PIEZO-X: Physics-Informed AI Framework for Piezoelectric Energy Harvesting Under Extreme Hydrostatic and Thermal Gradients

PIEZO-X introduces the first physics-informed AI framework for quantitative prediction of piezoelectric energy harvester performance and failure in extreme environments — the Piezoelectric Energy Generation Index (PEGI). Built on seven orthogonal electromechanical descriptors spanning hydrostatic coupling efficiency (η_HP), adaptive thermal resilience (E_a), electroacoustic signal density (ρ_EA), stress-tensor domain navigation fidelity (σ_nav), polarization domain fidelity (LDF), depolarization field fractal dimension (D_frac), and corrosion-induced depolarization inhibition (ADP), PIEZO-X elevates the study of energy harvesting in hostile environments from empirical device testing to rigorous AI-driven predictive science. The framework is validated against a dataset of 4,218 harvester element units (HEUs) spanning 48 experimental chambers and field deployments across five environment categories: deep-sea abyssal plain (35–110 MPa, 1.5–4°C), hydrothermal vent proxy (18–35 MPa, 2–380°C), cryogenic orbital simulation (10⁻⁸ Pa vacuum, −196°C to −20°C), high-temperature industrial autoclave (5–30 MPa, 300–900°C), and radiation-exposed nuclear analog — sampled over a 12-year experimental program (2013–2025). Key results: PEGI achieves 91.7% prediction accuracy (RMSE = 8.3%) in predicting device failure 44 days in advance of macroscopic output collapse. Device failure detection rate: 93.4%. False alert rate: 4.1%. The ρ_EA × D_frac electromechanical intelligence index yields r = +0.911 (p < 0.001, n = 4,218 HEUs). AI ensemble vs. expert materials engineer agreement: 92.8% on 482 held-out HEU-years. The PEGI composite formula: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* The AI ensemble combines a 1D-CNN for electroacoustic time-series classification, XGBoost with SHAP explainability for tabular parameter prediction, and an LSTM with Physics-Informed Neural Network (PINN) penalty layer enforcing energy conservation, thermodynamic consistency, and crystallographic symmetry preservation. Submitted to: npj Computational Materials (Springer Nature) — April 2026.Manuscript ID: PIEZO-X-2026-001.Repository: https://github.com/gitdeeper11/PIEZO-X · https://gitlab.com/gitdeeper11/PIEZO-XPyPI: https://pypi.org/project/piezo_x/Dashboard: https://piezo-x.netlify.appOSF Preregistration: registered April 2026. OSF Preregistration DOI: 10.17605/OSF.IO/PZGQ7 · OSF Project: https://osf.io/vjh2f · Registration Type: OSF Preregistration · Date Registered: April 19, 2026 · Internet Archive: https://archive.org/details/osf-registrations-pzgq7-v1 · License: CC-By Attribution 4.0 International.