jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. Kvashin | N. Anento | L. Malerba
Source: Journal of Nuclear Materials (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Point defect segregation at edge dislocations in α-Fe studied by kinetic activation-relaxation technique The mechanical properties of crystalline materials such as metals, are strongly related to the mobility of dislocations, which is directly affected by their interaction with other defects present in the microstructure and acting as obstacles. Under irradiation conditions the number density of point defects increases substantially, leading to several phenomena at the atomic scale, some of which are related with the behaviour of dislocations as sinks for vacancies and self-interstitial atoms. In this work we present an in-depth study of the segregation process of point defects to an edge dislocation in a-Fe, performed with an on-the-fly kinetic Monte Carlo model, the kinetic activation-relaxation technique (k-ART). Our KMC simulations show that, in the vicinity of the dislocation core, the dynamics of vacancies and SIAs is accelerated before absorption. For the former, the preferential path is along the compression region while for the latter is along the tensile region. This work therefore provides a greater knowledge of the dynamic properties of point defects around of dislocations, such as free migration time, acceleration/deceleration of point defects motion and energies of absorption events. These results will allow more precise modelling of the microstructure evolution of polycrystalline materials, improving the predictive capabilities of existing models in the long term. In order to ensure transferability of these findings to other KMC models, the data obtained in the simulations have been used to train a prediction model based on a Machine Learning logistic regression algorithm. Dislocation ; Kinetic Monte Carlo ; Crystallographic defect ; Microstructure ; Work (physics) ; Enhanced Data Rates for GSM Evolution ; Kinetic energy ; Compression (physics) Fusion materials and technologies ; Nuclear Materials and Properties ; High Entropy Alloys Studies
Authors: Kishore Kumar Venkatesan | Sathiyan Samikannu
Source: ITM Web of Conferences (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Intelligent Data-Driven Modeling of SARS-CoV-2 Interactions in BP–MXene–BP Heterostructure SPR Biosensors using Ridge Regression Model The global SARS-CoV-2 pandemic has emphasized the urgent need for rapid, accurate, and scalable diagnostic technologies suitable for widespread screening. Conventional laboratory methods such as RT-PCR and ELISA, although reliable, suffer from long turnaround times, high operational cost, and dependence on specialized personnel, limiting their applicability in resource-constrained environments. Surface Plasmon Resonance (SPR) biosensors have emerged as promising alternatives, offering real-time, label-free molecular detection with high sensitivity and specificity. Recent advances highlight that integrating 2D nanomaterials—particularly BP/MXene multilayer heterostructures—significantly enhances plasmonic field confinement, signal strength, sensitivity, and detection accuracy compared to traditional metal-only configurations. However, modeling and optimizing such advanced SPR architectures typically depend on computationally intensive analytical methods, such as the Trans- fer Matrix Method and Fresnel formulations, which rely on idealized material parameters and are difficult to scale for real-time optimization. To address these limitations, this work introduces an intelligent machine- learning-based prediction framework for CaF2/Ag/BP/MXene/BP SPR biosensors using regression models to learn nonlinear relationships between structural parameters and performance metrics, including sensitivity and resonance wavelength. The proposed data-driven approach enables faster and more accurate performance estimation without exhaustive simulations, supporting rapid optimization across diverse operating scenarios. By combining plasmonic nanostructures with AI- assisted predictive modeling, this study establishes a foundation for intelligent, self-optimizing SPR diagnostic platforms suitable for next-generation biomedical applications. Surface plasmon resonance ; Sensitivity (control systems) ; Scalability ; Benchmark (surveying) ; Biosensor ; Field (mathematics) ; Plasmon ; Nonlinear system MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; SARS-CoV-2 detection and testing
Authors: Mohammad Bagheri | Ethan Berger | Hannu‐Pekka Komsa | Pekka Koskinen
Source: Chemistry of Materials (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Massive Discovery of Low-Dimensional Materials from Universal Computational Strategy Low-dimensional materials have attractive properties that drive intense efforts toward the discovery of novel materials. However, experiments are tedious for systematic discovery, and the present computational methods are often tuned to two-dimensional (2D) materials, overlooking other low-dimensional materials. Here, we combined universal machine-learning interatomic potentials (UMLIPs) and an advanced, interatomic force constant (FC)-based dimensionality classification method to make a massive discovery of novel low-dimensional materials. We first benchmarked the UMLIPs’ first-principles-level accuracy in quantifying FCs and calculated phonons for 35,689 materials from the Materials Project database. We then used the FC-based method for dimensionality classification to discover 9139 low-dimensional materials, including 1838 0D clusters, 1760 1D chains, 3057 2D sheets/layers, and 2484 mixed-dimensional materials, all of which conventional geometric descriptors have not recognized. By calculating the binding energies for the discovered 2D materials, we also identified 887 sheets that could be easily or potentially exfoliated from their parent bulk structures. Curse of dimensionality ; Interatomic potential ; Force constant ; Phonon ; Constant (computer programming) Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Fengqi Zhang | Yang Ren
Source: Chinese Physics B (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Heat recycling with magnetocaloric materials for sustainable energy conversion Abstract Solid-state magnetocaloric effects (MCE) induced by external magnetic fields offer a promising pathway toward sustainable energy conversion technologies, utilizing field-driven temperature changes for efficient solid-state cooling and heating. This review examines magnetocaloric materials (MCMs) in the context of sustainable heat recycling, summarizing representative material systems and key advances from the past decade. The discussion is organized around a materials-applications-mechanism framework to provide a coherent perspective. Finally, we outline existing challenges and future opportunities in the field. Deeper mechanistic insight, combined with advances in manufacturing and the integration of artificial intelligence and machine learning, is expected to accelerate the development of magnetocaloric energy conversion technologies toward practical implementation. Such synergistic progress will accelerate the deployment of these technologies for carbon-neutral heat recycling. Magnetic refrigeration ; Software deployment ; Context (archaeology) ; Sustainable energy ; Energy transformation ; Efficient energy use ; Key (lock) ; Sustainable development Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Phase Change Materials Research
Authors: Florian Girault | Louïse Toualbi | Alexandre Tanguy | Anna Ask | Éric CHARKALUK
Source: Mechanics of Materials (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Unraveling plastic strain localization in aluminum polycrystals by coupling of in situ high-resolution digital image correlation and crystal plasticity Digital image correlation ; Plasticity ; Microstructure ; Crystal plasticity ; Deformation (meteorology) ; Speckle pattern ; Intermetallic ; Brittleness ; Ultimate tensile strength ; Aluminium Microstructure and mechanical properties ; High-Velocity Impact and Material Behavior ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Xiao Wang | Qian-Qian Li | Jing-Yuan Ma | Xun Zhang | Xue Wu | Qi Han | Ziying Jian | Luhai Wang | Bin Yan | Haoan Wu | Ming Ma | Zheng Ge | Yu Zhang
Source: Analytical Chemistry (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Molecularly Imprinted Nanozyme Intelligent Array Sensing Platform Realizes the Enrichment and Quantitative Detection of Small Extracellular Vesicles As tumor-associated biomarkers, proteins on small extracellular vesicles (sEVs) play a critical role in revealing tumor subtypes and disease progression. Nevertheless, the rapid isolation of sEVs and the quantitative detection of their surface proteins remain challenging. In this study, we developed a nanozyme (PB-MIP-PEG) array sensing platform based on molecular imprinting technology (MIT), which can efficiently enrich sEVs and simultaneously detect three proteins: CD20, CD19, and PD-L1. Fe<sub>3</sub>O<sub>4</sub>@TiO<sub>2</sub> magnetic beads interact with phosphate groups in the