Framework for Electrochemical & Electrical Energy Storage Materials Database

TL;DR

The paper addresses the challenge of inconsistent reporting and comparability across electrochemical and electrical energy storage studies. It presents a multidimensional framework with 45+ fields that capture materials, processing, testing, and cyclic performance, and supports data from both experiments and first-principles computations, with device-level normalization to the widely used $C_{18650}$ standard. A Unique Record Identifier (URI) and structured searching enable ranking by gravimetric capacity, energy density, and related metrics, providing a robust tool to resolve inconsistencies and accelerate material discovery. The framework is implemented as an online platform (power.tattvasar.com) to facilitate querying by materials, testing conditions, and cyclic performance, and the authors invite community contributions to expand the dataset and improve downstream tools.

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.

Figures (2)

• Figure 2: Style of ID representing a unique record in the Database.
• Figure 3: Overall structure showing (a) data classification and (b) record identification process.