Research

Materials for Next-generation Optoelectronic Devices

Optoelectronic devices (i.e. solar cells, light-emitting devices, thin-film transistors) play an important role in clean energy generation as well as in minimizing the energy demand. However, highly efficient and cost-effective materials are prerequisites to make these devices widely applicable in the real world.

In our group, we perform an in-depth computational investigation to identify potential materials for optoelectronics. We search for new unexplored material classes as well as seek to understand the fundamental processes that influence the performance of existing candidates. Below is a brief list of research topics that we are currently interested in,

Apply simulations that combine mixed quantum-classical non-adiabatic molecular dynamics (NAMD) and time-dependent density functional theory (TD-DFT) to study the charge carrier transport and recombination in newly emerging optoelectronic materials such as halide perovskites, kesterites, and organic crystals.
Use ab initio molecular dynamics (AIMD) and DFT to explore the effect of lattice-dynamics, point, and extended defects on optoelectronic and photovoltaic properties of materials.
Investigate ion migration and its impact on the performance and stability of materials using AIMD and nudge-elastic band methods.
In-silico material designing and interface engineering for harvesting hot carriers to realize highly efficient photovoltaic devices

Active Group Members: Pabitra, Nikhil, Bhawna, Aashutosh, Akansha, Pooja

Charge Dynamics on the Interface for Photocatalysts

In our group, we perform an in-depth computational investigation to identify potential materials for heterogeneous catalysis. Alongside, we are also investigating linker and nodal functionalizations to tune electronic, magnetic, ion-transport mechanisms and catalytic properties of Metal-Organic Frameworks (MOFs).

Below is a brief list of research topics that we are currently interested in:-

DFT-based Simulations: Predicting and understanding the electronic structure of catalysts and MOFs by integrating quantum computational chemistry, surface electrochemistry, and materials science.-

AIMD Simulations: Exploring the structural dynamics and stability of materials under realistic conditions to understand their behavior and performance in catalytic applications.-

NAMD Simulations: Simulating excited charge dynamics to calculate charge carrier lifetimes, particularly focusing on the effects of strong electron-phonon coupling on non-radiative recombination.-

Machine Learning (ML): Utilizing ML algorithms to streamline the catalyst and MOF discovery process by training models with data from electronic structure calculations.

By combining DFT, AIMD, NAMD, and ML techniques, we systematically study the effects of doping, defects, and interface engineering on various materials, including MOFs. Our goal is to design materials and interfaces that enhance catalytic performance and also design MOFs that can be used efficiently for magnetic, ion-transport, electronic and catalytic applications.

Active Group Members: Ankita, Sakshi, Bhawna

High-throughput and Machine Learning for Functional Materials Design

We are using the high-throughput and machine learning (ML)-based computational framework to accelerate functional materials design and discovery. The data-driven approach also provides an atomistic understanding of the complex functionalities of these materials. It uncovers hidden relations among several elemental and compound properties to their phase stabilities and performance metrics.

We are interested in developing high-accuracy supervised ML models to provide universal strategies for efficient optoelectronic material designs and discovery, particularly for LEDs, detectors, and solar cells.

Active Group Members: Pabitra, Kushal, Nikhil, Ankita

Low-dimensional Layered Materials for Spintronics and Nanoelectronics

Low-dimensional magnetic materials are promising for their applications ranging from low power consuming spintronic devices to quantum computing to efficient refrigeration. We use AIMD, DFT, and Monte Carlo-based techniques to explore these problems. In our group, we are working on the following problems,

Tuning the magnetic interactions on-demand in inorganic layered materials such as transition metal (TM) phosphorous trisulfide and triiodides
Finding suitable materials for high-temperature low-dimensional magnetic metal-organic frameworks (MOF).
Transition metal embedded graphene-like layered materials for data-storage applications.

Active Group Members: Kushal

Inorganic Nanocrystals for LEDs and Single Photo Emitter

Single-photon emitters (SPE) are key elements of any quantum photonic circuit. Recently, inorganic zero-dimensional nanocrystals (NCs) have emerged as potential candidates for SPE. The atomic-scale details of structural and optoelectronic properties of these NCs are very important for their performance as SPE. Similarly, controlled and precise emission from quantum dots is highly desirable for their light-emitting device applications. We employ AIMD, DFT, TD-DFT, and NAMD simulations for these projects. Here are some key issues that we are actively investigating,

Understanding the surface and interface properties of the inorganic NCs. Particularly, focus on electron-surface phonon interactions to strategically design passivating agents to enhance single-photon emission.
Explore the surface chemistry and size of non-stoichiometric NCs to tune their optoelectronic and emission properties.
Stability and impact of controlled chemical doping in NCs.

Active Group Members: Kushal, Alam, Jyoti

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