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Pravan Omprakash

Publications and source records attributed to Pravan Omprakash.

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Beyond the Binary Hull: Higher-Order Thermodynamic Stabilization in Inorganic Ternary Compounds

Stable ternary compounds lie below every competing phase on a conventional convex hull, but this does not reveal whether their stability is inherited from already favorable binaries or created by bringing three elements into one crystal. We define the emergent ternary stabilization energy, delta E3, as the energy by which a stable ternary lies below the complete unary-binary hull at the same composition. Applied to 19,209 Materials Project compounds calculated within the GGA framework, delta E3 has a median of 63.3 meV/atom and spans from nearly binary-degenerate phases to compounds stabilized by several hundred meV/atom. The distribution separates recognizable chemical limits: intermetallics have the smallest median stabilization, single-anion compounds occupy a broad intermediate regime, and polyanion compounds receive the largest gains. Within single anion families, large delta E3 is associated with cation electronegativity contrast, bandgap opening, and reconstruction of coordination, bond geometry, and local neighbour chemistry relative to the actual binary decomposition products. Experimentally linked compounds obtain a median 4.92% of their formation energy from ternary stabilization, compared with 3.53% for theoretical compounds, with the clearest separation in sulphides and selenides. delta E3 therefore provides a compact measure of the thermodynamic value of compositional complexity: low values identify phases whose stability is largely reproducible by binaries, while high values identify compounds that gain substantially from ternary specific electronic and structural organization.

cond-mat.mtrl-sci

First-principles-based Prediction of Phase Fields: Part I. Binary and Ternary Refractory Alloys

Multiple principal element alloys (MPEAs) exhibit complex phase equilibria involving multinary solid solutions and intermetallics, which makes it challenging to predict their temperature-composition phase diagrams. Their vast compositional space makes first principles methods prohibitively expensive, while CALPHAD is limited by scarce experimental data. Here, we present a computationally efficient framework to predict the solvus phase boundaries, and hence, phase fields, in refractory MPEAs composed of Cr, Hf, Mo, Nb, Ta, Ti, V, W, and Zr. The approach combines DFT calculated binary mixing enthalpies with sub regular solution models to construct phase diagrams without fitting higher order interactions, enabling efficient scaling across composition space. Validation against 36 binary and 15 ternary phase diagrams demonstrates good agreement, with both experimental results and CALPHAD calculations. We find that the prediction accuracy is enhanced by incorporating lattice dependent energetics through sub regular solution models and including temperature-dependent elemental phase transitions. The framework captures miscibility gaps, solid solution stability, and intermetallic formation, with predicted miscible temperatures typically within 300 K of experimental values. Overall, this work establishes a scalable, first principles based route for highthroughput prediction of phase diagrams in refractory MPEAs. A publicly accessible web interface has also been developed to allow interactive exploration of the predicted phase diagrams, available at https://raptor.engr.wustl.edu.

cond-mat.mtrl-sci

Twist-induced Out-of-plane Ferroelectricity in Bilayer Hafnia

Ferroelectric HfO2 is a promising candidate for next-generation memory devices due to its CMOS compatibility and ability to retain polarization at nanometer scales. However, the polar orthorhombic phase (Pca2_1) responsible for ferroelectricity is metastable and requires extrinsic stabilization, which makes it challenging for integration with silicon. We predict that bilayer 1T-HfO2 can exhibit robust and switchable out-of-plane (OOP) polarization arising from stacking-induced symmetry breaking. Using first-principles density functional theory, we predict that monolayer 1T-HfO2 can be cleaved from the (111) surface of cubic hafnia, and the monolayer is dynamically stable. When two aligned monolayers are twisted to form a moiré superlattice, it breaks the interlayer symmetry and allows the emergence of bistable OOP polarization. At a twist angle of 7.34o, the system exhibits a net polarization of ~16 μC/cm2. This sizeable polarization is due to the large polar displacements concentrated in AB stacking domains. Importantly, this polarization can be reversibly switched via interlayer sliding with a low energy barrier (~8 meV/formula unit) and comparable low coercive field (~0.2 V/nm), offering electric-field tunability. These findings establish twisted bilayer 1T-HfO2 as a scalable and robust 2D ferroelectric platform, enabling new pathways for integrating ferroelectric functionality into atomically thin memory and logic devices.

cond-mat.mtrl-sci

Hole-doping reduces the coercive field in ferroelectric hafnia

Ferroelectric hafnia (HfO2) holds promise for next-generation memory and logic applications because of its CMOS compatibility. However, the high coercive field required for polarization switching in HfO2 remains a critical challenge for efficient device operations. Using first-principles calculations and phenomenological modeling, we predict that hole doping can reduce the coercive field from 8 MV/cm in undoped hafnia to 6 MV/cm in hafnia doped with 0.2 holes per formula unit (f.u.). In the absence of doping, the reversal of polarization of the Pca21 phase is preferred through the non-polar, tetragonal P42/nmc phase. This switching pathway involves the coupling of three hard distortion modes that render undoped hafnia as an improper ferroelectric. The overall energy barrier through this pathway remains unchanged (80 meV/f.u.) upon hole doping. However, the introduction of holes hardens the polar distortion mode that connects the polar Pca21 phase to the non polar, orthorhombic Pbcm phase, and reduces the energy barrier from 180 meV/f.u. in undoped hafnia to 80 meV/f.u. at 0.2 holes/f.u.. The activation of the latter switching pathway through the Pbcm phase can lead to a reversal in the polarization direction. Overall, hole doping makes the switching pathway through the Pbcm phase competitive, and renders hafnia as a proper ferroelectric with a lower coercive field.

cond-mat.mtrl-sci

Design Rules and Discovery of Face-Sharing Hexagonal Perovskites

Hexagonal perovskites with face-sharing octahedral connectivity are an underexplored class of materials. We propose quantitative design principles for stabilizing face-sharing ABX3 hexagonal perovskites based on a comparative analysis of oxides and sulfides. By mapping structural preferences across a phase-space defined by an electronegativity-corrected tolerance factor and the Shannon radius of the A-site cations, we identify distinct thresholds that separate hexagonal phases from competing cubic polymorphs having corner-sharing octahedral connectivity. Our analysis reveals that sulfides differ significantly from oxides due to the increased covalency of the transition metal-sulfur bonds, which enables broader compositional flexibility. Applying these principles, we predict a set of thermodynamically formable ABO3 and ABS3 compounds that are likely to adopt face-sharing octahedral connectivity. These findings establish a predictive framework for designing hexagonal perovskites, highlighting sulfides as promising candidates for obtaining quasi-one-dimensional materials having transition-metal cations for novel ferroic phenomena.

cond-mat.mtrl-sci

Sub-nm2 ferroelectric domains via charged 180 degree walls in ZrO2

Flat phonon bands in fluorite ferroelectrics (HfO2 or ZrO2) shrink polar domains laterally to an irreducible half-unit-cell width (0.27 nm) within which the vertical arrangement of dipoles is expected to remain uniform. We report on the direct observation of nonuniform and nearly discrete vertical arrangements of dipoles in ZrO2 thin films consisting of closely spaced head-to-head (HH) and tail-to-tail (TT) charged 180 degree walls, each exhibiting a distinct bulk-like structure. These charged domain walls (CDWs) further compress the irreducibly narrow, laterally stacked domains vertically to a thickness of 1-2.75 nm, yielding in-plane domains with sub-nm2 footprints-among the smallest ever reported for any ferroelectric material. The HH and TT walls form due to their flat longitudinal optical (LO) polar bands and are electrostatically stabilized by bound-charge compensation via interstitial oxygen atoms, which act as natural structural defects at the HH walls. Moreover, these walls are predicted to be conducting and to exhibit ultralow propagation barriers, with HH walls (1.6 meV) being far more mobile than TT walls (22.3 meV), indicating strong potential for low-voltage, domain-wall-based nanoelectronics.

cond-mat.mtrl-sci

Antiferroelectric Hafnia Down to the 2D Limit

Antiferroelectricity is a material property characterized by alternating electric dipoles spontaneously ordered in antiparallel directions. Antiferroelectrics are promising for energy storage, solid-state cooling, and memory technologies; however, these materials are scarce, and their scalability remains largely unexplored. In this work, we demonstrate that single-crystalline hafnia, a lead-free CMOS-compatible material, exhibits antiferroelectricity under compressive-strain conditions. We observe antiparallel sublattice polarization and stable double-hysteresis in single-crystalline (111)-oriented epitaxial La-doped hafnia films grown on yttrium-stabilized zirconia and show that the antipolar orthorhombic phase of hafnia adheres to the Kittel model of antiferroelectricity. Notably, compressive strain strengthens the antiferroelectric order in thinner La-doped hafnia films, achieving an unprecedented 850 C ordering temperature in the two-dimensional limit, highlighting hafnia's potential for advanced antiferroelectric devices.

cond-mat.mtrl-sci

Design Principles and Identification of Birefringent Materials

Birefringence ($Δn$) is the dependence of the refractive index of a material on the polarization of light travelling through it. Birefringent materials are used as polarizers, waveplates, and for novel light-matter coupling. While several birefringent materials exist, only a handful of them show large $Δn$ > 0.3, and are primarily limited to the infrared region. The variation of $Δn$ across diverse materials classes and strategies to achieve highly birefringent materials with transparency covering different regions of the electromagnetic spectrum are missing. We have calculated the $Δn$ of 967 non-cubic, formable crystals having vastly different structures, polyhedral connectivity and chemical compositions. From this set of compounds, we have screened highly birefringent crystals ($Δn$ greater than 0.3) having transparency in different regions of the electromagnetic spectrum. The screened compounds belong to several families such as A3'MN3, AMO2, AN3, and A'N6 (A = Li, Na, K; A'= Ca, Sr, Ba; M = V, Nb, Ta). By analyzing the electronic structures of these compounds, we have distilled rules to enable the design of crystals with large $Δn$.

cond-mat.mtrl-sci

SymPlex Plots for Visualizing Properties in High-Dimensional Alloy Spaces

Conventional visualization tools such as phase diagrams and convex hulls are ill-suited to visualize multiple principal element alloys (MPEAs) due to their large compositional space that cannot be easily projected onto two dimensions. Here, SymPlex plots are introduced to enable the visualization of various properties along special paths in high-dimensional phase spaces of MPEAs. These are polar heatmaps that plot properties along high-symmetry paths radiating from the parent equimolar MPEA to a set of chosen lower-order compositions. SymPlex plots capture the changes in the energy landscape along the special paths and help visualize the effect of addition or substitution of components on the alloy stability, which can be especially useful to assess processing pathways for additive manufacturing. Thus, SymPlex plots can help guide design of MPEAs by showing connections between compositions and their properties in the high-dimensional phase space with more information concentrated near the equimolar region.

cond-mat.mtrl-sci

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

cond-mat.mtrl-sci

Polarization Pinning at Antiphase Boundaries in Multiferroic YbFeO$_3$

The switching characteristics of ferroelectrics and multiferroics are influenced by the interaction of topological defects with domain-walls. We report on the pinning of polarization due to antiphase boundaries in thin films of the multiferroic hexagonal YbFeO$_3$. We have directly resolved the atomic structure of a sharp antiphase boundary (APB) in YbFeO$_3$ thin films using a combination of aberration-corrected scanning transmission electron microscopy (STEM) and total energy calculations based on density-functional theory (DFT). We find the presence of a layer of FeO$_6$ octahedra at the APB that bridge the adjacent domains. STEM imaging shows a reversal in the direction of polarization on moving across the APB, which DFT calculations confirm is structural in nature as the polarization reversal reduces the distortion of the FeO$_6$ octahedral layer at the APB. Such APBs in hexagonal perovskites are expected to serve as domain-wall pinning sites and hinder ferroelectric switching of the domains.

cond-mat.mtrl-sci

AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements

Biometric systems based on Machine learning and Deep learning are being extensively used as authentication mechanisms in resource-constrained environments like smartphones and other small computing devices. These AI-powered facial recognition mechanisms have gained enormous popularity in recent years due to their transparent, contact-less and non-invasive nature. While they are effective to a large extent, there are ways to gain unauthorized access using photographs, masks, glasses, etc. In this paper, we propose an alternative authentication mechanism that uses both facial recognition and the unique movements of that particular face while uttering a password, that is, the temporal facial feature movements. The proposed model is not inhibited by language barriers because a user can set a password in any language. When evaluated on the standard MIRACL-VC1 dataset, the proposed model achieved an accuracy of 98.1%, underscoring its effectiveness as an effective and robust system. The proposed method is also data-efficient since the model gave good results even when trained with only 10 positive video samples. The competence of the training of the network is also demonstrated by benchmarking the proposed system against various compounded Facial recognition and Lip reading models.

cs.CV