Thermoelectric Materials and Energy Conversion
Symmetry Breaking in Janus Thermoelectric Materials
Investigates how structural asymmetry and interlayer coupling in Janus MSe₀.₅Te₀.₅ materials influence thermal transport and electronic properties, offering strategies for improving thermoelectric performance.
Engineering Heterostructures and Superlattices
Explores layered combinations of materials such as MSe/MTe, HfSe₂/SnX₂, ZrSe₂/HfSe₂, and Sn₀.₅Pb₀.₅O to improve thermoelectric properties through interface effects, band alignment, and modified phonon transport.
Ultralow Lattice Thermal Conductivity
Examines phonon scattering, localization, softening, and anharmonic lattice vibrations as mechanisms for suppressing heat conduction while retaining favorable electronic transport.
Conductive Networks in Half-Heusler Compounds
Studies how elemental substitution, vacancies, interstitial cobalt, and atomic off-centering modify electronic pathways and lattice vibrations in NbCoSb-based materials to enhance thermoelectric performance.
Band Engineering and Carrier Transport Optimization
Investigates band convergence, valley degeneracy, orbital interactions, strain, and chemical substitution as approaches to improve carrier transport and thermoelectric efficiency in two-dimensional and bulk materials.
Two-Dimensional Materials and Electronic Structure Engineering
Janus Materials and Rashba Spin Splitting
Investigates how broken structural symmetry in Janus monolayers and layered gallium chalcogenides influences electronic band splitting, carrier mobility, and thermal transport.
Electronic Bandgap Prediction and Material Design
Combines machine-learning-assisted methods with first-principles calculations to predict bandgaps in functionalized MX₂-based materials, supporting computational screening and the design of semiconductors.
Strain Engineering and Valley Degeneracy
Examines how mechanical strain modifies valley degeneracy and thermoelectric properties in two-dimensional Janus ferromagnetic LaBrI, highlighting the role of electronic structure in energy conversion.
Optical Properties, Nanomaterials and Carrier Dynamics
Tuning Chiroptical Properties of Hybrid Perovskites
Explores how organic spacer molecules and halide composition influence the chiroptical properties of two-dimensional hybrid perovskites, providing insights into composition-dependent optical functionality.
Lanthanide-Doped Gold Nanocluster Assemblies
Investigates dopant-directed size evolution, symmetry-dependent europium emission, and selective chemical recognition in gold nanocluster assemblies, connecting nanoscale structure with optical and sensing properties.
Ultrafast Carrier Cooling and Relaxation Dynamics
Examines how heterostructure interfaces, phonon scattering, charge delocalization, and heterovalent doping affect carrier cooling and relaxation in CsPbBr₃-based nanocrystals and heterostructures.
Defect Engineering, Chemical Substitution and Functional Oxides
Defect and Dopant Engineering
Studies how vacancies, elemental substitution, and dopant incorporation influence transport properties in compounds such as NbCoSb, Zr₀.₅Hf₀.₅NiSn, Ag₂Se, and SnTe, identifying compositional strategies for functional-property optimization.
Doping-Induced Structural and Dielectric Modulation
Investigates the effects of neodymium doping on the polymorphic behavior and dielectric properties of HfO₂ using experimental measurements and density functional theory calculations.
Vibrational Properties and Thermodynamic Behavior
Explores vibrational spectra and avoided-crossing effects in bulk and single-layer SnO and PbO, along with the role of lattice dynamics in determining thermodynamic behavior.
Superionic Halides and Colossal Dielectric Response
Investigates how intercluster Ag⁺ diffusion in RbAg₄I₅ contributes to its broadband colossal dielectric constant, connecting ionic mobility with unusual dielectric behavior.
Machine Learning Algorithms
We work to predict new advanced materials with better thermoelectric efficiency using the high-throughput approaches together with other functionalities. Furthermore, we are developing machine learning (ML) models to predict carrier transport parameters by analyzing the impact of the correct choice of descriptors and various algorithms, such as SVR, KRR, XGBoost, Random Forest and Decision tree algorithms.
Project title: Investigation of Janus monolayer for flexible transistor, thermoelectric and optoelectronic device applications using first-principles and data-driven machine learning approaches
Funding agency: ANRF
Rs. 37.15 Lakhs (Principal Investigator)
Project title: Investigation of Quantum Materials (MX; M =III & X=VI elements) for Thermoelectric and Optoelectronic Applications using First-principles and Machine Learning Formalisms
Funding agency: NERIST SEED Grant
Rs. 6.00 Lakhs (Principal Investigator)
Project title: Exploring carrier transport properties of superlattices for thermoelectric and optoelectronic applications through first-principles approach and Boltzmann transport theory
Funding agency: RUAS SEED Grant
Rs. 3.40 Lakhs (Principal Investigator)