arXiv subjects

Utkarsh Saha

Publications and source records attributed to Utkarsh Saha.

4 recordsLinked to original sources

arXiv2026

Above Room Temperature Ferroelectricity in Epitaxially Strained KTaO3

Epitaxial strain is a powerful means to engineer emergent phenomena in thin films and heterostructures. Here, we demonstrate that KTaO3, a cubic perovskite in bulk form, can be epitaxially strained into a highly tunable ferroelectric. KTaO3 films grown commensurate to SrTiO3 (001) substrates experience an in-plane strain of -2.1 % that transforms the cubic structure into a tetragonal polar phase with transition temperature of 475 K, consistent with our thermodynamic calculations. We show that the Curie temperature and the spontaneous electric polarization can be system- atically controlled with epitaxial strain. Scanning transmission electron microscopy reveals cooperative polar displacements of the potassium columns with respect to the neighboring tantalum columns at room temperature. Optical second-harmonic generation results are described by a tetragonal polar point group (4mm), indicating the emergence of a global polar ground state. We observe a ferroelectric hysteresis response, using metal-insulator-metal capacitor test structures. The results demon- strate a robust intrinsic ferroelectric state in epitaxially strained KTaO3 thin films.

cond-mat.mtrl-sci↗

arXiv2025

Terahertz-field activation of polar skyrons

Unraveling collective modes arising from coupled degrees of freedom is crucial for understanding complex interactions in solids and developing new functionalities. Unique collective behaviors emerge when two degrees of freedom, ordered on distinct length scales, interact. Polar skyrmions, three-dimensional electric polarization textures in ferroelectric superlattices, disrupt the lattice continuity at the nanometer scale with nontrivial topology, leading to previously unexplored collective modes. Here, using terahertz-field excitation and femtosecond x-ray diffraction, we discovered subterahertz collective modes, dubbed 'skyrons', which appear as swirling patterns of atomic displacements functioning as atomic-scale gearsets. Momentum-resolved time-domain measurements of diffuse scattering revealed an avoided crossing in the dispersion relation of skyrons. We further demonstrated that the amplitude and dispersion of skyrons can be controlled by sample temperature and electric-field bias. Atomistic simulations and dynamical phase-field modeling provided microscopic insights into the three-dimensional crystallographic and polarization dynamics. The discovery of skyrons and their coupling with terahertz fields opens avenues for ultrafast control of topological polar structures.

cond-mat.mtrl-sci↗

arXiv2021

Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering

Data-driven approaches to solve problems in materials science have gained immense popularity in recent times due to their ability to predict unknown material properties and uncover relationships between structure and property. Machine learning algorithms like GBRT, random forest and neural networks have had tremendous success in predicting target properties of materials and design of structures for various applications. However, a major drawback for achieving results within the required accuracy using these algorithms has been the need for large datasets which can be challenging for problems when data is not sufficiently available for training the models. In this work, we propose the use of a hierarchical clustering algorithm which can work considerably better on materials science problems with small dataset constraints. We apply the algorithm to screen out promising double perovskite materials as candidates for solar cells.

cond-mat.mtrl-sci↗

arXiv2020

Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach

Singlet fission has emerged as one of the most exciting phenomena known to improve the efficiencies of different types of solar cells and has found uses in diverse optoelectronic applications. The range of available singlet fission molecules is, however, limited as to undergo singlet fission, molecules have to satisfy certain energy conditions. Recent advances in material search using inverse design has enabled the prediction of materials for a wide range of applications and has emerged as one of the most efficient methods in the discovery of suitable materials. It is particularly helpful in manipulating large datasets, uncovering hidden information from the molecular dataset and generating new structures. However, we seldom encounter large datasets in structure prediction problems in material science. In our work, we put forward inverse design of possible singlet fission molecules using a transfer learning based approach where we make use of a much larger ChEMBL dataset of structurally similar molecules to transfer the learned characteristics to the singlet fission dataset.

physics.app-ph↗