arXiv subjects

Rajendra Paudel

Publications and source records attributed to Rajendra Paudel.

9 recordsLinked to original sources

arXiv2026

Study of Twistronics Induced Superconductivity in Twisted Bilayer Graphene

This work investigates the electronic properties of twisted bilayer graphene (TBG) through computational calculations, with the aim of understanding the emergence of flat bands and conditions favorable for superconductivity close to the magic angle. This study utilizes a k\cdot p continuum model, and the low-energy Hamiltonians are derived from angle-dependent datasets provided by Carr et al. Using this model, the band structure, density of states (DoS), and Fermi velocity are systematically calculated across a range of twist angles. The calculations are performed by discretizing high-symmetry paths in the moire Brillouin zone for band structure calculations, uniformly sampling a square grid for DoS analysis, and employing finite-difference methods to evaluate the Fermi velocity near the Dirac points. The results identify a narrow magic-angle window around $\theta \approx 0.98^\circ-1.00^\circ$, where the bands become nearly dispersionless, the DoS exhibits a sharp peak, and the Fermi velocity is strongly suppressed. This computational framework does not directly predict superconductivity, but rather establishes the electronic foundation for exploring flat-band physics and correlation-driven phenomena such as unconventional superconductivity in twisted bilayer graphene.

cond-mat.mes-hall↗

arXiv2025

How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades

The Everglades play a crucial role in flood and drought regulation, water resource planning, and ecosystem management in the surrounding regions. However, traditional physics-based and statistical methods for predicting water levels often face significant challenges, including high computational costs and limited adaptability to diverse or unforeseen conditions. Recent advancements in large time series models have demonstrated the potential to address these limitations, with state-of-the-art deep learning and foundation models achieving remarkable success in time series forecasting across various domains. Despite this progress, their application to critical environmental systems, such as the Everglades, remains underexplored. In this study, we fill the gap by investigating twelve task-specific models and five time series foundation models across six categories for a real-world application focused on water level prediction in the Everglades. Our primary results show that the foundation model Chronos significantly outperforms all other models while the remaining foundation models exhibit relatively poor performance. We also noticed that the performance of task-specific models varies with the model architectures, and discussed the possible reasons. We hope our study and findings will inspire the community to explore the applicability of large time series models in hydrological applications. The code and data are available at https://github.com/rahuul2992000/Everglades-Benchmark.

cs.LG↗

arXiv2025

Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades

Accurate water level forecasting in the Everglades is essential for flood mitigation, drought management, water resource planning, and biodiversity conservation. While recent time-series foundation models have shown strong performance on generic tasks (represented in their pre-training), their effectiveness in domain-specific applications remains insufficiently understood. In this work, we curate a domain-specific dataset for water-level forecasting in the Everglades and observe that the performance of current state-of-the-art models remains limited. To address this gap, we leverage a retrieval-augmented mechanism that retrieves analogous multivariate hydrological episodes from an external archive of historical observations to enrich the input context of those pre-trained models. We study two retrieval strategies, statistical similarity-based retrieval and mutual information-based retrieval, and analyze how incorporating retrieved historical contexts affects predictive performance. Extensive experiments show that retrieval augmentation consistently improves long-horizon water level forecasts and yields disproportionately larger gains during extreme events, which is particularly critical for environmental decision-making. Our study provides empirical evidence that analog-based retrieval can benefit pretrained time-series foundation models in environmental science, offering practical insights into their strengths, limitations, and failure modes when applied to hydrological forecasting in the Everglades. Although evaluated in the Everglades, the proposed framework is general and can be applied to other hydrological systems given time series data. The code and data have been made publicly available at https://github.com/rahuul2992000/WaterRAF.

cs.LG↗

arXiv2025

Improvement of Data Analytics Techniques in Reflection High Energy Electron Diffraction to Enable Machine Learning

Perovskite oxides such as LaFeO$_3$ are a well-studied family of materials that possess a wide range of useful and novel properties. Successfully synthesizing perovskite oxide samples usually requires a significant number of growth attempts and a detailed film characterization on each sample to find the optimal growth window of a material. The most common real-time \textit{in situ} diagnostic technique available during molecular beam epitaxy (MBE) synthesis is reflection high-energy electron diffraction (RHEED). Conventional use of RHEED allows a highly experienced operator to determine growth rate by monitoring intensity osciallations and make some qualitative observations during growth, such as recognizing the sample has become amorphous or recognizing that large islands have formed on the surface. However, due to a lack of theoretical understanding of the diffraction patterns, finer, more precise levels of observations are challenging. To address these limitations, we implement new data analytics techniques in the growth of three LaFeO$_3$ samples on Nb-doped SrTiO$_3$ by MBE. These techniques improve our ability to perform unsupervised machine learning using principal component analysis (PCA) and k-means clustering by using drift correction to overcome sample or stage motion during growth and intensity transformations that highlight more subtle features in the images such as Kikuchi bands. With this approach, we enable the first demonstration of PCA and k-means across multiple samples, allowing for quantitative comparison of RHEED videos for two LaFeO$_3$ film samples. These capabilities set the stage for real-time processing of RHEED data during growth to enable machine learning-accelerated film synthesis.

