ASME JEECS ‘Emerging Investigators in Electrochemical Energy Conversion and Storage 2022’

We recently published this article in ASME Special Issue on “Emerging Investigators in Electrochemical Energy Conversion and Storage 2022” :

V. Sharma, D. Datta, Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage, ASME Journal of Electrochemical Energy Conversion and Storage, 19(4):041006, 2022

Abstract : Designing a new heterostructure electrode has many challenges associated with interface engineering. Demanding simulation resources and lack of heterostructure databases continue to be a barrier to understanding the chemistry and mechanics of complex interfaces using simulations. Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. This computationally demanding simulation data is further used to develop machine learning (ML)-based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and the transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high-dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root-mean-squared error (RMSE) for test interfaces fall in the range of 0.01–0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite a limited dataset. Improved accuracy in the ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability.

NSF Grant to study Oxide Anodes for High-Performance Aqueous Batteries

We received an NSF grant from CBET Electrochemical System program. Collaborative Research: Fundamental Study of Niobium Tungsten Oxide Anodes for High-Performance Aqueous Batteries

Modern human society requires efficient, affordable and safe means for energy storage. Today, rechargeable lithium–ion batteries dominate the energy storage landscape from portable electronics to the rapidly expanding electric vehicles and electricity (grid) storage applications. However, current lithium-ion batteries suffer from safety and cost issues, primarily because of flammable, moisture-sensitive and expensive organic solvents used in the electrolytes. This project is aimed at replacing the organic solvent electrolyte with water, in a manner that does not compromise on battery performance (i.e., volumetric and gravimetric energy and power density). To accomplish this, the research team proposes to explore new classes of complex oxide (niobium tungsten oxide) materials that will be designed specifically for aqueous battery chemistries, enabling breakthrough improvements in volumetric energy and power density for the next generation of aqueous batteries. This work will contribute to low-cost, high-performance and safe aqueous batteries that are critical for large-scale energy storage. Click Here for more details.