arXiv subjects

Vinit Sharma

Publications and source records attributed to Vinit Sharma.

12 recordsLinked to original sources

arXiv2021

Probabilistic deep learning approach for targeted hybrid organic-inorganic perovskites

We develop a probabilistic machine learning model and use it to screen for new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band gap. The data set used for this work is highly diverse, containing multiple atomic structures for each of 192 chemically distinct HOIP formulas. Therefore, any property prediction on a given formula must be associated with an irreducible "uncertainty" that comes from its unknown atomic details. As a result, dozens of new HOIP formulas with band gap falling between 1.25 and 1.50 eV were identified and validated against suitable first-principles computations. Through this demonstration we show that the probabilistic deep learning approach is robust, versatile, and can be used to properly quantify this uncertainty. In conclusion, the probabilistic standpoint and approach described herein could be widely useful for the very common and inevitable data uncertainty which is rooted at the incompleteness of information during experiments and/or computations.

cond-mat.mtrl-sci↗

arXiv2021

Electron-beam induced emergence of mesoscopic ordering in layered MnPS$_{3}$

Ordered mesoscale structures in 2D materials induced by small misorientations have opened pathways for a wide variety of novel electronic, ferroelectric, and quantum phenomena. Until now, the only mechanism to induce this periodic ordering was via mechanical rotations between the layers, with the periodicity of the resulting moir\'e pattern being directly related to twist angle. Here we report a fundamentally new mechanism for emergence of mesoscopic periodic patterns in multilayer sulfur-containing metal phosphorous trichalcogenide, MnPS$_{3}$, induced by the electron beam. The formation under the beam of periodic hexagonal patterns with several characteristic length scales, nucleation and transitions between the phases, and local dynamics are demonstrated. The associated mechanisms are attributed to the relative contraction of the layers caused by beam-induced sulphur vacancy formation with subsequent ordering and lattice parameter change. As a result, the plasmonic response of the system is locally altered, suggesting an element of control over plasmon resonances by electron beam patterning. We pose that harnessing this phenomenon provides both insight into fundamental physics of quantum materials and opens a pathway towards device applications by enabling controlled periodic potentials on the atomic scale.

cond-mat.mtrl-sci↗

arXiv2021

A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection

An analysis of high-dimensional data can offer a detailed description of a system but is often challenged by the curse of dimensionality. General dimensionality reduction techniques can alleviate such difficulty by extracting a few important features, but they are limited due to the lack of interpretability and connectivity to actual decision making associated with each physical variable. Variable selection techniques, as an alternative, can maintain the interpretability, but they often involve a greedy search that is susceptible to failure in capturing important interactions or a metaheuristic search that requires extensive computations. This research proposes a new method that produces subspaces, reduced-dimensional physical spaces, based on a randomized search and leverages an ensemble of critical subspace-based models, achieving dimensionality reduction and variable selection. When applied to high-dimensional data collected from the failure prediction of a composite/metal hybrid structure exhibiting complex progressive damage failure under loading, the proposed method outperforms the existing and potential alternatives in prediction and important variable selection.

stat.ME↗

arXiv2021

Predicting phase preferences of two-dimensional transition metal dichalcogenides using machine learning

Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of several possible structures, with the most common being the trigonal prismatic and octahedral symmetry phases. Since the structure determines the electronic properties, being able to predict phase-preferences of TMDs from just the knowledge of the constituent atoms is highly desired, but has remained a long-standing problem. In this study, we applied high-throughput quantum mechanical computations with machine learning algorithms to solve this old problem. Our analysis provides insights into determining physiochemical factors that dictate the phase-preference of a TMD, identifying and going beyond the attributes considered by earlier researchers in predicting crystal structures. A knowledge of these underlying physiochemical factors not only helps us to rationalize, but also to accurately predict structural preferences. We show that machine learning algorithms are powerful tools that can be used not only to find new materials with targeted properties, but also to find connections between elemental attributes and the target property/properties that were not previously obvious.

cond-mat.mtrl-sci↗

arXiv2020

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, 1,500 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 .

cond-mat.mtrl-sci↗

arXiv2019

High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses

Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute {\Gamma}-point phonons, infrared intensities, Born-effective charges, piezoelectric, and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943 materials with at least one far and mid-infrared mode, respectively. We identify 577 high-piezoelectric materials, using a threshold of 0.5 C/m2. Using a threshold of 20, we find 593 potential high-dielectric materials. Importantly, we analyze the chemistry, symmetry, dimensionality, and geometry of the materials to find features that help explain variations in our datasets. Finally, we develop high-accuracy regression models for the highest infrared frequency and maximum Born-effective charges, and classification models for maximum piezoelectric and average dielectric tensors to accelerate discovery.

cond-mat.mtrl-sci↗

arXiv2015

Dopant-mediated structural and magnetic properties of TbMnO3

Structural and magnetic properties of the doped terbium manganites (Tb,A)MnO3 (A = Gd, Dy and Ho) have been investigated using first-principles calculations and further confirmed by subse- quent experimental studies. Both computational and experimental studies suggest that compared to the parent material, namely, TbMnO3 (with a magnetic moment of 9.7 /muB for Tb3+) Dy- and Ho- ion substituted TbMnO3 results in an increase in the magnetic moment (< 10.6/muB for Dy3+ and Ho3+). The observed spiral-spin AFM order in TbMnO3 is stable with respect to the dopant substitutions, which modify the Mn-O-Mn bond angles and lead to stronger the ferromagnetic component of the magnetic moment. Given the fact that magnetic ordering in TbMnO3 causes the ferroelectricity, this is an important step in the field of the magnetically driven ferroelectricity in the class of magnetoelectric multiferroics, which traditionally have low magnetic moments due to the predominantly antiferromagnetic order. In addition, the present study reveals important insights on the phenomenological coupling mechanism in detail, which is essential in order to design new materials with enhanced magneto-electric effects at higher temperatures.

