Framework for Electrochemical & Electrical Energy Storage Materials Database

TL;DR

This paper introduces a comprehensive multi-dimensional database framework for electrochemical and electrical energy storage materials, enabling detailed comparison and assessment of device performance from experimental and computational data.

Contribution

It presents a novel, detailed framework with 45+ fields for capturing diverse parameters influencing energy storage device performance, integrating experimental and theoretical data for better comparison.

Findings

Framework effectively ranks cell performance based on key metrics

Supports data from experiments and computational predictions

Facilitates comprehensive analysis of energy storage materials

Abstract

Several electrochemical and electrical energy storage devices are reported every day, with the claim of outperforming the established ones. The use of newer materials and recent advanced techniques to synthesize and/or assemble them into a device leads to improved performance. Cyclic stability of a device is the most effective way of assessing the performance of the device. A wide variety of parameters can influence the cyclic stability of a cell, and there is no single fundamental parameter that reliably captures or assesses its overall performance. Therefore, we developed a multi-dimensional assessment framework that could account for various parameters like various types of materials used, selected fabrication techniques, current density, operating voltage window, temperature, environment, and other conditions, and can effectively rank cell performance based on essential assessment metrics like specific capacity and energy density that are substantial for a well-founded comparison. The framework is designed with 45+ fields that capture various details related to i) materials used to fabricate cells, ii) processing techniques associated with electrode preparation and cell assembly, iii) cell information like weights and volumes, iv) electrochemical testing parameters, and v) cyclic charge-discharge performance details of a cell. The framework also accommodates charge-discharge gravimetric specific capacity, which is believed to be a key asset in accurately extracting lots of useful information, such as specific capacity, energy density, quantum efficiency, and other efficiencies. A distinctive feature of the framework is its ability to store data from both experimental and theoretical/computational sources (such as DFT and ML predictions) and facilitate effective comparison between them.