Ø Machine Learning (ML) Aided Batteries: The AI/ML technique has inbuilt potential to understand the underlying chemistry of the materials and their property towards the applicability of energy storage systems. In this project, we try to understand the contribution of electrolytes from electrochemical properties to the overall health of the batteries using DFT and AI/ML techniques. Our main focus in this project is finding suitable small-molecule liquid electrolytes for high voltage batteries using AI/ML techniques. Furthermore, the interaction energies and their contribution to voltage have been explained using the DFT-ML techniques in metal ion batteries.
More Details: Chem. Mater., J. Mater. Chem. A, Electrochemica. Acta
Ø Electrochemical Window and Electrode Designing: In this project, we try to understand electrolyte stability by considering the important parameters of electrolytes are electrochemical window (ECW) and solid electrolyte interphase (SEI). Here, we have considered both classical molecular dynamics (MD) and ab initio molecular dynamics (AIMD) for the calculation of ECW which helps to fabricate the high voltage batteries. Similarly, AIMD studies have considered allowing the formation of the SEI layer upon the decomposition of electrolyte molecules. Furthermore, we are interested in the electrode designing process for better electrochemical properties based on Dual ion Batteries (DIBs).
More Details: J. Phys. Chem. C, ACS. Applied. Eng. Mater., Mater. Adv., J. Phys. Chem. C
Ø Li-S Batteries: Rechargeable Li-S batteries have been greatly hindered by the severe shuttle effect and sluggish kinetics for practical implication. Exploring the anchoring effect of different soluble polysulfides on the host materials by the chemisorption energy with the help of DFT-ML techniques for extending the battery life.
Exploring the intramolecular band alignment between the inorganic and organic layers in 2D hybrid perovskite halides with the help of DFT methods as well as ML-based classification techniques. Furthermore, our research delves into comprehending the stability of perovskite-water interfaces by using the ab initio molecular dynamics simulation.
More Details: J. Mater. Chem. A, Energy Adv.
We have modeled an AI nanopore by integrating ML strategy with the quantum transport approach to identify nucleotides for high-throughput DNA sequencing, which has numerous applications in genomic research, disease diagnosis, and personalized medicine (Nanoscale, 2023, 15, 18080-18092). Moreover, our research extends to study the change of structural conformation of protein/DNA in biological systems by using large-scale molecular dynamics and ML approaches.
More Details: Nanoscale
Ø Electrocatalysis of ORR/OER Li-O2 Batteries: Exploring the suitable electrocatalyst for the Li-O2 battery, we initiated an investigation into the bimetallic MOF like structure to predict the adsorption energies of various Li-containing oxide intermediates using DFT-ML approach and compared our finding with the existence experimental reports.
More Details: J. Mater. Chem. A
Ø CO2 Hydrogenation Reaction: In this collaborative project, we have investigated the systematic free energy profile diagram to understand the thermodynamics and kinetic favorability of the CO2 reduction.
More Details: ACS Appl. Mater. Interfaces
Ø Nanoclusters: I worked with various experimental groups. In these projects, we have mainly performed detailed theoretical investigations such as UV-visible spectra, and charge transfer of Ag nanocluster, which helps to understand their structural orientations as well as catalytic reaction.
More Details: Chem. Sci., Inorg. Chem., Chem. Sci.
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