jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Jin | Yiliang Lu | Qiang Zhang | Yun Chen | Xiaofang Wang | Bingkun Yang
Source: International Journal of Electrochemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Study on the Corrosion Behavior of Grounding Materials in Acidic Soils With Different Cl <sup>−</sup> Contents The grounding system is a key infrastructure that ensures the stable and safe operation of the power system. The corrosion behavior of earthing materials within soil environments significantly impacts their long‐term reliability. This article studies the corrosion behavior of three typical grounding materials systematically: Cu, carbon steel, and galvanized steel, in acidic soil simulated solutions with varying Cl − concentrations (0.05%, 0.5%, 1%, 2%). The corrosion rate and corrosion resistance of the material were evaluated through electrochemical testing and weight loss method, combined with corrosion morphology observation and product analysis. The results indicate that the impedance arcs and the impedance modulus of the three materials both decrease as the concentration of Cl − increases. The impedance modulus of Cu decreased from 10 5 to 10 3 Ω·cm 2 . The corrosion current density increased significantly from 0.157 to 8.190 μA·cm −2 for Cu and from 9.130 to 19.300 μA·cm −2 for carbon steel, and the corrosion rate rises significantly. Cu exhibits optimal corrosion resistance due to its ability to form a dense corrosion product film. The corrosion rate remained low below 0.008 mm/a across all Cl − concentrations. Galvanized steel has certain protective capabilities when the coating is intact, but its failure accelerates with the increase in the Cl − concentration showing a maximum corrosion rate of 0.0273 mm/a at 1% Cl − and a maximum pitting depth of 6.804 μm at 2% Cl − . The corrosion products of carbon steel are loose and porous, with the worst corrosion resistance. The carbon steel exhibits a maximum pitting depth of 109.56 μm at 2% Cl − . This research can provide a basis for selecting materials for grounding electrodes in acidic environments with high Cl − content. Corrosion ; Galvanization ; Carbon steel ; Coating ; Electrochemistry ; Soil water ; Carbon fibers Corrosion Behavior and Inhibition ; Lightning and Electromagnetic Phenomena ; Concrete Corrosion and Durability
Authors: Helen Qu | Rudy Morel | Michael McCabe | Alberto Bietti | François Lanusse | Shirley Ho | Yann LeCun
Source: arXiv (Cornell University) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Representation Learning for Spatiotemporal Physical Systems Machine learning approaches to spatiotemporal physical systems have primarily focused on next-frame prediction, with the goal of learning an accurate emulator for the system's evolution in time. However, these emulators are computationally expensive to train and are subject to performance pitfalls, such as compounding errors during autoregressive rollout. In this work, we take a different perspective and look at scientific tasks further downstream of predicting the next frame, such as estimation of a system's governing physical parameters. Accuracy on these tasks offers a uniquely quantifiable glimpse into the physical relevance of the representations of these models. We evaluate the effectiveness of general-purpose self-supervised methods in learning physics-grounded representations that are useful for downstream scientific tasks. Surprisingly, we find that not all methods designed for physical modeling outperform generic self-supervised learning methods on these tasks, and methods that learn in the latent space (e.g., joint embedding predictive architectures, or JEPAs) outperform those optimizing pixel-level prediction objectives. Code is available at https://github.com/helenqu/physical-representation-learning. Embedding ; Representation (politics) ; Physical system ; Relevance (law) ; Feature learning ; Perspective (graphical) ; Supervised learning ; Meta learning (computer science) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Wenhan Qian | Ziqu Shang
Source: ChemRxiv (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