sEVs phospholipid bilayer and, under an external magnetic field, achieve effective separation from complex samples. Prussian blue (PB) nanozymes with peroxidase (POD)-like activity, coated with a silicon dioxide (SiO<sub>2</sub>) imprinting layer, showed enhanced enrichment toward 3,3',5,5'-tetramethylbenzidine (TMB), which contributed to improved catalytic activity and detection sensitivity. Furthermore, density functional theory (DFT) and molecular dynamics (MD) simulations further revealed that hydrogen bonding and π-π stacking are the dominant interactions during the imprinting process. By integration of colorimetric imaging with a position-encoding system and employing a convolutional neural network-long short-term memory (CNN-LSTM) model for automated feature extraction, the concentrations of CD20, CD19, and PD-L1 were rapidly distinguished. This array sensing platform, which integrates molecularly imprinted nanozymes with neural network models, provides a new approach for the intelligent analysis of sEVs surface proteins and demonstrates a potential application value in lymphoma-related monitoring. Molecular imprinting ; Prussian blue ; Stacking ; Analyte ; Sensor array ; Molecular recognition ; Molecularly imprinted polymer ; Horseradish peroxidase Advanced Nanomaterials in Catalysis ; Extracellular vesicles in disease ; Advanced biosensing and bioanalysis techniques
Authors: Sherman W. Marcus | Natanel Nissan | Vinay K. Killamsetty | Ravi Kant Yadav | Dan Raviv | Giryes, Raja | Ariel Epstein
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing Selective State Space Models to Enhance Semianalytical Design of Fabrication-Ready Multilayered Huygens' Metasurfaces: Part I - Field-based Semianalytical Synthesis Planar metasurfaces can profoundly control electromagnetic scattering. At microwave frequencies, such devices are typically implemented using multilayer cascades of patterned metallic sheets, whose design often requires time-consuming full-wave optimization. Here, we extend analytical models originally developed for sparse loaded-wire metagratings to accurately describe densely packed Jerusalem-cross meta-atoms embedded in standard printed circuit board (PCB) dielectric stacks. The model captures both near- and far-field coupling within and between layers, enabling efficient prediction of the dual-polarized response. Using this framework, we identify highly transmissive meta-atoms whose phase is controlled by the leg lengths of the Jerusalem crosses (microscopic design stage). This (phase)-(leg-length) "lookup table" allows rapid synthesis of Huygens' metasurfaces (macroscopic design stage), demonstrated through a full-wave-validated metalens exhibiting low-reflection beam manipulation. Notably, we implement a judicious scaling method to further extend the model to predict wideband meta-atom responses. In the companion paper (Part II), a hybrid machine-learning approach leverages this semianalytical framework to enhance accuracy without requiring the conventional exhaustive full-wave training, enabling ultrafast inverse design across the full parameter space. Overall, the presented methodology -- the standalone semianlytical scheme (Part I) and the machine-learning enhanced version (Part II) -- establishes an effective open-source toolkit for versatile, rapid, and highly accurate synthesis of fabrication-ready dual-polarized transmissive Huygens' meta-atoms and metasurfaces. Planar ; Microwave ; Coupling (piping) ; Inverse ; Wideband ; Scaling ; Design tool ; Circuit design ; Printed circuit board Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Raiane Ferreira Monteiro | João Marcos T. Palheta | Tulio G. Grison | Octavio Rodrigues Filho | Renato Luis Tame Parreira | Diego Guedes‐Sobrinho | Celso R. C. Rêgo | Alexandre C. Dias | Krys Elly de Araújo Batista | Maurício J. Piotrowski
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters Transition-metal nanoclusters exhibit structural and electronic properties that depend on their size, often making them superior to bulk materials for heterogeneous catalysis. However, their performance can be limited by sulfur poisoning. Here, we use dispersion-corrected density functional theory (DFT) and physics-informed machine learning to map how atomic sulfur adsorbs and causes poisoning on 13-atom icosahedral clusters from 30 different transition metals (3$d$ to 5$d$). We measure which sites sulfur prefers to adsorb to, the thermodynamics and energy breakdown, changes in structure, such as bond lengths and coordination, and electronic properties, such as $\varepsilon_d$, the HOMO-LUMO gap, and charge transfer. Vibrational analysis reveals true energy minima and provides ZPE-based descriptors that reflect the lattice stiffening upon sulfur adsorption. For most metals, the metal-sulfur interaction mainly determines adsorption energy. At the same time, distortion penalties are usually moderate but can be significant for a few metals, suggesting these are more likely to restructure when sulfur is adsorbed. Using unsupervised \textit{k}-means clustering, we identify periodic trends and group metals based on their adsorption responses. Supervised regression models with leave-one-feature-out analysis identify the descriptors that best predict adsorption for new samples. Our results highlight the isoelectronic triad \ce{Ti}, \ce{Zr}, and \ce{Hf} as a balanced group that combines strong sulfur binding with minimal structural change. Additional DFT calculations for \ce{SO2} adsorption reveal strong binding and a clear tendency toward dissociation on these clusters, linking electronic states, lattice response, and poisoning strength. These findings offer data-driven guidelines for designing sulfur-tolerant nanocatalysts at the subnanometer scale. Nanoclusters ; Adsorption ; Sulfur ; Density functional theory ; Icosahedral symmetry ; Dissociation (chemistry) ; Binding energy ; Electronic structure Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Raiane Ferreira Monteiro | João Marcos T. Palheta | Tulio G. Grison | Octavio Rodrigues Filho | Renato Luis Tame Parreira | Diego Guedes‐Sobrinho | Celso R. C. Rêgo | Alexandre C. Dias | Krys Elly de Araújo Batista | Maurício J. Piotrowski
Source: arXiv (Cornell University) (preprint, 2026-01-20)
Field: Materials Science — Materials Chemistry

Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters Transition-metal nanoclusters exhibit structural and electronic properties that depend on their size, often making them superior to bulk materials for heterogeneous catalysis. However, their performance can be limited by sulfur poisoning. Here, we use dispersion-corrected density functional theory (DFT) and physics-informed machine learning to map how atomic sulfur adsorbs and causes poisoning on 13-atom icosahedral clusters from 30 different transition metals (3$d$ to 5$d$). We measure which sites sulfur prefers to adsorb to, the thermodynamics and energy breakdown, changes in structure, such as bond lengths and coordination, and electronic properties, such as $\varepsilon_d$, the HOMO-LUMO gap, and charge transfer. Vibrational analysis reveals true energy minima and provides ZPE-based descriptors that reflect the lattice stiffening upon sulfur adsorption. For most metals, the metal-sulfur interaction mainly determines adsorption energy. At the same time, distortion penalties are usually moderate but can be significant for a few metals, suggesting these are more likely to restructure when sulfur is adsorbed. Using unsupervised \textit{k}-means clustering, we identify periodic trends and group metals based on their adsorption responses. Supervised regression models with leave-one-feature-out analysis identify the descriptors that best predict adsorption for new samples. Our results highlight the isoelectronic triad \ce{Ti}, \ce{Zr}, and \ce{Hf} as a balanced group that combines strong sulfur binding with minimal structural change. Additional DFT calculations for \ce{SO2} adsorption reveal strong binding and a clear tendency toward dissociation on these clusters, linking electronic states, lattice response, and poisoning strength. These findings offer data-driven guidelines for designing sulfur-tolerant nanocatalysts at the subnanometer scale. Nanoclusters ; Adsorption ; Sulfur ; Density functional theory ; Icosahedral symmetry ; Dissociation (chemistry) ; Binding energy ; Electronic structure Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Aviraj M. Teli | Sonali A. Beknalkar | Ninad B. Velhal | Rutuja U. Amate | Pritam J. Morankar | Rushikesh P. Dhavale | Tukaram D. Dongale | Ho-Sung Kim | Jae Cheol Shin
Source: Journal of Alloys and Compounds (article, 2026-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Boosting energy storage performance via electron pathway modulation in hierarchical NiCo oxide-sulphide hybrids Energy storage ; Electrode ; Supercapacitor ; Electrolyte ; Electrochemistry ; Capacitance ; Electrochemical energy storage ; Nanorod ; Fabrication Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Markus Becker
Source: Journal of Solution Chemistry (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Revisiting Solvent Effects: Dynamic Solvation Fields in Perovskite, Sol–Gel, and Electrodeposited Films Abstract Recent experimental and computational studies increasingly demonstrate that solvent dynamics play a decisive role in nucleation, crystallization, and interfacial kinetics during solution-based thin-film deposition. Across perovskite, sol–gel, and electrodeposited systems, time-dependent solvent reorganization and interfacial structuring have been shown to directly influence film morphology, defect formation, and functional performance. Despite this growing evidence, solvent effects are still predominantly rationalized using static descriptors such as dielectric constant, polarity, or donor number. These macroscopic averages fail to capture the dynamic, time-dependent processes that govern precursor coordination, nucleation, and crystallization under nonequilibrium conditions. This mini-review consolidates experimental and computational evidence showing that solvent reorganization, interfacial structuring, and fluctuating solvation fields critically shape film morphology and functionality. Time-resolved spectroscopies (SFG, 2D-IR, ultrafast X-ray) and in situ scattering (GIWAXS/GISAXS) reveal femtosecond to second-scale solvent dynamics that steer phase transitions and defect formation in perovskite, sol–gel, and electrodeposited systems. Complementary ab initio molecular dynamics (AIMD) and multiscale simulations demonstrate how fluctuating solvation fields modulate reaction barriers and interfacial kinetics. Across materials classes, dynamic solvation—rather than equilibrium solvent parameters—emerges as the controlling factor for structure evolution and performance. The review outlines experimental–computational strategies to quantify solvent fluctuation timescales, proposes dynamic descriptors as new design parameters, and discusses how machine learning can integrate time-resolved solvation data for predictive solvent and process optimization in thin-film science. Graphical Abstract TOC: Beyond static solvent parameters toward dynamic, field-based descriptions of solvation and reactivity in thin-film processes. Solvation ; Solvent ; Non-equilibrium thermodynamics ; Solvent effects ; Molecular dynamics ; Dielectric ; Crystallization ; Solvation shell Machine Learning in Materials Science ; Perovskite Materials and Applications ; TiO2 Photocatalysis and Solar Cells
Authors: Chenyan Wang | Mouyang Cheng | Ji Chen
Source: Open MIND (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

Beyond overcomplication: a linear model suffices to decode hidden structure-property relationships in glasses Establishing reliable and interpretable structure-property relationships in glasses is a longstanding challenge in condensed matter physics. While modern data-driven machine learning techniques have proven highly effective in establishing structure-property correlations, many models are criticized for lacking physical interpretability and being task-specific. In this work, we identify an approximate linear relation between structure profiles and disorder-induced responses of glass properties based on first order perturbation theory. We analytically demonstrate that this relationship holds universally across glassy systems with varying dimensions and distinct interaction types. This robust theoretical relationship motivates the adoption of linear machine learning models, which we show numerically to achieve surprisingly high predictive accuracy for structure-property mapping in a wide variety of glassy materials. We further devise regularization analysis to further enhance the interpretability of our model, bridging the gap between predictive performance and physical insight. Overall, this linear relation establishes a simple yet powerful connection between structural disorder and spectral properties in glasses, opening a new avenue for advancing their studies. Interpretability ; Bridging (networking) ; Variety (cybernetics) ; Regularization (linguistics) ; Linear model ; Linear response theory ; Perturbation (astronomy) ; Relation (database) Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Phase-change materials and chalcogenides
Authors: Brenna Ren | Eli Krasnoff | Dhruva Bhat | Dvita Bhattacharya | Isabelle Chan | Aditi Kiran | Miriam Rafailovich
Source: MRS Communications (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Investigating the impact of bidispersity on spin-coated polystyrene thin films using machine learning Thin film ; Polystyrene ; Pattern recognition (psychology) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties