arXiv subjects

Stefanos Papanikolaou

Publications and source records attributed to Stefanos Papanikolaou.

At least 19 recordsLinked to original sources

arXiv2026

Computational Study of Water Adsorption and Dissociative Mechanisms Impacting g-C3N4's Optical and Electronic Properties

In the quest for sustainable energy solutions, water splitting emerges as a crucial process for generating clean hydrogen a versatile and renewable fuel essential for energy storage, emissions reduction, and achieving sustainability goals. This study employs a comprehensive computational approach, utilizing atomistic simulations to systematically investigate the effects of water absorption on the electronic and optical properties of g-C3N4 nanosheets. Our methodology integrates ab initio computations grounded in density functional theory (DFT), which allows for a detailed characterization of the nanosheet and serves as a benchmark for self-consistent charge density functional tight binding (SCC-DFTB) simulations. This approach provides valuable insights into the behavior of the nanosheet under the influence of absorbed OH and H2O molecules by considering calculated parameters for photocatalytic efficiency. Additionally, we extend our investigation to classical molecular dynamics simulations within the ReaxFF framework, modeling the emission of multiple H2O molecules and assessing the subsequent rate of H2 evolution. A key finding of our study reveals that the dissociation of H2O into HO and O molecules significantly enhances both the optical absorbance and conductance of the nanosheet compared to its pristine state. These results underscore the potential of g-C3N4 nanosheets as effective materials for water splitting applications.

cond-mat.mtrl-sci↗

arXiv2025

Evaluation of tetracycline photocatalytic degradation using NiFe2O4/CeO2/GO nanocomposite for environmental remediation: In silico molecular docking, Antibacterial performance, degradation pathways, and DFT calculations

Graphene-based nanostructures with distinct structural and physicochemical characteristics may be able to photodegrade antibiotics effectively. Herein, this study reports the successful synthesis of NiFe2O4/CeO2/GO nanocomposite (NC) by anchoring NiFe2O4/CeO2 to the surface of GO (Graphene oxide). All state-of-the-art characterization techniques investigated the nanostructure, crystallinity, phonon modes, chemical composition analysis, elemental composition, surface area, magnetic properties, and optical band gap. Hydrothermal approach assisted NiFe2O4/CeO2/GO catalyst showed better charge carrier separation and prompted the tetracycline (TC-HCl) photocatalytic degradation under visible light. Following 90 minutes of exposure to visible light, NiFe2O4/CeO2/GO nanocomposite demonstrated superior photocatalytic activity, with a TC-HCl degradation rate of 95%. Reasonable mechanisms of tetracycline degradation were proposed where the OH and O played a leading role based on identified intermediates. Moreover, tetracycline photodegradation intermediates and the optimal pathway were identified using LC-MS spectrometry. This study also performed Density Functional Theory (DFT) calculations for the prepared materials to validate the experimental data. In vitro, antibacterial studies were consistent with the molecular docking investigations of the NiFe2O4/CeO2/GO nanocomposite against DNA gyrase and FabI from Escherichia coli (E. coli) and Staphylococcus aureus (S.aureus). Lastly, the outcomes revealed a new potential for NiFe2O4/CeO2/GO nanocomposite for improved photocatalytic performance, making it a promising photocatalyst for wastewater treatment.

cond-mat.mtrl-sci↗

arXiv2025

Dynamical pathways for the interaction of O2, H2O, CH4, and CO2 with a-alumina surfaces: Density functional tight-binding calculations

In this study, we investigated the physisorption mechanisms of O2, H2O, CH4, and CO2 molecules on alumina and their effect on electronic properties. We employed quantum-classical molecular dynamics simulations and the self-consistent-charge density-functional tight-binding (SCC-DFTB) approach to dynamically model these mechanisms. Our results revealed the binding pathways of O, H, and C atoms in the various molecules to Al and O atoms at the top atomic layers of the alpha-alumina surface. We examined several adsorption sites and molecular orientations relative to Al-terminated and Ox-terminated alumina surfaces and found that the most stable physisorbed state on the Al-terminated surface is located above the Al atom, while the Ox-terminated state is found above the oxygen, resulting in enhanced optical adsorbance. The dissociation of CH4 into CH2+H2 after interaction with the surface resulted in hydrogen production, but with low adsorbate rates. While, O2 molecules primarily bond to the Al atoms, leading to the highest adsorbance rate among the other molecules. Our findings provide important insights into the physisorption mechanisms of molecules on alumina and their impact on electronic properties

cond-mat.mtrl-sci↗

arXiv2024

Transfer Learning in Materials Informatics: structure-property relationships through minimal but highly informative multimodal input

In this work we propose simple, effective and computationally efficient transfer learning approaches for structure-property relation predictions in the context of materials, with highly informative input from different modalities. As materials properties stand from their electronic structure, representations are extracted directly from datasets of electronic charge density profile images using Neural Networks. We demonstrate transferability of the existing pre-trained Convolutional Neural Networks and Large Language Models knowledge to physics domain data, exploring a wide set of compositions for the regression of energetics- or structure- related properties, and the role of semantic crystallographic information in the context of multimodal approaches. We test the applicability of the CLIP multimodal model, and employ as well a training protocol for building a more interpretable and versatile stacked custom solution from different pre-trained modalities. The study offers a promising avenue for enhancing the effectiveness of descriptor identification in physical systems, shedding light on the power of multimodal transfer learning for materials property prediction. Instead of using the well-established GNN-based approaches, we explore the transfer learning of image- and text-based architectures, which can impact decision making for new low-cost AI methods in the field of Materials and Chemoinformatics.

cond-mat.mtrl-sci↗

arXiv2024

Hydrogen Diffusion in Magnesium Using Machine Learning Potentials: a comparative study

Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to address with standard ab initio techniques. This work addresses this challenge by employing accelerated machine learning (ML) molecular dynamics simulations through active learning. We conduct a comparative study of different ML-based interatomic potential schemes, including VASP, MACE, and CHGNet, utilizing various training strategies such as on-the-fly learning, pre-trained universal models, and fine-tuning.By considering different temperatures and concentration regimes, we obtain hydrogen diffusion coefficients and activation energy values which align remarkably well with experimental results, underlining the efficacy and accuracy of ML-assisted methodologies in the context of diffusive dynamics. Particularly, our procedure significantly reduces the computational effort associated with traditional transition state calculations or ad-hoc designed interatomic potentials. The results highlight the limitations of pre-trained universal solutions for defective materials and how they can be improved by fine-tuning. Specifically, fine-tuning the models on a database produced during on-the-fly training of VASP ML force-field allows the retrieving of DFT-level accuracy at a fraction of the computational cost.

cond-mat.mtrl-sci↗

arXiv2024

In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs

Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial machine learning tasks during inference only. In this work, we address the question: can we leverage in-context learning to predict out-of-distribution materials properties? However, this would not be possible for structure property prediction tasks unless an effective method is found to pass atomic-level geometric features to the transformer model. To address this problem, we employ a compound model in which GPT-2 acts on the output of geometry-aware graph neural networks to adapt in-context information. To demonstrate our model's capabilities, we partition the QM9 dataset into sequences of molecules that share a common substructure and use them for in-context learning. This approach significantly improves the performance of the model on out-of-distribution examples, surpassing the one of general graph neural network models.

cs.LG↗

arXiv2024

Unbiased Atomistic Predictions of Crystal Dislocation Dynamics using Bayesian Force Fields

Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions, starting from quantum methods, have been beyond reach. This is especially true for phenomena like stacking faults and dislocation cross-slip, which are computationally intractable with methods like density functional theory, as $\sim 10^5-10^6$ atoms are required to reliably simulate such systems. Hence, this work extends quantum-mechanical accuracy to mesoscopic molecular dynamics simulations and opens unprecedented possibilities in material design for extreme mechanical conditions with direct atomistic insight at the deformation mesoscale. To accomplish this, we construct a Bayesian machine-learned force field (MLFF) from ab initio quantum training data, enabling direct observations of high-temperature and high-stress dislocation dynamics in single-crystalline Cu with atomistic resolution. In doing so, a generalizable training protocol is developed for construction of MLFFs for dislocation kinetics, with wide-ranging applicability to other single element systems and alloys. The resulting FLARE MLFF provides excellent predictions of static bulk elastic properties, stacking fault widths and energies, dynamic evolutions and mobilities of edge and screw dislocations, as well as cross-slip energy barriers for screw dislocations, all of which are compared to available experimental measurements. This work marks the first reliable quantitative determination of dislocation mobilities and cross-slip barriers, demonstrating a substantial advancement over previous empirical and machine learned force field models.

cond-mat.mtrl-sci↗

arXiv2023

Compositional Search of Stable Crystalline Structures in Multi-Component Alloys Using Generative Diffusion Models

Exploring the vast composition space of multi-component alloys presents a challenging task for both \textit{ab initio} (first principles) and experimental methods due to the time-consuming procedures involved. This ultimately impedes the discovery of novel, stable materials that may display exceptional properties. Here, the Crystal Diffusion Variational Autoencoder (CDVAE) model is adapted to characterize the stable compositions of a well studied multi-component alloy, NiFeCr, with two distinct crystalline phases known to be stable across its compositional space. To this end, novel extensions to CDVAE were proposed, enhancing the model's ability to reconstruct configurations from their latent space within the test set by approximately 30\% . A fact that increases a model's probability of discovering new materials when dealing with various crystalline structures. Afterwards, the new model is applied for materials generation, demonstrating excellent agreement in identifying stable configurations within the ternary phase space when compared to first principles data. Finally, a computationally efficient framework for inverse design is proposed, employing Molecular Dynamics (MD) simulations of multi-component alloys with reliable interatomic potentials, enabling the optimization of materials property across the phase space.

cond-mat.mtrl-sci↗

arXiv2023

Neural Network Interatomic Potentials For Open Surface Nano-mechanics Applications

Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such purposes, we develop a robust neural-network interatomic potential (NNIP), and we provide a test for the example of molecular dynamics (MD) nanoindentation, and the case of body-centered cubic crystalline molybdenum (Mo). We employ a similarity measurement protocol, using standard local environment descriptors, to select ab initio configurations for the training dataset that capture the behavior of the indented sample. We find that it is critical to include generalized stacking fault (GSF) configurations, featuring a dumbbell interstitial on the surface, to capture dislocation cores, and also high-temperature configurations with frozen atom layers for the indenter tip contact. We develop a NNIP with distinct dislocation nucleation mechanisms, realistic generalized stacking fault energy (GSFE) curves, and an informative energy landscape for the atoms on the sample surface during nanoindentation. We compare our NNIP results with nanoindentation simulations, performed with three existing potentials -- an embedded atom method (EAM) potential, a gaussian approximation potential (GAP), and a tabulated GAP (tabGAP) potential -- that predict different dislocation nucleation mechanisms, and display the absence of essential information on the shear stress at the sample surface in the elastic region. We believe that these features render specialized NNIPs essential for simulations of nanoindentation and nano-mechanics with near-quantum accuracy.

cond-mat.mtrl-sci↗

arXiv2023

Serrated plastic flow in slowly-deforming complex concentrated alloys: universal signatures of dislocation avalanches

Under plastic flow, multi-element high/medium-entropy alloys (HEAs/MEAs) commonly exhibit complex intermittent and collective dislocation dynamics owing to inherent lattice distortion and atomic-level chemical complexities. Using atomistic simulations, we report on an avalanche study of slowly-driven model face-centered cubic (fcc) NiCoCrFeMn and NiCoCr chemically complex alloys aiming for microstructural/topological characterization of associated dislocation avalanches. The results of our avalanche simulations reveal a close correspondence between the observed serration features in the stress response of the deforming HEA/MEA and the incurred slip patterns within the bulk crystal. We show that such correlations become quite pronounced within the rate-independent (quasi-static) regime exhibiting scale-free statistics and critical scaling features as universal signatures of dislocation avalanches.

cond-mat.mtrl-sci↗

arXiv2023

Alloy Informatics through Ab Initio Charge Density Profiles: Case Study of Hydrogen Effects in Face-Centered Cubic Crystals

Materials design has traditionally evolved through trial-error approaches, mainly due to the non-local relationship between microstructures and properties such as strength and toughness. We propose 'alloy informatics' as a machine learning based prototype predictive approach for alloys and compounds, using electron charge density profiles derived from first-principle calculations. We demonstrate this framework in the case of hydrogen interstitials in face-centered cubic crystals, showing that their differential electron charge density profiles capture crystal properties and defect-crystal interaction properties. Radial Distribution Functions (RDFs) of defect-induced differential charge density perturbations highlight the resulting screening effect, and, together with hydrogen Bader charges, strongly correlate to a large set of atomic properties of the metal species forming the bulk crystal. We observe the spontaneous emergence of classes of charge responses while coarse-graining over crystal compositions. Nudge-Elastic-Band calculations show that RDFs and charge features also connect to hydrogen migration energy barriers between interstitial sites. Unsupervised machine-learning on RDFs supports classification, unveiling compositional and configurational non-localities in the similarities of the perturbed densities. Electron charge density perturbations may be considered as bias-free descriptors for a large variety of defects.

cond-mat.mtrl-sci↗

arXiv2023

Tuning brittleness in multi-component metallic glasses through chemical disorder aging

Shear localization in slowly-driven bulk metallic glasses (BMGs) is typically accompanied by a sharp drop in the bulk stress response as a signature of the plastic yielding transition. It is also observed that the sharpness of this elastic-plastic dynamical transition depends on the extent of local chemical and microstructural orders, as well as the glass preparation protocol ( ie. thermal annealing). Here, we investigate sheared multi-element BMGs in molecular dynamics (MD) simulations, and demonstrate that glass aging, implemented through a hybrid Monte-Carlo(MC)-MD process, sharpens the elastic-plastic transition through a distinct crossover, seen in strain patterns that gradually shift from diffuse features in as-quenched samples to localized (yet system-spanning) patterns in well-annealed glasses. This effect of glass aging on the elastic-plastic transition is found to be correlated to the inherent interplay between aging-induced icosahedra ordering and co-operative formation of shear transformation zones. The observed crossover is quantified through a measure of the age-dependent susceptibility to plastic rearrangements, exhibiting strong (anti-)correlations to local ordering features, and the corresponding spatial correlation length grows with the aging timescale.

cond-mat.soft↗

arXiv2023

Substitutional Alloying Using Crystal Graph Neural Networks

Materials discovery, especially for applications that require extreme operating conditions, requires extensive testing that naturally limits the ability to inquire the wealth of possible compositions. Machine Learning (ML) has nowadays a well established role in facilitating this effort in systematic ways. The increasing amount of available accurate DFT data represents a solid basis upon which new ML models can be trained and tested. While conventional models rely on static descriptors, generally suitable for a limited class of systems, the flexibility of Graph Neural Networks (GNNs) allows for direct learning representations on graphs, such as the ones formed by crystals. We utilize crystal graph neural networks (CGNN) to predict crystal properties with DFT level accuracy, through graphs with encoding of the atomic (node/vertex), bond (edge), and global state attributes. In this work, we aim at testing the ability of the CGNN MegNet framework in predicting a number of properties of systems previously unseen from the model, obtained by adding a substitutional defect in bulk crystals that are included in the training set. We perform DFT validation to assess the accuracy in the prediction of formation energies and structural features (such as elastic moduli). Using CGNNs, one may identify promising paths in alloy discovery.

cond-mat.mtrl-sci↗

arXiv2023

Multiscale modeling of kinetic sluggishness in equiatomic NiCoCr and NiCoCrFeMn single-phase solid solutions

Complex, concentrated, multi-component alloys have been shown to display outstanding thermo-mechanical properties, that have been typically attributed to sluggish diffusion, entropic, and lattice distortion effects. Here, we investigate two metal alloys with such exemplary properties, the equiatomic, single-phase, face-centered-cubic (FCC) alloys NiCoCr and NiCoCrFeMn, and we compare their microstructural kinetics to the behaviors in a pure-Ni FCC metal. We perform long-time, kinetic Monte Carlo (kMC) simulations, and we analyze in detail the kinetics of atomic vacancies. We find that vacancies in both concentrated alloys exhibit subdiffusive thermally driven dynamics, in direct contrast to the diffusive dynamics of pure Ni. Subdiffusive dynamics shall be attributed to dynamical sluggishness, that is modeled by a fractional Brownian random walk. Furthermore, we analyze the statistics of waiting times, and we interpret long power-law-distributed rest periods as a direct consequence of barriers' energy-scales and lattice distortions.

cond-mat.mtrl-sci↗

arXiv2022

Prediction of steel nanohardness by using graph neural networks on surface polycrystallinity maps

As a bulk mechanical property, nanoscale hardness in polycrystalline metals is strongly dependent on microstructural features that are believed to be heavily influenced from complex features of polycrystallinity -- namely, individual grain orientations and neighboring grain properties. We train a graph neural network (GNN) model, with each grain center location being a graph node, to assess the predictability of micromechanical responses of nano-indented low-carbon 310S stainless steel (alloyed with Ni and Cr) surfaces, solely based on surface polycrystallinity, captured by electron backscatter diffraction maps. The grain size distribution ranges between $1-100~μ$m, with mean grain size at $18~μ$m. The GNN model is trained on a set of nanomechanical load-displacement curves, obtained from nanoindentation tests and is subsequently used to make predictions of nano-hardness at various depths, with sole input being the grain locations and orientations. Model training is based on a sub-standard set of $\sim10^2$ hardness measurements, leading to an overall satisfactory performance. We explore model performance and its dependence on various structural/topological grain-level descriptors, such as the grain size and number of nearest neighbors. Analogous GNN model frameworks may be utilized for quick, inexpensive hardness estimates, for guidance to detailed nanoindentation experiments, akin to cartography tool developments in the world exploration era.

cond-mat.mtrl-sci↗

arXiv2022

Detection of the onset of yielding and creep failure from digital image correlation

There are a multitude of applications in which structural materials would be desired to be nondestructively evaluated, while in a component, for plasticity and failure characteristics. In this way, safety and resilience features can be significantly improved. Nevertheless, while failure can be visible through cracks, plasticity is commonly invisible and highly microstructure-dependent. Here, we show that an equation-free method based on principal component analysis is capable of detecting yielding and tertiary creep onset, directly from strain fields that are obtained by digital image correlation, applicable on components, continuously and nondestructively. We demonstrate the applicability of the method to yielding of Ni-based Haynes 230 metal alloy polycrystalline samples, which are also characterized through electron microscopy and benchmarked using continuum polycrystalline plasticity modeling. Also, we successfully apply this method to yielding during uniaxial tension of Hastelloy X polycrystalline samples, and also to the onset of tertiary creep in quasibrittle fiber composites under uniaxial tension. We conclude that there are key features in the spatiotemporal fluctuations of local strain fields that can be used to infer mechanical properties.

cond-mat.mtrl-sci↗

arXiv2022

Dislocation plasticity in equiatomic NiCoCr alloys: The effect of short-range order

Equiatomic NiCoCr solid solutions have been recently shown to display exceptional mechanical properties, that have been suggested to be linked to nanostructural short-range order (SRO) features that may arise from thermal treatments, such as annealing or/and aging. Here, we use hybrid Monte Carlo-Molecular Dynamics simulations to gain further insights of thermal effects on the SRO formation as well as the edge dislocation plasticity mechanisms of equiatomic NiCoCr face-centered cubic solid solution. For that purpose, we utilize two well-known NiCoCr interatomic potentials, one of which displays well documented SRO, believed to be linked to experimental evidence and labeled as the Li-Sheng-Ma potential, while the other (Farkas-Caro) does not. We use these two potentials to discern short range ordering (from inherent randomness in random solid solutions) and understand how SROs influence dislocation depinning dynamics in various thermal annealing scenarios. In this context, we used robust, scale-dependent metrics to infer a characteristic SRO size in the Li-Sheng-Ma case by probing local concentration fluctuations which otherwise indicate uncorrelated patterns in the Farkas-Caro case in a close agreement with random alloys. Using relevant order parameters, we also report on the drastic increase of chemical ordering within the stacking fault region. More importantly, we find that the Li-Sheng-Ma potential leads to excellent edge dislocation depinning strength with low stacking fault width. Our findings indicate an enhanced roughening mechanism due to the SROs-misfit synergy that leads to significant improvements in dislocation glide resistance. We argue that the improvements in alloy strength have their atomistic origins in the interplay between nanoscopic SROs and atomic-level misfit properties.

cond-mat.mtrl-sci↗

arXiv2022

Yielding in multi-component metallic glasses: Universal signatures of elastic modulus heterogeneities

Sheared multi-component bulk metallic glasses are characterized by both chemical and structural disorder that define their properties. We investigate the behavior of the local, microstructural elastic modulus across the plastic yielding transition in six Ni-based multi-component glasses, that are characterized by compositional features commonly associated with solid solution formability. We find that elastic modulus fluctuations display consistent percolation characteristics pointing towards universal behavior across chemical compositions and overall yielding sharpness characteristics. Elastic heterogeneity grows upon shearing via the percolation of elastically soft clusters within an otherwise rigid amorphous matrix, confirming prior investigations in granular media and colloidal glasses. We find clear signatures of percolation transition with spanning clusters that are universally characterized by scale-free characteristics and critical scaling exponents. The spatial correlation length and mean cluster size tend to diverge prior to yielding, with associated critical exponents that exhibit fairly weak dependence on compositional variations as well as macroscopic stress-strain curve details.

cond-mat.mtrl-sci↗