AI-driven Discovery of Amorphous Fluorinated Polymer Electret with Improved Charge Stability for Energy Harvesting.

Abstract

Electret materials are promising dielectric materials with trapped charges for various applications such as vibration energy harvesters and acoustic transducers. In the present work, we discovered ionization potential as the descriptor to quantify the charging performance for amorphous fluorinated polymer electrets. Using this descriptor, high-throughput computations, and graph neural network models, we screened 1,176,591 functional groups on the cyclic transparent optical polymers (CYTOP) and identified 3 promising electrets. The electrets were synthesized experimentally as 15-μm-thick films. The films were able to keep their both bipolar surface potentials above ±3.1kV for over 1500 hours and are estimated to have longevity of 146 years under 80°C, achieving significant improvements on charging stability among CYTOP-based polymer electrets. The excellent bipolar charging performance can greatly enhance power generation capacity of electret-based vibration energy harvesters. This work also demonstrates the use of deep learning as a new paradigm for accelerating practical materials discovery. This article is protected by copyright. All rights reserved.