jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R.H. Chen | Qingyuan Zhang | Ji Chen
Source: arXiv (Cornell University) (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Dataset Distillation for Machine Learning Force Field in Phase Transition Regime Machine learning force field (MLFF) has emerged as a powerful data-driven tool for atomistic simulations, enabling large-scale and complex atomic systems to be simulated with accuracy comparable to \textit{ab initio} methods. However, MLFFs often suffer from low training efficiency in the phase transition regime, where structural fluctuations are significantly elevated. To address this challenge, we propose a Central-Peripheral Distillation (CPD) algorithm for training dataset distillation. By strategically integrating representative samples with critical corner cases, the CPD algorithm ensures that the distilled dataset retains maximum structural diversity. We validated the efficacy of the CPD method on the liquid-liquid phase transition of dense hydrogen. Results show that, with the CPD approach, only 200 configurations are sufficient to train a MLFF that can fully reproduce the structural and dynamical properties of liquid hydrogen in the vicinity of its phase transition regime. This work paves the way for high-fidelity labeling of the MLFF training datasets, for instance by adopting high-level \textit{ab initio} calculations beyond the standard density functional theory, thereby enhancing the predictive accuracy of MLFFs. Work (physics) ; Field (mathematics) ; Phase (matter) ; Distillation ; Phase transition ; Force field (fiction) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum, superfluid, helium dynamics
Authors: Elizabeth A. Peterson | Dongye Liu | Sean H. Mills | Tiffany C. Kaspar | Hyosim Kim | Yongqiang Wang | Blas P. Uberuaga | Andrew M. Minor
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Irradiation-induced amplification of electric fields at oxide interfaces as revealed by correlative DPC-STEM and DFT Heterointerfaces are ubiquitous in modern devices, found in technologies ranging from microelectronics to structural components for energy applications. Many of these emerging technologies are found in applications such as satellites, batteries, and next generation nuclear reactors, that are subject to harsh environments. In some scenarios, multiple extreme conditions, such as irradiation and corrosion, act on the material simultaneously. Extending the lifetime of these technologies is dependent on a detailed understanding of how their component materials platforms and interfaces respond in extreme environments, where irradiation and corrosion may couple in unique ways, distinct from corrosion under ambient conditions. Oxides, which form readily over metal underlayers, can act as protective coatings; enhancing the robustness of oxide overlayers to protect underlying metal alloys is a potential avenue towards corrosion mitigation. Here we study the impact of irradiation-induced non-equilibrium defects on charge segregation and electric fields at and near multi-phase oxide heterointerfaces. We perform a detailed study of irradiated Fe2O3-Cr2O3 thin film heterostructures using first-principles DFT electronic structure modeling paired with 4D-STEM DPC and EELS techniques to measure nanoscale changes in electric fields. Our results show clear evidence that irradiation drives substantial modulation of interfacial electric fields that can be tailored by controlling the atomistic chemical structure of the oxide interface. We show that irradiation can selectively induce built-in electric fields, thereby altering their direction; this suggests a pathway to engineering protective oxide heterostructure overlayers that can electrically control the spatial distribution of defects, with significant implications for the design of corrosion-resistant materials for extreme environments. Oxide ; Microelectronics ; Heterojunction ; Electric field ; Irradiation ; Corrosion ; Nanoscopic scale ; Atomic units Fusion materials and technologies ; Ion-surface interactions and analysis ; Iron oxide chemistry and applications
Authors: Hong Sun | Joshua A. Vita | Amit Samanta | Vincenzo Lordi
Source: Machine Learning Science and Technology (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields Abstract Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization. Autoencoder ; Cluster analysis ; Pruning ; Graph ; Outlier ; Representation (politics) ; Pattern recognition (psychology) ; Unsupervised learning ; Feature learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yang Li | Y. Mishin
Source: ArXiv.org (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