cond-mat.mtrl-sci↗

arXiv2024

Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.

cond-mat.mtrl-sci↗

arXiv2022

Band-Engineered LaFeO$_{3}$-LaNiO$_{3}$ Thin Film Interfaces for Electrocatalysis of Water

Transition metal oxides have generated significant interest for their potential as catalysts for the oxygen evolution reaction (OER) in alkaline environments. Iron and nickel-based perovskite oxides have proven particularly promising, with catalytic over-potentials rivaling precious metal catalysts when the alignment of the valence band relative to the OER reaction potential is tuned through substitutional doping or alloying. Here we report that engineering of band alignment in LaFeO$_{3}$/LaNiO$_{3}$ (LFO/LNO) heterostructures via interfacial doping yields greatly enhanced catalytic performance. Using density functional theory modeling, we predict a 0.2 eV valence band offset (VBO) between metallic LNO and semiconducting LFO that significantly lowers the barrier for hole transport through LFO compared to the intrinsic material and make LFO a p-type semiconductor. Experimental band alignment measurements using in situ X-ray photoelectron spectroscopy of epitaxial LFO/LNO heterostructures agree quite well with these predictions, producing a measured VBO of 0.3(1) eV. OER catalytic measurements on the same samples in alkaline solution show an increase in catalytic current density by a factor of ~275 compared to LFO grown on n-type Nb-doped SrTiO$_{3}$. These results demonstrate the power of tuning band alignments through interfacial band engineering for improved catalytic

cond-mat.mtrl-sci↗

arXiv2021

Thickness Dependent OER Electrocatalysis of Epitaxial LaFeO$_{3}$ Thin Films

Transition metal oxides have long been an area of interest for water electrocatalysis through the oxygen evolution and oxygen reduction reactions. Iron oxides, such as LaFeO$_{3}$, are particularly promising due to the favorable energy alignment of the valence and conduction bands comprised of Fe$^{3+}$ cations and the visible light band gap of such materials. In this work, we examine the role of band alignment on the electrocatalytic oxygen evolution reaction (OER) in the intrinsic semiconductor LaFeO$_{3}$ by growing epitaxial films of varying thicknesses on Nb-doped SrTiO$_{3}$. Using cyclic voltammetry and electrochemical impedance spectroscopy, we find that there is a strong thickness dependence on the efficiency of electrocatalysis for OER. These measurements are understood based on interfacial band alignment in the system as confirmed by layer-resolved electron energy loss spectroscopy and electrochemical Mott-Schottky measurements. Our results demonstrate the importance of band engineering for the rational design of thin film electrocatalysts for renewable energy sources.

cond-mat.mtrl-sci↗

arXiv2020

Probing Emergent Surface and Interfacial Properties in Complex Oxides via in situ X-ray Photoelectron Spectroscopy

Emergent behavior at complex oxide interfaces has driven much of the research in the oxide thin film community for the past twenty years. Interfaces have been engineered for potential applications in spintronics, topological quantum computing, and high-speed electronics in cases where the bulk materials would not exhibit the desired properties. Advances in thin film growth have made the synthesis of these interfaces possible, while surface characterization tools such as X-ray photoelectron spectroscopy have been critical to understanding surface and interfacial phenomena in these materials. In this review we discuss the leading research in the oxide field over the past 5-10 years with a focus on connecting the key results to the X-ray photoelectron spectroscopy studies that enabled them. We describe how in situ integration of synthesis and spectroscopy can be used to improve the film growth process and to perform immediate experiments on specifically tailored interfacial heterostructures. These studies can include determination of interfacial intermixing, valence band alignment, and interfacial charge transfer. We also show how advances in synchrotron-based spectroscopy techniques have answered questions that cannot be addressed in a lab-based system. By further tying together synthesis and spectroscopy through in situ techniques, we conclude by discussing future opportunities in the field through the careful design of thin film heterostructures that are optimized for X-ray studies.

cond-mat.mtrl-sci↗

arXiv2020

Machine Learning Analysis of Perovskite Oxides Grown by Molecular Beam Epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and k-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO$_{3}$ growths, heteroepitaxial SrTiO$_{3}$ grown on scandate substrates, BaSnO$_{3}$ films grown on SrTiO$_{3}$ substrates, and LaNiO$_{3}$ films grown on LaAlO$_{3}$ substrates. This analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control over growth processes and film quality.

cond-mat.mtrl-sci↗