Computational pipeline to search for 2D energy storage materials

Research on environment-friendly energy storage devices, mainly lithium-ion batteries (LIB) and supercapacitors, has gained tremendous momentum in recent times due to the emergence of electric vehicles. In the quest for lighter and smaller energy storage devices, electrode materials with high surface-to-mass ratios are greatly desired. Two-dimensional (2D) materials are an ideal fit for such requirements. However, the 2D material space is massive, and despite recent efforts (both experimental and theoretical), only a smallfraction of it has been explored. The limited choice has hindered the promising applications of 2D materials in energy storage devices.

To address this, Arnab Kabiraj and Santanu Mahapatra from the Department of Electronic Systems Engineering report a fully automated, first-principles-based computational framework, which can assess the energy storage capacity of thousands of 2D materials in a high-throughput fashion.

A realistic estimation of the maximum Li-ion storage capacity in any material requires a global-minima-search, which is computationally hard and expensive.The researchers devised four easy-to-calculate descriptors to simplify this problem. These descriptors not only enable rapid screening of materials but also provide important chemical insights (such as the volumetric change of the electrode during charge and discharge).

Their approach also combines explicit-ion and implicit-solvent formalisms to assess a material’s potential for both LIB and supercapacitors, making it useful for a supercapacitor-battery hybrid system, which has been proposed as a viable solution for electric vehicles.

Leveraging the big-data generated by thecomputationalpipeline, the researchers trained crystal graph-based machine learning models and demonstrated how this data-driven model could be helpful for the rapid discovery of potential materials from other databases.

Although many high-throughput studies to scan large material spaces are being reported in recent times, this work stands out for its targeted application, holistic approach, and unique computational techniques. The curated material set is expected to act as a starting point for future experimental efforts.


Kabiraj and Mahapatra, High-throughput assessment of two-dimensional electrode materials for energy storage devices, Cell Reports Physical Science (2021),

Code, Data, ML Pickle: