activity
20162025
most citedAnti-site disorder and Berry curvature driven anomalous Hall effect in spin gapless semiconducting Mn2CoAl Heusler compound

34 citations · 93 across the 32 of their papers we have counts for

collaborators

32 papers

cs.LG2025

LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation

Subhojyoti Khastagir, Kishalay Das, Pawan Goyal +3

Recent advances in generative modeling have shown significant promise in designing novel periodic crystal structures. Existing approaches typically rely on either large language mo…

cond-mat.mtrl-sci2025

Role of Oxygen during Methane Oxidation on Pd/PdO@CeO Surface: A Combined Density Functional Theory, Microkinetic, and Machine Learning Approach

Shalini Tomar, Hojin Jeong, Joon Hwan Choi +2

This work explores the role of oxygen in industrial methane oxidation. Oxygen, a well-known oxidizing agent, drives CH conversion to CO and HO. We report how oxygen inf…

cond-mat.mtrl-sci2025

Mechanistic Insights into Complete Methane Oxidation on Single-Atom Pd Supported by SSZ-13 Zeolite: A First-Principles Study

Anuroopa Behatha, Shalini Tomar, Hojin Jeong +3

Complete catalytic oxidation of methane is an effective strategy for greenhouse gas mitigation and clean energy conversion; yet, ensuring both high catalytic activity and stability…

cond-mat.mtrl-sci2025

Quantum Simulations of Battery Electrolytes with VQE-qEOM and SQD: Active-Space Design, Dissociation, and Excited States of LiPF, NaPF, and FSI Salts

Sk Mujaffar Hossain, Seung-Cheol Lee, Satadeep Bhattacharjee

Accurate prediction of excited states in battery electrolytes is central to understanding photostability, oxidative stability, and degradation. We employ hybrid quantum-classical a…

cond-mat.mtrl-sci2025

Testing the spin-bath view of self-attention: A Hamiltonian analysis of GPT-2 Transformer

Satadeep Bhattacharjee, Seung-Cheol Lee

The recently proposed physics-based framework by Huo and Johnson~\cite{huo2024capturing} models the attention mechanism of Large Language Models (LLMs) as an interacting two-body s…

cond-mat.mtrl-sci2025

Tunable Thermal Expansion in Functionalized 2D Boron Nitride: A First-Principles Investigation

Sk Mujaffar Hossain, Dobin Kim, Jaehyun Park +2

This study investigates the thermal expansion coefficient of two-dimensional (2D) functionalized boron nitride (f-BN) materials using first-principles density functional theory (DF…

cond-mat.mtrl-sci2025

Multi-site mixing and entropy stabilization of CsPbI with potential application in photovoltaics

Namitha Anna Koshi, Krishnamohan Thekkepat, Doh-Kwon Lee +2

Metal halide perovskite solar cells have achieved dramatic improvements in their power conversion efficiency in the recent past. Since compositional engineering plays an important…

cond-mat.mtrl-sci2025

Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali-Metal-Ion Battery Materials

Sk Mujaffar Hossain, Namitha Anna Koshi, Seung-Cheol Lee +2

Accurate prediction of the voltage of battery materials plays a pivotal role in the advancement of energy storage technologies and the rational design of high-performance cathode m…

cs.LG2025

Periodic Materials Generation using Text-Guided Joint Diffusion Model

Kishalay Das, Subhojyoti Khastagir, Pawan Goyal +3

Equivariant diffusion models have emerged as the prevailing approach for generating novel crystal materials due to their ability to leverage the physical symmetries of periodic mat…

cond-mat.mtrl-sci2024

Local Symmetry Breaking in Skyrmion-Hosting Centrosymmetric Hexagonal Compounds

Anupam K. Singh, Krishna K. Dubey, Parul Devi +10

Dzyaloshinskii-Moriya interaction (DMI) plays a crucial role in stabilizing the exotic topologically stable skyrmion spin textures in the noncentrosymmetric crystals. The recent di…

cond-mat.mtrl-sci2024

Enhancement of spin Hall angle by an order of magnitude via Cu intercalation in MoS/CoFeB heterostructures

Abhisek Mishra, Pritam Das, Rupalipriyadarsini Chhatoi +7

Transition metal dichalcogenides (TMDs) are a novel class of quantum materials with significant potential in spintronics, optoelectronics, valleytronics, and opto-valleytronics. TM…

cond-mat.mtrl-sci2024

Origin of unexpected weak Gilbert damping in the LSMO/Pt bilayer system

Pritam Das, Pushpendra Gupta, Seung-Cheol Lee +2

This study presents a first-principles and semiclassical analysis of the puzzling observation that a LaSrMnO (LSMO) thin film exhibits larger Gilbert damping th…

cond-mat.mtrl-sci2024

Magnetization dynamics in skyrmions due to high-speed carrier injections from Dirac half-metals