The effect of normal stress on stacking fault energy in face-centered cubic metals Plastic deformation and fracture of FCC metals involve the formation of stable or unstable stacking faults (SFs) on (111) plane. Examples include dislocation cross-slip and dislocation nucleation at interfaces and near crack tips. The stress component normal to (111) plane can strongly affect the SF energy when the stress magnitude reaches several to tens of GPa. We conduct a series of DFT calculations of SF energies in six FCC metals: Al, Ni, Cu, Ag, Au, and Pt. The results show that normal compression significantly increases the stable and unstable SF energies in all six metals, while normal tension decreases them. The SF formation is accompanied by inelastic expansion in the normal direction. The DFT calculations are compared with predictions of several representative classical and machine-learning interatomic potentials. Many potentials fail to capture the correct stress effect on the SF energy, often predicting trends opposite to the DFT calculations. Possible ways to improve the ability of potentials to represent the stress effect on SF energy are discussed. Stacking-fault energy ; Nucleation ; Dislocation ; Stress (linguistics) ; Stacking ; Tension (geology) ; Stacking fault ; Deformation (meteorology) ; Compression (physics) Microstructure and mechanical properties ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Manuela Cedrún-Morales | Enrica Soprano | Giulia Vanoni | Anil Chandra | Helena Iuele | Francesco Colella | Stefania Forciniti | Valentina Onesto | Giuliana Grasso | Giuseppe Gigli | Loretta L. del Mercato
Source: Journal of Materials Chemistry C (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advances in fluorescence-based point-of-care diagnostics: probes, nanostructures and integrated systems Fluorescence-based point-of-care (POC) technologies are rapidly evolving through advances in materials chemistry. This review provides a comprehensive overview of fluorescent molecular probes, proteins, hybrid nanostructures, metal-organic frameworks, silica platforms, carbon dots, and upconverting nanoparticles engineered for portable diagnostics. We highlight fundamental photophysical mechanisms, design strategies for improving brightness, stability, and multiplexing, and the integration of fluorescent materials with microfluidics, wearable interfaces, and smartphone readouts. Current limitations, translational challenges, and emerging trends, including AI-enabled data analysis and next-generation NIR and AIE fluorophores, are discussed. Together, these insights outline how material innovation is driving the next generation of high-performance POC sensing systems. Nanostructure ; Carbon fibers ; Fluorescence ; Emerging technologies ; Carbon nanotube Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection
Authors: Léna Triestram | François‐Xavier Coudert
Source: ChemRxiv (article, 2026-01-22)
Field: Chemistry — Inorganic Chemistry

Identifying Phase Transitions in Zeolitic Imidazolate Frameworks: Microscopic Insight from Molecular Simulations Metal–organic frameworks (MOFs) feature a rich structural diversity, including crystalline, amorphous, and liquid phases of varying topologies. Their structural characterization is often performed either at the local scale (through pair distribution functions, bond angle distributions, etc.) or, for crystalline phases, through topology analysis of the periodic framework — leaving out disordered and amorphous phases. In this work, we develop a computational methodology for the structural characterization of middle-range order in MOFs that is applicable to both crystalline and amorphous phases. We base our method on the statistical analysis of the geometry of the supramolecular framework at the microscopic level, and its evolution during molecular simulation. We analyze the statistics of metal–organic rings, their distribution in size, as well as their geometrical characteristics through mathematical tools derived from polymer physics: radius of gyration, asphericity, and writhe. We show that this advanced characterization can be leveraged for the identification of phases and the detection and analysis of phase transitions. Characterization (materials science) ; Amorphous solid ; Zeolitic imidazolate framework ; Molecular dynamics ; Supramolecular chemistry ; Polymer ; Phase (matter) ; Phase transition ; Pair distribution function Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: P. Muthukumaran | M. Kamaraj | Suresh Babu | Jeyaseelan Aravind
Source: Nature Environment and Pollution Technology (article, 2026-02-23)
Field: Materials Science — Biomaterials

Biopolymers Production Strategies and Their Usage as Clean Material for Environmental Remediation The renewable sources, biodegradability, and customizable physicochemical features of biopolymers make them viable alternatives to synthetic materials. Their use in wastewater, air, and soil remediation offers promising answers to pollution problems. This comprehensive analysis encompasses the natural extraction, microbial biosynthesis, and chemical polymerization of biopolymers. Chitosan, alginate, bacterial cellulose, and polyhydroxyalkanoates (PHAs) are excellent biopolymers for wastewater treatment because they effectively adsorb heavy metals, dyes, and organic contaminants. Additionally, biopolymer-based membranes, composites, and hydrogels are garnering attention for air filtration and soil stabilization. Functional modifications have enhanced the efficiency and environmental sustainability of biopolymers through the application of synthetic biology and nanotechnology. This paper explores the potential of biopolymer-based environmental remediation technologies to replace synthetic materials in sustainable pollution management, highlighting recent advances, challenges, and prospects. Environmental remediation ; Sustainability ; Reuse ; Environmental pollution ; Pollution ; Groundwater remediation ; Sewage treatment ; Production (economics) ; Renewable resource Nanocomposite Films for Food Packaging ; Polymer-Based Agricultural Enhancements ; Advanced Cellulose Research Studies
Authors: Ronan Pelé | María González Martínez | Ange Nzihou
