AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries

TL;DR

This paper presents a machine learning framework that accelerates the discovery and optimization of redox-active organic materials for batteries, aiming to replace critical metals and improve sustainability in energy storage.

Contribution

It introduces a data-fusion meta learning model that predicts battery properties, enabling faster design and selection of organic electrode materials.

Findings

The ML model accurately predicts voltage and capacity for various organic electrode combinations.

The framework identifies promising organic materials from large libraries for sustainable batteries.

Accelerates experimental workflows and inverse design of battery components.

Abstract

The use of transition group metals in electric batteries requires extensive usage of critical elements like lithium, cobalt and nickel, which poses significant environmental challenges. Replacing these metals with redox-active organic materials offers a promising alternative, thereby reducing the carbon footprint of batteries by one order of magnitude. However, this approach faces critical obstacles, including the limited availability of suitable redox-active organic materials and issues such as lower electronic conductivity, voltage, specific capacity, and long-term stability. To overcome the limitations for lower voltage and specific capacity, a machine learning (ML) driven battery informatics framework is developed and implemented. This framework utilizes an extensive battery dataset and advanced ML techniques to accelerate and enhance the identification, optimization, and design of redox-active organic materials. In this contribution, a data-fusion ML coupled meta learning model capable of predicting the battery properties, voltage and specific capacity, for various organic negative electrodes and charge carriers (positive electrode materials) combinations is presented. The ML models accelerate experimentation, facilitate the inverse design of battery materials, and identify suitable candidates from three extensive material libraries to advance sustainable energy-storage technologies.