Research

Computational Design of Electrode Materials for Energy Storage

The computational design of electrode materials for energy storage is the primary focus of my study. Rechargeable batteries, CO2reduction, and photoactive materials are all included. Using DFT calculations and MD (ab-initioand classical) simulations, I have investigated a variety of carbon-based 2D materials as prospective electrode materials for rechargeable batteries. I also looked at the photo-actuating properties of organic molecular crystals and the catalytic activity of double halide perovskites for the reduction of CO2. Identifying materials that can contribute to green energy for the future is my main goal.

Prepared by - Abhijitha

Analytical and Computational Studies of Spin-Orbit Coupled Systems

Analytical and computational studies of spin-orbit coupled systems are at the forefront of quantum physics research, delving into the intricate interplay between electron spin and orbital motion. This field provides a fascinating journey through the quantum realm, where theoretical frameworks and computational tools converge to unveil the hidden dynamics of these systems. Starting with a foundational understanding of spin-orbit coupling, researchers dive into the theoretical underpinnings, including the Dirac equation, which forms the basis for describing relativistic electron behavior. The real-world significance of spin-orbit coupling becomes evident through experimental observations across various domains, from atomic and molecular structures to complex condensed matter systems.

Prepared by - Pratik Kumar Sahu

Quantum Materials

We study the quantum phenomena emerging out of van der Waal and oxide family that arise due to various broken symmetry and complex interplay among the competing many-body interactions such as hopping, Coulomb repulsion, spin-orbit coupling, Hund’s coupling, Heisenberg, Dzyaloshinskii-Moriya interaction, etc. by employing ab-initio electronic structure and many body model Hamiltonian calculations. We also study the effect of doping and disorder on the transport physics of the aforementioned family of materials. Our theoretical and computational studies include quantum phenomena like magnetic properties for both collinear and noncollinear spin system, unusual electron transport properties such as anomalous, orbital, and spin Hall effect, topologically protected real space spin textures like skyrmions, etc. which will enrich the area of next generation quasi-particle based devices like spintronics, orbitronics, topotronics, racetrack memory device, and quantum computation.

Prepared by - Arijit Mandal

Electronic Structure and Magnetic Properties of Strongly Correlated Perovskite Systems

The perovskite systems have been attaining huge interest for their intriguing electronic and magnetic properties, that arise due to the strong interaction among spin, orbital, charge and lattice degrees of freedom. My research interest broadly lies on to investigate the electronic structure and magnetic properties of oxide systems in general and A-site ordered perovskite systems in particular. For this purpose, I use first principles electronic structure calculations within the framework of the density functional theory.

Prepared by - Jatin Kumar Bidika

Machine Learning Potentials

Welcome to Machine Learning Potentials (MLPs)! MLPs combine quantum mechanics-based ab initio molecular dynamics (AIMD) with classical molecular dynamics (CMD), offering AIMD-level accuracy at the computational speed, length, and time scales of CMD. They learn the potential energy surface from reference data at density functional theory (DFT) precision, representing atomic coordinates and species as 'fingerprints' for machine learning models like neural networks (NNs) and kernels. MLPs serve as force fields in large-scale molecular dynamics simulations, known as machine-learned molecular dynamics (MLMD), enabling the study of complex molecular systems at extended length and time scales with DFT-level accuracy.

Prepared by - Babuji Dandigunta

Computational Designing of Perovskite Solar Cells

The innovative realm of computational design for Perovskite solar cells, where we harness cutting-edge techniques to optimize both their optical and electrical behavior. In this captivating journey, we explore advanced design strategies that leverage computational power to fine-tune the structure and properties of Perovskite solar cells, unlocking their maximum efficiency potential. Central to our exploration is the fascinating concept of Whispering Gallery Modes (WGM), which enhances light-matter interactions within these materials. Through striking images and simulations, we showcase how WGM elevates light absorption and trapping, thereby significantly improving the optical aspects of these solar cells. Additionally, we delve into the intricacies of electrical behavior enhancement, revealing insights and simulations that boost charge carrier dynamics. The synergy between computational design and WGM leads to remarkable efficiency breakthroughs in Perovskite solar cell technology, setting the stage for a sustainable energy future. Our website keeps you abreast of the latest research findings, methodologies, and experimental validations driving this dynamic field, while also fostering engagement within a community of researchers, engineers, and renewable energy enthusiasts passionate about shaping the future of solar energy.

