Using multiscale methodology consisting of MD, DFT and KMC techniques, I discovered that dsDNA exhibits superior conductance in both the charge transport regimes, i.e. the hopping and the tunneling CT regime (Aggarwal et al., Nanoscale, 2020). Additionally, by investigating the effect of stretching dsRNA on its structure and CT properties, I revealed the striking similarity between structural polymorphism in dsRNA and dsDNA under different pulling protocols. These conformational variations significantly affect conductance, presenting potential applications in detecting structural changes.

To overcome the challenge of studying the conductance properties of every possible sequence and length of dsDNA, I harnessed the power of machine learning. Through my research, I developed a neural network model trained on a vast dataset using the Coulomb matrix representation of DNA base pairs. This model can accurately predict electronic couplings between dsDNA base pairs with any structural orientation, achieving an impressive mean absolute error of less than 0.014 eV (Aggarwal et al., JCIM 2021). This advancement in predictive modeling opens new possibilities for rapid and efficient exploration of DNA conductance.

During the global pandemic of COVID-19, I decided to utilize my skillset to understand the leading cause of higher transmissibility of some mutated variants of SARS-CoV-2 virus than others. Through extensive MD simulations and advanced free energy sampling simulations, I characterized the molecular details of the interaction between the RBD and ACE2, providing insights into viral infectivity and potential therapeutic interventions (Aggarwal et al., PCCP, 2021). 

Currently, I am tackling the energy storage challenges by modeling the electrode-electrolyte interface and computing the free energy profiles of Li-ion adsorption at the electrode-electrolyte interface. I have also modelled the polymer electrolyte systems with different levels of salt concentrations and studied the effect of different solvation environments of Li+ ion diffusivity. I have developed a first-of-its-kind computational model to track the solvation environment of each Li+ ion which can be directly linked to the battery performance, further accompanied by guidelines to design molecular environments for Li+ ions. This research showcases my ability to quickly learn, adapt and apply computational techniques to different challenging problems.