Machine Learning-based Battery Life Detection and Photoelectrode Materials Selection for Lithium Batteries

Abstract

Herein, we developed three-dimensional pristine titanium dioxide (TiO₂) photo-electrocatalyst material (PEM) with homogeneous distribution of oxygen vacancies (OV) for lithium-oxygen (Li-O₂) battery system (denoted as LOBs) under illumination. This rationally designed OV-TiO₂ photoelectrode-catalyst has exhibited excellent capacity, small overpotential, long-term cycle stability, and higher rate capability performance according to our electrochemical experiment study. In short, OV as photoinduced charge separation centers (inert surface atomic modification method) fascinate the effective separation of electrons (e⁻) and holes (h⁺). In turn, induced e⁻ and h⁺ are beneficial to the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) process. More importantly, machine learning (ML) algorithms to analyze and optimize battery performance are innovative in the photoelectrical field. The utility of ML analysis is extensively shown to be effective in learning the in/output connection of interest. Based on ML analysis results, the OV-TiO₂ cathode is indeed the key point to extend the LOB life span. More importantly, our brilliant anatase OV-TiO₂ revealed the optimization of electrode material for high performance and reversibility in LOBs. We expect that it will bring special OV-TiO₂ and some other hierarchical hollow nanomaterials, a big step toward battery technology no matter in cost-effectiveness and environmentally friendly aspects.

Keywords

Machine learning / Oxygen vacancies / Lithium batteries

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Funding

Royal Melbourne Institute of Technology

RIGHTS & PERMISSIONS

The Author(s)