2D Materials: An emerging platform for nanoelectronics, spintronics, and energy storage applications

Two-dimensional (2D) materials have emerged as a versatile platform for enabling next-generation technologies. Their unique tunable band structures, arising from strong quantum confinement, combined with large surface-to-volume ratios and weak interlayer van der Waals interactions, allow 2D materials to overcome the limitations of conventional semiconductors. These properties open exciting opportunities for energy-efficient transistors, spin-based logic devices, and high-capacity storage systems. My research focuses on tuning the properties of 2D materials through phase engineering, surface modification, and heterostructure design, using first-principles density functional theory (DFT) calculations.

4.  Deep Neural Network Interatomic Potentials for  Structural Optimization of Moiré Superlattices

Moiré superlattices formed by tiny twist angles in 2D materials create massive atomic structures that are too large for conventional DFT to fully optimize, making accurate simulations extremely challenging. While classical force fields are faster, they often miss subtle interlayer interactions and atomic reconstructions that define moiré physics. Deep Neural Network interatomic potentials (NNPs) solve this problem by learning from DFT data, delivering near first-principles accuracy at a fraction of the computational cost. This allows realistic, large-scale simulations of twisted materials, unlocking deeper insight into the fascinating physics of moiré superlattices. AA flowchart of the model used to train the NNP is provided below.

5.  Deep Neural Network–Trained Tight-Binding Model for Studying Electronic Properties of Moiré Superlattices

Although NNPs can efficiently handle structural optimization, studying the electronic properties of moiré superlattices with small twist angles using plane-wave DFT is computationally impractical because of the extremely large basis set required. To overcome this, a detailed ab initio tight-binding (TB) model is needed to accurately reproduce the electronic band structure. The accuracy of a TB model strongly depends on the choice of atomic-orbital basis. In this work, maximally localized Wannier functions (MLWFs) derived from DFT are used to build a full-range TB Hamiltonian. However, this leads to a large number of hopping parameters that are highly sensitive to local structural changes. To address this, a deep neural network is trained to predict environment-dependent TB parameters, while symmetry operations help reduce the number of independent variables.The framework also includes spin-orbit coupling, interlayer interactions, and higher-order effects, enabling the study of complex phenomena such as topological phases, anomalous Hall effects, and valley excitonic states. This work aims to develop a powerful theoretical platform for understanding and predicting the electronic behavior of twisted 2D materials.

Work in progress

1.  MXene-Based MTJs for Non-Volatile SOT-MRAM Applications

Certain MXenes exhibit room-temperature ferromagnetism along with strong out-of-plane magnetic anisotropy. In addition, MXenes containing heavy transition metals show significant spin–orbit coupling effects. Together, these properties make MXenes highly promising for constructing two-dimensional van der Waals heterostructure-based magnetic tunnel junctions (MTJs) for next-generation non-volatile spin–orbit torque magnetic random-access memory (SOT-MRAM) applications. This study is focused on analyzing the transport characteristics and evaluating the tunneling magnetoresistance (TMR) of MXene-based MTJs using the non-equilibrium Green’s function (NEGF) method.

Work in progress

2.  MXene Alloy-Based Metal-Semiocnductor Contact for Low-Resitive FETs

This work explores MXenes as a platform for next-generation electronics by engineering low-resistance metal–semiconductor contacts for field-effect transistors. Using first-principles calculations, transport simulations, and alloy phase design, we demonstrate the feasibility of achieving high current-carrying, low-resistance MXene contacts. Notably, the Ta₂CO₂–Ti₂CO₂ heterojunction shows exceptionally low Schottky barrier height, while interfacial alloying strategies further suppress Fermi-level pinning and reduce contact resistance. 

(Communications Engineering, 4(1), 190.)

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3.  Graphene-Buffered Co/PtSSe Interfaces: Unlocking High-Performance SpinFETs

Rashba spin-orbit coupling in 2D materials enables spin-momentum locking, a key mechanism for fast and energy-efficient spin field-effect transistors (spinFETs). PtSSe, a 2D semiconductor, shows strong local Rashba effects, making it ideal for spinFETs. However, forming high-quality contacts with ferromagnetic metals is challenging due to metal-induced gap states (MIGSs). Our first-principles study demonstrates that inserting a graphene layer between Co and PtSSe suppresses MIGSs, lowers the Schottky barrier, enhances gate tunability, and boosts spin polarization at the interface. This combined control of barrier height and Rashba parameters under an electric field paves the way for low-power, high-performance spinFETs.

(APL Electronic Devices 1, no. 4 (2025).)

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6.  Influence of Surface Termination Distribution on Li Storage and Diffusion in MXenes: A Multiscale Modeling Study

This study explores how the spatial arrangement of surface terminations in MXenes influences their electrochemical performance. Using a combination of DFT, kinetic Monte Carlo (KMC), and AIMD simulations, we show that even at the same composition, different O/F termination patterns on Ti₃C₂ MXenes can lead to dramatic variations in Li diffusion and storage capacity. Our findings highlight that local termination environments, rather than averaged compositions, are key to optimizing MXenes as high-performance electrode materials.

(Journal of Energy Storage139 (2025): 118594.)

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7. Coordination-Symmetry–Driven Stable Phases in MXenes

MXenes can adopt either trigonal-prismatic or octahedral coordination, each imparting distinct structural and electronic properties. Using density functional theory (DFT) simulations, we systematically investigated carbide MXenes with various transition metals and surface terminations, demonstrating that phase stability is strongly governed by coordination symmetry. The observed stability trends, rationalized through crystal field model by simply counting non-bonding electrons, provide a design framework for phase-engineering MXenes with tailored functionalities.

(The Journal of Physical Chemistry C 127.42 (2023): 20734-20741).

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