arXiv subjects

Stefanos Giaremis

Publications and source records attributed to Stefanos Giaremis.

7 recordsLinked to original sources

arXiv2026

Nanoscale Phase Distribution Governs Exchange Bias in Multiphase Magnetic Nanoparticles

Exchange bias at ferromagnet-antiferromagnet interfaces underpins magnetic memory, spintronic devices, and nanoscale electromagnetic technologies, yet its behaviour in complex nanoscale heterostructures remains poorly understood. Here we uncover how exchange bias emerges in functional multiphase metal-oxide nanoparticles by combining gas-phase synthesis, advanced magnetic characterisation, and first-principles-informed spin-dynamics simulations. Using Ni-Cr/NiO nanoparticles as a model system, we show that exchange bias is governed not simply by the presence of ferromagnetic and antiferromagnetic phases, but critically by their nanoscale spatial distribution and interfacial topology. At 10 K, significant negative exchange bias (0.8 kOe) and coercivity enhancement (1.4 kOe) was exhibited; both decreased due to either Cr-segregation (at low Cr content) or to Cr accumulation inside the core (at high Cr content). The resulting competition between magnetic phases produces a temperature-driven inversion from negative to positive exchange bias and a crossover from exchange-dominated to dipolar interactions. By linking density-functional-theory calculations directly to spin-dynamics simulations of nanoparticle ensembles, we establish a predictive framework for designing exchange-coupled nanomagnets capable of operating beyond the superparamagnetic limit.

cond-mat.mtrl-sci↗

arXiv2026

StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model

Storm surge forecasting remains a critical challenge in mitigating the impacts of tropical cyclones on coastal regions, particularly given recent trends of rapid intensification and increasing nearshore storm activity. Traditional high fidelity numerical models such as ADCIRC, while robust, are often hindered by inevitable uncertainties arising from various sources. To address these challenges, this study introduces StormNet, a spatio-temporal graph neural network (GNN) designed for bias correction of storm surge forecasts. StormNet integrates graph convolutional (GCN) and graph attention (GAT) mechanisms with long short-term memory (LSTM) components to capture complex spatial and temporal dependencies among water-level gauge stations. The model was trained using historical hurricane data from the U.S. Gulf Coast and evaluated on Hurricane Idalia (2023). Results demonstrate that StormNet can effectively reduce the root mean square error (RMSE) in water-level predictions by more than 70\% for 48-hour forecasts and above 50\% for 72-hour forecasts, as well as outperform a sequential LSTM baseline, particularly for longer prediction horizons. The model also exhibits low training time, enhancing its applicability in real-time operational forecasting systems. Overall, StormNet provides a computationally efficient and physically meaningful framework for improving storm surge prediction accuracy and reliability during extreme weather events.

cs.LG↗

arXiv2026

HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks

The coastal regions of the eastern and southern United States are impacted by severe storm events, leading to significant loss of life and properties. Accurately forecasting storm surge and wind impacts from hurricanes is essential for mitigating some of the impacts, e.g., timely preparation of evacuations and other countermeasures. Physical simulation models like the ADCIRC hydrodynamics model, which run on high-performance computing resources, are sophisticated tools that produce increasingly accurate forecasts as the resolution of the computational meshes improves. However, a major drawback of these models is the significant time required to generate results at very high resolutions, which may not meet the near real-time demands of emergency responders. The presented work introduces HURRI-GAN, a novel AI-driven approach that augments the results produced by physical simulation models using time series generative adversarial networks (TimeGAN) to compensate for systemic errors of the physical model, thus reducing the necessary mesh size and runtime without loss in forecasting accuracy. We present first results in extrapolating model bias corrections for the spatial regions beyond the positions of the water level gauge stations. The presented results show that our methodology can accurately generate bias corrections at target locations spatially beyond gauge stations locations. The model's performance, as indicated by low root mean squared error (RMSE) values, highlights its capability to generate accurate extrapolated data. Applying the corrections generated by HURRI-GAN on the ADCIRC modeled water levels resulted in improving the overall prediction on the majority of the testing gauge stations.