Satadeep Bhattacharjee, Seung-Cheol Lee

Recent developments in the magnetization dynamics in spin textures, particularly skyrmions, offer promising new directions for magnetic storage technologies and spintronics. Skyrmi…

cond-mat.mtrl-sci2024

Controlling Moisture for Enhanced Ozone Decomposition: A Study of Water Effects on CeO Surfaces and Catalytic Activity

Suchitra Gupta, Joon Hwan Choi, Hojin Jeong +2

This study investigates the catalytic degradation of ground-level ozone on low-index stoichiometric and reduced CeO surfaces using first-principles calculations. The presence o…

cs.CL2024★ 18 cited

MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction

Ankan Mullick, Akash Ghosh, G Sai Chaitanya +5

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entit…

cond-mat.mtrl-sci2024

Tuning the Electronic and Magnetic Properties of Double Transition Metal MCrCT (M = Ti, Mo) Janus MXenes for Enhanced Spintronics and Nanoelectronics

Swetarekha Ram, Namitha Anna Koshi, Seung-Cheol Lee +1

Janus MXenes, a new category of two-dimensional (2D) materials, shows promising potential for advances in optoelectronics, spintronics and nanoelectronics. Our theoretical investig…

cond-mat.mtrl-sci2024★ 1 cited

A Hybrid Machine Learning Framework for Predicting Hydrogen Storage Capacities in Metal Hydrides: Unsupervised Feature Learning with Deep Neural Networks

Satadeep Bhattacharjee, Pritam Das, Swetarekha Ram +1

In this study, we present a sophisticated hybrid machine-learning framework that significantly improves the accuracy of predicting hydrogen storage capacities in metal hydrides. Th…

cond-mat.mtrl-sci2024

Calculation of Gilbert damping and magnetic moment of inertia using torque-torque correlation model within ab initio Wannier framework

Robin Bajaj, Seung-Cheol Lee, H. R. Krishnamurthy +2

Magnetization dynamics in magnetic materials are well described by the modified semiclassical Landau-Lifshitz-Gilbert (LLG) equation, which includes the magnetic damping and th…

cond-mat.mes-hall2023

Magneto-transport in the monolayer MoS2 material system for high-performance field-effect transistor applications

Anup Kumar Mandia, Rohit Kumar, Seung-Cheol Lee +2

Electronic transport in monolayer MoS2 is significantly constrained by several extrinsic factors despite showing good prospects as a transistor channel material. Our paper aims to…

cond-mat.mtrl-sci2023

Customizing PBE Exchange-Correlation functionals: A comprehensive approach for band gap prediction in diverse semiconductors

Satadeep Bhattacharjee, Namitha Anna Koshi, Seung-Cheol Lee

Accurate band gap prediction in semiconductors is crucial for materials science and semiconductor technology advancements. This paper extends the Perdew-Burke-Ernzerhof (PBE) funct…

cond-mat.mtrl-sci2023

Insights into Heterogeneous Catalysis on Surfaces with 3d Transition Metals: Spin-Dependent Chemisorption Models and Magnetic Field Effects

Satadeep Bhattacharjee, Swetarekha Ram, Seung-Cheol Lee

This article provides a review of recent developments in the field of 3d transition metal (TM) catalysts for different reactions including oxygen-based reactions such as Oxygen Red…

cond-mat.mtrl-sci2023★ 9 cited

CrysMMNet: Multimodal Representation for Crystal Property Prediction

Kishalay Das, Pawan Goyal, Seung-Cheol Lee +2

Machine Learning models have emerged as a powerful tool for fast and accurate prediction of different crystalline properties. Exiting state-of-the-art models rely on a single modal…

cond-mat.mtrl-sci2023

Combining first-principles modeling and symbolic regression for designing efficient single-atom catalysts in Oxygen Evolution Reaction on MoCO MXenes

Swetarekha Ram, Gwan Hyun Choi, Albert S. Lee +2

In this study, we address the significant challenge of overcoming limitations in catalytic efficiency for the oxygen evolution reaction (OER). The current linear scaling relationsh…

cond-mat.mes-hall2023

Straintronics using the monolayer-Xene platform -- a comparative study

Swastik Sahoo, Namitha Anna Koshi, Seung-Cheol Lee +2

Monolayer silicene is a front runner in the 2D-Xene family, which also comprises germanene, stanene, and phosphorene, to name a few, due to its compatibility with current silicon f…