Research Projects

Spintronics exploits electron spin states for data storage and computing. A core component of this technology is the Magnetic Tunnel Junction (MTJ), a building block for Magnetic Random Access Memory (MRAM) and hard disk drives. Combined quantum tunneling and electronic band structure engineering lead to high tunnel magnetoresistance (TMR) in MTJs, enabling low power consumption, fast switching, and high-density storage. As the demand for compact and sustainable memory grows, defect-free, atomically thin materials have become essential. Two-dimensional (2D) materials meet this need and are easy to exfoliate due to their van der Waals gap. However, magnetism in 2D materials is rare, as thermal fluctuations typically hinder magnetic ordering. Recent breakthrough room-temperature 2D ferromagnets Fe₃GeTe₂ have opened a new avenue for all-2D spintronic devices. Even so, most of the 2D ferromagnets face limitations, like low transition temperatures (Tc), in-plane anisotropy, and insulating ground states. Additionally, integrating these materials into nano-devices is still in its early stage, yet to achieve commercial limit (CoFeB/MgO, room temperature TMR~600%). The two fundamental mechanisms, exchange interactions and spin-orbit coupling, together govern vital properties such as magnetic ordering, Tc, anisotropy, and electrical conductivity. These parameters depend highly on the material's crystal structure prototype and elemental composition. Therefore, tuning these factors could lead to new 2D materials with the targeted properties required for practical applications. Identifying suitable materials through traditional trial-and-error processes (experiments/simulations) is both time-consuming and costly. Machine learning (ML) offers an efficient alternative by training models on comprehensive datasets of physical attributes that characterize materials, enabling the fast screening of unexplored compounds to identify those with desired properties. Our previous ML-assisted predictions of complex oxides, later experimentally validated, provide confidence in this method. This research proposes a pipeline for the robust prediction of 2D ferromagnets with targeted properties using ML algorithms based on quantum mechanical features, followed by validation through detailed ab initio calculations. Finally, the new materials will be evaluated in nano-devices using non-equilibrium Green's function-based transport calculations to identify candidates with high TMR. This study bridges fundamental science and applied technology by addressing a critical gap: many computational studies limit themselves to theoretical insights without assessing practical performance. Combining multipronged approaches like ML, ab initio, and quantum transport calculations, this research aims to streamline material discovery to enhance spintronic device performance by replacing conventional ferromagnets with 2D materials, potentially benefiting future fields such as quantum computing.