.17·ρ_EA* + 0.14·σ_nav* + 0.13·LDF* + 0.10·D_frac* + 0.07·ADP* // AI Correction with Environment Bias (Equation from paper Section 4.3) PEGI_adj = σ(PEGI_raw + β_env + β_thermal + β_rad) // Python implementation from piezo_x import PEGIParameters, compute_pegi params = PEGIParameters( eta_hp=0.28, e_a=0.84, rho_ea=0.31, sigma_nav=0.73, ldf=0.88, d_frac=1.84, adp=0.41 ) result = compute_pegi(params, environment='deep_sea_abyssal')

7 Parameters

Seven Electromechanical Descriptors

Parameter	Description	Weight	Domain
η_HP	Hydrostatic Coupling Efficiency	21%	High-Pressure Electromechanics
E_a	Adaptive Thermal Resilience Coefficient	18%	Thermomechanical Dynamics
ρ_EA	Electroacoustic Signal Density	17%	Electroacoustic Analysis
σ_nav	Stress-Tensor Domain Navigation Fidelity	14%	Tensor Mechanics
LDF	Polarization Domain Fidelity	13%	Ferroelectric Domain Analysis
D_frac	Depolarization Field Fractal Dimension	10%	Fractal Crystallography
ADP	Corrosion-Induced Depolarization Inhibition	7%	Materials Degradation

AI Architecture

Physics-Informed Neural Network

// PINN penalty layer constraints (from paper Section 4.3) // • Energy conservation: electrical_output ≤ mechanical_input - losses // • Thermodynamic consistency: ΔG < 0 for spontaneous depolarization // • Symmetry preservation: domain configurations respect crystallographic point group // Python implementation from piezo_x import PiezoXPredictor predictor = PiezoXPredictor() result = predictor.predict(electroacoustic_data, current_params)

Validation Scope

Five Extreme Environments

93.3%

Deep-Sea Abyssal Plain

35–110 MPa · 1.5–4°C · 12 sites

94.1%

Hydrothermal Vent Proxy

18–35 MPa · 2–380°C · 10 sites

90.4%

Cryogenic Orbital Simulation

10⁻⁸ Pa · -196 to -20°C · 10 sites

92.6%

High-Temp Industrial Autoclave

5–30 MPa · 300–900°C · 9 sites

89.2%

Radiation-Exposed Nuclear Analog

Ambient–5 MPa · -40 to +180°C · 7 sites

📦 Installation

Quick setup

# Clone repository git clone https://github.com/gitdeeper11/PIEZO-X.git cd PIEZO-X # Run prediction python bin/run_prediction.py --environment deep_sea_abyssal # Verify installation python -c "from piezo_x import __version__; print(__version__)"

🔧 API Reference

Python interface

PEGIParameters

Seven electromechanical descriptor container

from piezo_x import PEGIParameters params = PEGIParameters( eta_hp=0.28, e_a=0.84, rho_ea=0.31, sigma_nav=0.73, ldf=0.88, d_frac=1.84, adp=0.41 )

compute_pegi

PEGI computation with environment-specific normalization

from piezo_x import compute_pegi result = compute_pegi(params, environment='deep_sea_abyssal') print(result.value) # PEGI value print(result.status) # CRITICAL/WARNING/MODERATE/GOOD/EXCELLENT

PiezoXPredictor

AI predictor with PINN constraints and SHAP explanation

from piezo_x import PiezoXPredictor predictor = PiezoXPredictor() prediction = predictor.predict(electroacoustic_data, current_params) print(prediction.days_to_failure) # Early warning days

🧩 Core Modules

PIEZO-X architecture

parameters.py

7 Parameters

η_HP, E_a, ρ_EA, σ_nav, LDF, D_frac, ADP

pegi.py

PEGI

Composite formula + corrections

predictor.py

Predictor

AI prediction with PINN

monitor.py

Monitor

Real-time monitoring system

ai/

AI Models

CNN, XGBoost, LSTM, PINN

bin/

CLI

run_prediction.py, reports

👤 Author

Principal investigator

⚡

Samir Baladi

Interdisciplinary AI Researcher — Electromechanical Systems & Computational Energy 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. PIEZO-X is a physics-informed AI framework for piezoelectric energy harvesting under extreme environments, integrating high-pressure crystallography, electroacoustic impedance spectroscopy, DFT computation, and PINN architecture.

No conflicts of interest declared. All code and data are open-source under MIT License.

📧 gitdeeper@gmail.com 🔗 ORCID: 0009-0003-8903-0029 🐙 GitHub 🦊 GitLab

📝 Citation

How to cite

@software{baladi2026piezox, author = {Samir Baladi}, title = {PIEZO-X: Piezoelectric Energy Harvesting Under Extreme Hydrostatic and Thermal Gradients}, year = {2026}, version = {1.0.0}, publisher = {Zenodo}, doi = {10.5281/zenodo.19637804}, url = {https://doi.org/10.5281/zenodo.19637804}, note = {Physics-Informed AI Framework} }

"Piezoelectric domain networks are not passive transducers — they are active information processing systems that sense, integrate, respond to, and transmit information about environmental state across spatial scales from individual domain walls to macroscopic electrode surfaces with 91.7% accuracy."