Prepared by - Ayusmin Panda

Electronic Structure of Solids

We solve the Hamiltonian of interacting electrons to provide the theoretical description of the properties of solids. Generally, we use the mean-field based methods such as DFT and tight-binding model Hamiltonian study for this purpose. The primary component of my research is to understand the interplay among the spin, charge, and lattice degrees of freedom of electron in crystalline solids. The focus of our work is to examine the symmetry driven non-trivial quantum phases in spin-orbit coupled systems. We have developed model Hamiltonians for spin-orbit coupled, polar and non-polar halide perovskites and double perovskite family, which helps to understand the emerging tools for topology, optoelectronics, and spintronics applications.

Prepared by - Mayank Gupta

Two-Dimensional Material Modelling

The aim is to exploring the physical properties of novel two-dimensional (2D)materials using advanced first-principles calculations in the framework of density functional theory (DFT). 2D materials have an atomic thickness. Because of this reduction of the dimensions, quantum confinement effects will have an important influence on the properties of the material. The field of 2D materials is expanding with the continued introduction of new members and the new phenomena they bring. Currently, the research has transitioned towards perovskite materials, where we engaged in a detailed study of their optoelectronic properties. Leveraging GW-BSE calculations we are trying to observe excitonic effects in the perovskite systems. Exciton binding energies found in these perovskites is pushing the limit of our understanding of the electron-hole interaction and exciton formation in solids. Spin-orbit coupling effects play a crucial role in the mixing up spin-singlet and spin-triplet excitons. This exciting endeavor represents a captivating avenue in the quest to uncover novel materials with extraordinary properties.

Prepared by - Susmita Jana

2D - Dimensional Topological Semimetals

We dedicate our time to exploring 2D topological semimetals, which are exceptional, adaptable, and premier electrode materials for both 2D-2D and 2D-3D heterogeneous electronics. Additionally, we investigate the dynamics of electrons and other charge carriers, as well as their scattering events, either directly or indirectly, in these topological semimetals when a voltage bias is applied in the presence of a magnetic field across various temperature ranges. Furthermore, we use density functional theory to examine the electronic structures of stacked 2D material heterostructures to gain insights into their optical and electrical transport properties.

Prepared by - Bubunu Biswal

Quantum Materials and Machine Learning

We study the magnetic, transport, and optical properties emerging from the various family of quantum materials such as perovskite, pyrochlore lattice, etc. using first principle calculation and many body model Hamiltonian. We develop a machine learning model to study the parameter based properties of double perovskite materials like variation of band gap with fundamental features containing electronegativity, ionization potential, etc. We are also planning to explore magnetic behavior of correlated electron system with the competition between hopping and onsite repulsion strength using machine learning method.

Prepared by - Arindom Das

Unique dxy superconducting state in the cuprate Ba2CuO3+δ

The discovery of superconductivity at a transition temperature of 73K in the doped layered compound Ba2CuO3+x for x∼0.2 has sparked significant interest. Experiments like XRD, RIXS reveal a unique structure where compressed octahedra position the Cu-dz2 orbital above the Cu-dx2−y2 orbital, unlike traditional cuprate superconductors. Using first-principles calculations and multi-band spin fluctuation theory, we demonstrate that strong Fermi surface nesting induces a dxy-wave superconducting state in Ba2CuO3+x.

Prepared by - Priyo Adhikary

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