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 (ηHP), adaptive thermal resilience (Eₐ), electroacoustic signal density (ρEA), stress-tensor domain navigation fidelity (σₙav), polarization domain fidelity (LDF), depolarization field fractal dimension (Dfrac), 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 ρEA × Dfrac 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·ηHP* + 0. 18·Eₐ* + 0. 17·ρEA* + 0. 14·σₙav* + 0. 13·LDF* + 0. 10·Dfrac* + 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/piezoₓ/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.