activity
20002026
most citedEngineering multiferroism in CaMnO

206 citations · 819 across the 79 of their papers we have counts for

collaborators

82 papers

cond-mat.mtrl-sci2026

Unconventional magnetoelastic behavior in Al rich CoFeAl Films with inverse Heusler like local order for flexible spintronics

Rupalipriyadarsini Chhatoi, Anuroopa Behatha, Swayang Priya Mahanta +7

Strain engineering of magnetic properties offers a promising route toward flexible spintronic applications. Here, we report an unconventional magnetoelastic response in Al rich Co…

quant-ph2026

Circuit Depth Compression via Spectral Gap Amplification in Quantum Phase Estimation

Sk Mujaffar Hossain, Satadeep Bhattacharjee

We show that quantum phase estimation (QPE) circuits can be significantly compressed in depth by preprocessing the input operator with a sigmoid spectral filter before estimation.…

cond-mat.mtrl-sci2026

A Magnetism-Aware Crystal Graph Neural Network with Explicit Magnetic-Sublattice and Exchange-Geometry Representations

Sourav Mal, Satadeep Bhattacharjee

Magnetic order in crystals is governed by moment-carrying sublattices and ligand-mediated exchange pathways, yet standard crystal graph neural networks do not explicitly distinguis…

cond-mat.mtrl-sci2026

Uncertainty-Aware Symbolic Regression through Bayesian Support Selection

Satadeep Bhattacharjee

The Sure Independence Screening and Sparsifying Operator (SISSO) framework is a powerful symbolic-regression method for extracting compact and interpretable descriptors from large…

cond-mat.mtrl-sci2026

SR-CGCNN: Shared Recurrent Convolution in Crystal Graph Neural Networks for Materials Property Prediction

Satadeep Bhattacharjee

Crystal graph neural networks predict materials properties by propagating information through local atomic environments. In conventional crystal graph convolutional neural networks…

stat.ML2026

Filtered Spectral Projection for Quantum Principal Component Analysis

Sk Mujaffar Hossain, Satadeep Bhattacharjee

Quantum principal component analysis (qPCA) is commonly formulated as the extraction of eigenvalues and eigenvectors of a covariance-encoded density operator. Yet in many qPCA sett…