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Our research focuses on using first principles computational modeling techniques like density functional theory (DFT) and many-body perturbation theory (MBPT) to study and design novel semiconductors. We leverage Quantum Mechanics laws to calculate material properties without empirical assumptions, covering structural, vibrational, electronic, magnetic, transport, and optical aspects. Our expertise includes automated high-throughput screening and analytical modeling to accelerate materials discovery. Key areas of focus include computational condensed matter physics, renewable energy materials, and quantum materials, particularly complex organic and inorganic phases. We heavily employ contemporary DFT-based approaches, aiming to predict material properties accurately without empirical parameters. Additionally, we explore correlated quantum state's role in charge, spin, and heat transport, with potential applications in spin-based electronics and quantum sensing. Our theoretical work benefits from collaboration with experimental efforts at IIT Ropar, national and international collaborations.
Nanoscience and Computational Material Chemistry, Graphene, Semiconductor Nanostructures, Materials for Renewable Energy Applications, Li-ion and Na-ion Batteries, Novel Batteries Technology, 2D Material Chemistry, Solar Energy Conversion, Solar Cells, Molecular and Nano-scale Magnetic Materials, Molecular Modeling, Multi-Scale Modeling, Energy and Charge Transfer in Prediction of Novel Properties in Nanoclusters, Nanotubes and Nano-sheets of Carbon-Based Materials.
Materials for storage:
Our research focuses on energy storage, particularly in rechargeable batteries and hydrogen storage. For rechargeable batteries, we employ DFT, random structural search, and machine learning techniques to address challenges in electrode modeling, alkali ion insertion capacity, and cathode material oxidation during charging-discharging processes, which significantly impacts battery lifetime. In hydrogen storage, we investigate chemisorption processes for atomic hydrogen storage, focusing on understanding H2 dissociation and physisorption via weak vdW binding. We address challenges such as electron self-interaction errors and accurate prediction of binding sites and strengths between H2 and host materials. We also explore "Beyond Li-ion" battery chemistries like Li-Air, Li-S, etc., employing computational chemistry for electrolyte decomposition, molecular dynamics simulations, high-throughput screening, and machine learning for novel cathode materials and electrolyte formulations, and multiphysics simulations of cells and packs.
[1] Singh et al. Journal of Physics: Energy 3, 012005 (2020)
[2] Gond et al. Inorganic chemistry 56, 5918-5929 (2017)
Jeong et al. Nature 592, 381 (2021)
Wang et al. Nature Communications 10,129 (2019)
Shukla et al. Phys. Chem. Chem. Phys. 20 (35), 22952 (2018)
Materials design:
An Edisonian laboratory exploration (i.e. growth + characterization) via trial-and-error processes of many candidate materials is considered impractical these days. High-throughput computing is cost effective way to design new materials and investigate their properties. Which can further be complemented with statistical methods such as data driven approach, supervised and unsupervised machine learning methods. One of the example ideas is to understand the structure-property relations in bulk and layered hybrid-perovskites, oxides, 2D materials, and nanostructure. Our focus is theoretical and computational research in materials, particularly to enable a better understanding of experiments.
Magnetism and spintronics:
We perform large-scale computational studies of bulk and layered materials towards spintronics applications. In particular, we study spin textures, spin-to-charge conversion and coupling between different degrees of freedom, based on the ab-initio calculations and symmetry analysis for hundreds of existing and hypothetical crystals. We aim on finding materials that combine multiple functionalities, useful to design all-in-one devices for spintronics and nanoelectronics. Finally, we study van der Waals heterostructures, also including exotic phenomena emerging from their moiré patterns.
Phys. Rev. Materials 8, 074403 (2024)
Manuscript (In Preparation)
Physical Review Materials 6 (11), 116001 (2022)
ACS Applied Electronic Materials 3 (2), 733-742 (2021)
Quantum transport in nanoscale devices:
We focus on predicting quantum transport properties of complex systems using the TranSiesta code. Our work includes exploring single molecular rectifiers, length-dependent thermal and electronic conductance in molecular wires, as well as pristine and defected 2D materials and nano-junctions. These materials hold promise for future electronic devices, addressing challenges in semiconductor chip miniaturization. We investigate the impact of defects and impurities on material properties for technological applications. Utilizing the Landauer formula and NEGF method, we analyze transport properties, including in solutions for improved biomolecule and gas detection.
Method development and benchmarking:
The choice of exchange-correlation (xc) functional significantly impacts the accuracy of DFT calculations. In condensed matter, various interatomic forces, notably vdW forces, are crucial, particularly in sparse matter like biomolecular, heterostructures, and vdW interfaces. GGAs often perform inadequately in these cases, prompting the development of semi-empirical corrections. The vdW-DF method offers a systematic, nonlocal approximation for xc energy functional in DFT, demonstrating effectiveness in addressing perovskites, ferroelectric polymer polarization, magnetic crystals, and biomolecular problems. Recent introduction, range-separated hybrids (RSH), surpasses dispersion corrected xc functionals. Our current research focuses on benchmarking xc functionals against experiments and high-level theory, and employing machine learning to enhance accuracy.
Journal of Physics:Condensed Matter 34, 025902 (2021)
Physical Review X 6 (11), 116001 (2022)
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