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Sharma, Vinit K.

Publications and source records attributed to Sharma, Vinit K..

Oxygen Vacancy Injection as a Pathway to Enhancing Electromechanical Response in Ferroelectrics

Since their discovery in late 1940s, perovskite ferroelectric materials have become one of the central objects of condensed matter physics and materials science due to the broad spectrum of functional behaviors they exhibit, including electro-optical phenomena and strong electromechanical coupling. In such disordered materials, the static properties of defects such as oxygen vacancies are well explored but the dynamic effects are less understood. In this work, the first observation of enhanced electromechanical response in BaTiO3 thin films is reported driven via dynamic local oxygen vacancy control in piezoresponse force microscopy (PFM). A persistence in peizoelectricity past the bulk Curie temperature and an enhanced electromechanical response due to a created internal electric field that further enhances the intrinsic electrostriction are explicitly demonstrated. The findings are supported by a series of temperature dependent band excitation PFM in ultrahigh vacuum and a combination of modeling techniques including finite element modeling, reactive force field, and density functional theory. Furthermore, this study shows the pivotal role that dynamics of vacancies in complex oxides can play in determining functional properties and thus provides a new route toward– achieving enhanced ferroic response with higher functional temperature windows in ferroelectrics and other ferroic materials.

36 MATERIALS SCIENCE↗

Exotic Long-Range Surface Reconstruction on La 0.7 Sr 0.3 MnO 3 Thin Films

Due to an extremely diverse phase space, La 1–x Sr x MnO 3 , as with other manganites, offers a wide range of tunability and applications including colossal magnetoresistance and use as spin-polarized electrodes. Here, we study an unprecedented, exotic surface reconstruction (6 × 6) in La 1–x Sr x MnO 3 (x = 0.3) observed via low-energy electron diffraction (LEED). Scanning tunneling microscopy (STM) shows the surface is relatively flat, with unit-cell step heights, and X-ray photoelectron spectroscopy (XPS) reveals a strong degree of Sr segregation at the surface. By combining electron diffraction and first-principles computations, we propose that the long-range surface reconstruction consists of a Sr-segregated surface with La (6 × 6) ordering. This study expands our understanding of manganite systems and underscores their ability to form interesting surface reconstructions, driven largely by cation segregation that can potentially be controlled for tuning surface ordering.

36 MATERIALS SCIENCE↗

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .

36 MATERIALS SCIENCE↗