Source: Journal of Environmental Management (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

NOx decomposition through seaweed biocarbon as biosourced catalyst: Experimental and density functional theory approaches Understanding the NOx abatement and deactivation mechanisms is essential to improve the performance and regeneration of bio-based catalysts. In this study, biocarbon obtained from marine algae (SW), rich in nitrogen and minerals, was used as a biosourced catalyst for the decomposition of a flux of 1021 ppm of NO in argon (deNOx). Several parameters were studied, including pyrolysis temperature (650 and 800 °C) and deNOx operating temperature (200, 350, and 500 °C). Additionally, a theoretical study using density functional theory (DFT) calculations investigated the role of nitrogen and catalytic (Fe) or promoter (K) elements and functional groups (O-groups) in NO adsorption. The results indicated that the bio-based catalyst produced at 800 °C (SW800) achieved the highest conversion rate of 22.5 ± 1% at 350 °C compared to the catalyst produced at 650 °C (SW650), which achieved a conversion rate of 15.6 ± 1%. This result is attributed to the higher mineral content (52.99 wt%, db) of the SW800 catalyst rather than its specific surface area, which was not identified as a discriminating parameter under the studied conditions. Regarding the reaction mechanism, nitrogen and oxygen atomic balances confirm the multistep decomposition hypothesis. Furthermore, the slight increase in lactone and carbonyl groups after the deNOx process suggests that oxygen remains on the biocarbon. This contributes to reduce active sites and to deactivate the biocarbon catalyst, as observed. Coke deposition on active sites and metal sintering can also contribute to deactivation. Based on the calculations on representative nitrogen-rich biocarbon structures, the adsorption energies (E<sub>ads</sub>) on pyridinic N (-71.10 kJ mol<sup>-1</sup>) and pyrrolic N (-69.30 kJ mol<sup>-1</sup>) sites are high, demonstrating their roles in NO abatement. However, the presence of an oxygen group close to a nitrogen site inhibits NO adsorption due to its high electronegativity. This information is in agreement with the deactivation of nitrogen sites by residual oxygen from NO decomposition, as observed experimentally. NOx ; Decomposition ; Catalysis ; Nitrogen ; Adsorption ; Density functional theory ; Pyrolysis ; Oxygen Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Flame retardant materials and properties
Authors: Kaveh Edalati
Source: Hydrogen (article, 2026-02-04)
Field: Materials Science — Metals and Alloys

Contribution of Severe Plastic Deformation via High-Pressure Torsion to the Hydrogen Cycle: From Hydrogen Production and Storage to Hydrogen Embrittlement Hydrogen is a key energy carrier for achieving carbon neutrality, yet its widespread deployment is hindered by challenges associated with efficient hydrogen production, safe and reversible hydrogen storage, and hydrogen-induced embrittlement. Severe plastic deformation processes, particularly high-pressure torsion (HPT), have emerged as a powerful approach capable of addressing these challenges through extreme grain refinement, defect engineering, phase stabilization far from equilibrium, and synthesis of novel materials. This article reviews the impact of HPT on hydrogen-related materials, covering hydrogen production, hydrogen storage, and hydrogen embrittlement resistance. For hydrogen production, HPT enables the synthesis of nanostructured, defect-rich, and compositionally complex compounds, including high-entropy oxides and oxynitrides, which exhibit enhanced hydrolytic, electrocatalytic, photocatalytic, photoelectrocatalytic, and photoreforming performance. For hydrogen storage, HPT fundamentally modifies hydrogenation activation and kinetics, and modifies thermodynamics by hydrogen binding energy engineering, enabling reversible hydrogen storage at room temperature in systems such as Mg-based and high-entropy alloys. For hydrogen embrittlement resistance, HPT under optimized conditions suppresses hydrogen-assisted fracture by engineering ultrafine grains and defects (vacancies, dislocations, Lomer–Cottrell locks, D-Frank partial dislocations, stacking faults, twins, and grain boundaries) that control hydrogen diffusion, trapping, and strain localization. By integrating insights across these three domains, this article highlights HPT as a transformative strategy for developing next-generation hydrogen materials and identifies key opportunities for future research at the intersection of severe plastic deformation and hydrogen technologies. Hydrogen embrittlement ; Hydrogen ; Hydrogen storage ; Severe plastic deformation ; Hydrogen production ; Embrittlement ; Hydrogen fuel Hydrogen embrittlement and corrosion behaviors in metals ; Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials
Authors: G. Mustafa | Sadia Muzammal | Awais Ahmad | Faisal Ali | Ikram Ahmad
Source: Materials Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Dual-phyto mediated synthesis of magnesium zinc ferrite nanoparticles using <i>Parthenium hysterophorus</i> and <i>Nerium oleander</i> : a novel strategy for eco-friendly wastewater detoxification This study includes the green synthesis of MgZnFe 2 O 4 and MgZnFe 2 O 4 ferrites using P-H and N-O plant extracts as reducing agents. Nanoparticle ; Magnesium ; Wastewater ; Zinc ; Detoxification (alternative medicine) ; Zinc ferrite Magnetic Properties and Synthesis of Ferrites ; Magnesium Oxide Properties and Applications ; Advanced Photocatalysis Techniques
Authors: Mariam T. Akopian | Dmitry A. Medvedev
Source: Chimica Techno Acta (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Heavily doped proton-conducting perovskites: Relationships between defect structure and functional properties Proton-conducting oxides based on perovskite structures are essential materials for intermediate-temperature electrochemical devices. However, classical systems, such as barium cerates and zirconates, are limited by the solubility of acceptor dopants. This limits the achievable concentration of protonic defects and prevents the investigation of the transport mechanisms in the concentrated defect regime. This review systematically examines the current understanding of proton transport in acceptor-doped perovskite oxides. It identifies limitations inherent to conventional materials and explores the BaSn1–xInxO3–δ system. The latter is unique, since it can accommodate high indium concentrations while retaining cubic perovskite symmetry. The exceptional solid solution formation in this system allows for access to doping levels where oxygen vacancy concentrations exceed 0.2 per formula unit. This is a condition where defect-defect interactions are inevitable, and behavior may deviate qualitatively from that described by ideal solution models in the dilute limit. The present review establishes correlations between composition, average crystal structure, local atomic arrangements, defect structure, and various physicochemical and functional properties, such as hydration thermodynamics, proton conductivity, chemical expansion, and thermomechanical behavior. Particular attention is devoted to the heavily doped compositions (x = 0.4–0.7), where pronounced local structural disorder, composition-dependent trapping phenomena, and the interplay between thermal and chemical expansion effects significantly influence macroscopic transport behaviour. The combination of experimental and computational findings establishes a framework for understanding the relationships between structure and properties in highly defective proton-conducting perovskites. Perovskite (structure) ; Doping ; Vacancy defect ; Indium ; Solubility ; Crystallographic defect ; Crystal structure ; Solid solution Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Hirotaka Inoue | Takamasa Onoki | Akira Takakura | Anastasios Karakassides | Huan Jiang | Toshihiko Fujimori | Esko I Kauppinen
Source: ChemRxiv (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Near-Armchair-Enriched Growth of Single-Walled Carbon Nanotubes via Fe–W Bimetallic Catalysts in Mist FC-CVD Chiral-angle-controlled synthesis of carbon nanotubes (CNTs) remains a central challenge, particularly in continuous growth processes such as floating catalyst chemical vapor deposition (FC-CVD), where high productivity is attractive but tight structural control is difficult to achieve. In this study, a mist-based FC-CVD platform is established for continuous CNT growth, enabling systematic chiral-angle control through expanded catalyst-precursor design. By introducing ultrasonic aerosol-mist delivery, volatility constraints in precursor feeding are relaxed, allowing co-delivery of a non-volatile W precursor (ammonium metatungstate) with ferrocene to form an Fe–W bimetallic catalyst under atmospheric-pressure FC-CVD conditions. This catalyst system induces pronounced near-armchair-selective growth, shifting the CNT chiral-angle distribution away from achiral limits while also narrowing the distribution compared with W-free synthesis. Analysis based on a theoretical abundance expression for CNT growth on solid catalysts supports an interface-stabilization mechanism, in which W enhances the structural stability of catalyst nanoparticles and the catalyst–CNT interface, thereby strengthening chiral-angle selectivity. Furthermore, tuning the effective carbon supply reveals an optimal regime for maximizing near-armchair enrichment, whereas supply-limited and overfed conditions broaden the distribution through distinct kinetic pathways. These results provide a practical route toward scalable chiral-angle-controlled CNT synthesis in continuous FC-CVD. Bimetallic strip ; Catalysis ; Carbon nanotube ; Chemical vapor deposition ; Nanoparticle ; Volatility (finance) ; Annealing (glass) Carbon Nanotubes in Composites ; Electrocatalysts for Energy Conversion ; Graphene research and applications
Authors: Poonam Rani
Source: International Journal of Applied and Behavioral Sciences (article, 2026-01-10)
Field: Chemistry — Organic Chemistry

Recent Advances in Synthesis of Thiazoles ring: Mini Review Thiazole-containing compounds are widely found in natural products as well assynthetic sources. Many thiazole-based compounds possess a broad spectrum of bioactivities,and some of them are well-known drugs in the markets. The use of thiazole derivatives inother fields such as organic materials, cosmetics, and organic synthesis has also been widelyreported. Due to a wide range of applicability, the synthesis of thiazole-containingcompounds has attracted extensive interest from chemists, and many studies in the synthesisof thiazole skeleton have been reported recently. Thiazole ; Broad spectrum ; Organic synthesis ; Natural product Synthesis and biological activity ; Synthesis and Reactivity of Sulfur-Containing Compounds ; Synthesis of Tetrazole Derivatives