cond-mat.mtrl-sci↗

arXiv2015

Controlling Electronic Structure Through Epitaxial Strain in ZnSe/ZnTe Nano-heterostructures

Using first-principles computations, we study the effect of epitaxial strains on electronic structure variations across ZnSe/ZnTe nano-heterostructures. Epitaxial strains of various types are modeled using pseudomorphic ZnSe/ZnTe heterostructures. We find that a wide range of band gaps (spanning the visible solar spectrum) and band offsets (0-1.5 eV) is accessible across the heterostructures in a controllable manner via reasonable levels of epitaxial strain. In addition to quantum confinement effects, strain in ZnSe/ZnTe heterostructures may thus be viewed as a powerful degree of freedom that can enable the rational design of optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2015

Magnetism of Ta Dichalcogenide Monolayers Tuned by Strain and Hydrogenation

The effects of strain and hydrogenation on the electronic and magnetic properties of monolayers of Ta based dichalcogenides (TaX2; X = S, Se, Te) are investigated using density-functional theo-ry. We predict a complex scenario of strain-dependent magnetic phase transitions involving par-amagnetic, ferromagnetic, and modulated antiferromagnetic states. Covering one of the two chalcogenide surfaces with hydrogen switches the antiferromagnetic/nonmagnetic TaX2 mono-layers to a semiconductor. Our research opens new pathways towards the manipulation of mag-netic properties for future optoelectronics and spintronics applications.

cond-mat.mtrl-sci↗

arXiv2015

Exploring PtSO$_4$ and PdSO$_4$ phases: an evolutionary algorithm based investigation

Metal sulfate formation is one of the major challenges to the emissions aftertreatment catalysts. Unlike the incredibly sulfation prone nature of Pd to form PdSO$_4$, no experimental evidence exits for the PtSO$_4$ formation. Given the mystery of nonexistence of the PtSO$_4$, we explore the PtSO$_4$ using a combined approach of evolutionary algorithm based search technique and quantum mechanical computations. Experimentally known PdSO$_4$ is considered for the comparison and validation of our results. We predict many possible low-energy phases of the PtSO$_4$ and PdSO$_4$ at 0K, which are further investigated under wide range of temperature-pressure conditions. An entirely new low-energy (tetragonal $P4_2/m$) structure of the PtSO$_4$ and PdSO$_4$ is predicted, which appears to be the most stable phase of the PtSO$_4$ and a competing phase of the experimentally known monoclinic $C_2/c$ phase of PdSO$_4$. Phase stability at finite temperature is further examined and verified by free energy calculations of sulfates towards their possible decomposition products. Finally, temperature-pressure phase diagrams are computationally established for both PtSO$_4$ and PdSO$_4$.

cond-mat.mtrl-sci↗

arXiv2014

Pathways Towards Ferroelectricity in Hafnia

The question of whether one can systematically identify (previously unknown) ferroelectric phases of a given material is addressed, taking hafnia (HfO$_2$) as an example. Low free energy phases at various pressures and temperatures are identified using a first-principles based structure search algorithm. Ferroelectric phases are then recognized by exploiting group theoretical principles for the symmetry-allowed displacive transitions between non-polar and polar phases. Two orthorhombic polar phases occurring in space groups $Pca2_1$ and $Pmn2_1$ are singled out as the most viable ferroelectric phases of hafnia, as they display low free energies (relative to known non-polar phases), and substantial switchable spontaneous electric polarization. These results provide an explanation for the recently observed surprising ferroelectric behavior of hafnia, and reveal pathways for stabilizing ferroelectric phases of hafnia as well as other compounds.

cond-mat.mtrl-sci↗

arXiv2014

Predicting Polymeric Crystal Structures by Evolutionary Algorithms

The recently developed evolutionary algorithm USPEX proved to be a tool that enables accurate and reliable prediction of structures for a given chemical composition. Here we extend this method to predict the crystal structure of polymers by performing constrained evolutionary search, where each monomeric unit is treated as one or several building blocks with fixed connectivity. This greatly reduces the search space and allows the initial structure generation with different sequences and packings using these blocks. The new constrained evolutionary algorithm is successfully tested and validated on a diverse range of experimentally known polymers, namely polyethylene (PE), polyacetylene (PA), poly(glycolic acid) (PGA), poly(vinyl chloride) (PVC), poly(oxymethylene) (POM), poly(phenylene oxide) (PPO), and poly (p-phenylene sulfide) (PPS). By fixing the orientation of polymeric chains, this method can be further extended to predict all polymorphs of poly(vinylidene fluoride) (PVDF), and the complex linear polymer crystals, such as nylon-6 and cellulose. The excellent agreement between predicted crystal structures and experimentally known structures assures a major role of this approach in the efficient design of the future polymeric materials.

cond-mat.mtrl-sci↗