cs.LG↗

arXiv2024

Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracy

Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm surge forecast models with respect to real-world water height observations from gauge stations during hurricane events. The overall goal of this work is to predict the systemic error of the physics model and use it to improve the accuracy of the simulation results post factum. We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the U.S. and we tested its performance in bias correcting modeled water level data predictions from hurricane Ian (2022). We show that our model can consistently improve the forecasting accuracy for hurricane Ian -- unknown to the ML model -- at all gauge station coordinates used for the initial data. Moreover, by examining the impact of using different subsets of the initial training dataset, containing a number of relatively similar or different hurricanes in terms of hurricane track, we found that we can obtain similar quality of bias correction by only using a subset of six hurricanes. This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the produced simulation accuracy. The presented work is an important first step in creating a bias correction system for real-time storm surge forecasting applicable to the full simulation area. It also presents a highly transferable and operationally applicable methodology for improving the accuracy in a wide range of physics simulation scenarios beyond storm surge forecasting.

cs.LG↗

arXiv2023

Density functional description of long-range electron Coulomb interactions in bulk SnS

A high-throughput benchmarking technique for testing the performance of different exchange-correlation functionals and pseudopotentials is proposed and applied to bulk SnS. It is shown that, contrary to the popular view that the local density approximation can best describe layered materials, a semilocal pseudopotential with a functional having a gradient dependence better described lattice vectors and `tetragonicity' of the lattice. We classify the pseudopotentials based on this value and show that the participation ratio of maximally localized Wannier functions follows the theory which states that more distorted structures have higher anti-bonding hybridization as stabilizing factor. In order to classify pseudopotentials, the local and nonlocal potential contributions to the dynamical Born effective charges are taken for each pseudopotential. Finally, a strategy is proposed for learning exchange-correlation functionals based on the distinction between short and long range parts of the Kohn-Sham potential.

cond-mat.mtrl-sci↗

arXiv2023

Ab initio investigation of the effects of B-doping on the adsorption of H2O, H2 and O2 molecules at diamond surfaces

Boron doped diamond is extensively studied for its use in tribological and electrochemical applications due to its remarkable physical and chemical properties. However, ambient conditions play a major role to its macroscopically observed behavior. In this study, the fundamental interactions between the low Miller index (001), (110) and (111) B-doped diamond surfaces with H2O, H2 and O2 molecules, which are commonly present in ambient air and commonly involved in electrochemical reactions, are investi-gated by means of ab initio simulations. The results are presented in close comparison with previous studies on undoped diamond surfaces to reveal the impact of B on the adsorption properties. It is demonstrated that the B dopant is preferably incorporated on the topmost carbon layer and enhances the physisorption of H2O by forming a dative bond with O, while, in some cases, it can weaken the ad-sorption of O2, compared to the undoped diamond. Moreover, a noticeable displacement of the surface atoms attached to the fragment of the dissociated H2O and O2 molecules was observed, which can be associated to the first stage of wear at the atomistic level. These qualitative and quantitative results aim to provide useful insight towards the development of improved protective coatings and electrochemical devices.

cond-mat.mtrl-sci↗

arXiv2019

Decorated dislocations against phonon propagation for thermal management

The impact of decorated dislocations on the effective thermal conductivity of GaN is investigated by means of equilibrium molecular dynamics simulations via the Green-Kubo approach. The formation of "nanowires" by a few atoms of In in the core of dislocations in wurtzite GaN is found to affect the thermal properties of the material, as it leads to a significant decrease of the thermal conductivity, along with an enhancement of its anisotropic character. The thermal conductivity of In-decorated dislocations is compared to the ones of pristine GaN, InN, and random and ordered InxGa1-xN alloy, to examine the impact of doping. Results are explained by the stress maps, the bonding properties and the phonon density of states of the aforementioned systems. The decorated dislocations engineering is a novel way to tune, among other transport properties, the effective thermal conductivity of materials at the nanoscale, which can lead to the manufacturing of interesting candidates for thermoelectric or anisotropic thermal dissipation devices.

cond-mat.mtrl-sci↗