SoluScaleNet: A Graph Neural Network with Learnable Solvation Scaling for Generalizable Aqueous Solubility Prediction Predicting aqueous solubility (log S) is critical but challenging due to molecular complexity, temperature variations, and generalization issues with unseen molecules. We introduce SoluScaleNet, a novel Graph Neural Network addressing these challenges by embedding learnable solvation free energy scaling. SoluScaleNet employs two coupled GNNs learning intrinsic solubility from molecular graphs and temperature, and solvation factors from dynamically optimized features. This end-to-end framework autonomously learns solubility components directly from molecular structure and temperature, transcending handcrafted descriptors. Trained on nearly fifty thousand unique samples and rigorously evaluated using a scaffold-based splitting strategy, SoluScaleNet achieved state-of-the-art performance. It demonstrated superior generalization on unseen molecular scaffolds, achieving a Mean Absolute Error of about three-quarters of a log S unit and a Root Mean Squared Error of approximately nine-tenths of a log S unit, significantly outperforming existing models. SoluScaleNet offers a powerful, physically-informed deep learning approach for accurate and generalizable aqueous solubility prediction across diverse chemical spaces and temperatures. Solvation ; Solubility ; Generalization ; Artificial neural network ; Embedding ; Aqueous solution ; Molecular graph ; Scaling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zhendong Guo | Yucong Duan
Source: Electronics (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Bridging Cognitive and Expression Spaces in Creative AI by Integrating DIKWP-TRIZ and Semantic Mathematics Large Language Models (LLMs) generate fluent text but often struggle with reliable multi-step reasoning, factual grounding, and stable use of long context, especially when inputs are incomplete, inconsistent, or imprecise. To address these challenges, we propose a Creative AI framework that integrates DIKWP-TRIZ with a semantic-mathematical constraint layer. DIKWP-TRIZ extends TRIZ by embedding a DIKWP (Data–Information–Knowledge–Wisdom–Purpose) network, enabling purposeful, value-aware transformations and explicit repair operations under 3-No conditions. The semantic layer introduces three context-indexed constraints over concept–expression mappings (Existence, Contextual Uniqueness, and Transitivity), making ambiguities and contradictions explicit and checkable during inference and generation. We enumerate the DIKWP × DIKWP transformation type space (25 ordered pairs over {D,I,K,W,P}) and provide candidate TRIZ inventive principles for each type as design-time guidance. A global Purpose controller steers transformation selection and enforces goal alignment and ethical constraints. We present a reference architecture and qualitative case analyses against a standard LLM, illustrating how the framework structures intermediate steps, surfaces assumptions, and supports traceable explanations. Quantitative benchmarking remains for future work. TRIZ ; Bridging (networking) ; Embedding ; Benchmarking ; Inference ; Transformation (genetics) ; Expression (computer science) ; Constraint (computer-aided design) ; Architecture Machine Learning in Materials Science ; Artificial Intelligence in Games ; Multimodal Machine Learning Applications
Authors: Qiyuan Zhang | Junyi Zhou | Yufei Wang | Fuyuan Lyu | Yidong Ming | Can Xu | Qingfeng Sun | Kai Zheng | Peng Kang | Xue Liu | Chen Ma
Source: ArXiv.org (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

RubricBench: Aligning Model-Generated Rubrics with Human Standards As Large Language Model (LLM) alignment evolves from simple completions to complex, highly sophisticated generation, Reward Models are increasingly shifting toward rubric-guided evaluation to mitigate surface-level biases. However, the community lacks a unified benchmark to assess this evaluation paradigm, as existing benchmarks lack both the discriminative complexity and the ground-truth rubric annotations required for rigorous analysis. To bridge this gap, we introduce RubricBench, a curated benchmark with 1,147 pairwise comparisons specifically designed to assess the reliability of rubric-based evaluation. Our construction employs a multi-dimensional filtration pipeline to target hard samples featuring nuanced input complexity and misleading surface bias, augmenting each with expert-annotated, atomic rubrics derived strictly from instructions. Comprehensive experiments reveal a substantial capability gap between human-annotated and model-generated rubrics, indicating that even state-of-the-art models struggle to autonomously specify valid evaluation criteria, lagging considerably behind human-guided performance. Rubric ; Benchmark (surveying) ; Pipeline (software) ; Discriminative model ; Pairwise comparison ; Reliability (semiconductor) ; Simple (philosophy) ; Bridge (graph theory) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Topic Modeling
Authors: Biao Guo | Xiaonan Mu | Hongmei Zhang | Xingwang Cheng
Source: Materials (article, 2026-03-14)
Field: Materials Science — Ceramics and Composites

Synthesis, Sintering, and Characterization of Composites Composites, renowned for their lightweight nature, high strength, high specific modulus, high temperature resistance, and excellent wear resistance, have become indispensable key materials in fields such as aerospace, rail transportation, defense, and energy [...]. Characterization (materials science) ; Advanced composite materials ; High energy ; Composite number ; Key (lock) ; Impact energy Advanced ceramic materials synthesis ; Tribology and Wear Analysis ; Fiber-reinforced polymer composites
Authors: Lorenzo Guidi | Elisabetta Rosellini | Gaia Riccio | Maria Grazia Cascone
Source: Biomimetics (article, 2026-03-04)
Field: Materials Science — Biomaterials

Advancements and Challenges in Tissue-Engineered Heart Valves: Integrating Biomechanics, Biomaterials, and Biomimetic Design for Functional Maturity Valvular heart disease remains a major global health burden, with currently available prosthetic heart valves failing to fully reproduce the adaptive, regenerative, and long-term functional properties of native valves. Tissue-engineered heart valves (TEHVs) have emerged as a promising alternative, aiming to develop living valve replacements capable of growth, remodeling, and physiological integration. However, despite substantial progress, the clinical translation of TEHVs remains limited, indicating the need for design strategies that go beyond material selection toward functionally mature constructs. This review presents recent advances in TEHV development from a biomimetic perspective, using native heart valves as a biological reference characterized by hierarchical structure, anisotropic mechanical behavior, mechanoresponsive cell populations, immune regulation, and temporally coordinated remodeling. We integrate current understanding of valve biology and mechanobiology with advances in scaffold materials and architecture, bioactive functionalization, biomechanical conditioning, and emerging fabrication and monitoring technologies. We discuss how biomimetic scaffold designs aim to replicate native extracellular matrix organization and nonlinear mechanics, how biological cues are used to regulate thrombosis, immune response, and cell recruitment, and how dynamic bioreactor systems support functional tissue maturation through controlled mechanical stimulation. Finally, key challenges for clinical translation are highlighted, and future directions are outlined, emphasizing integrated and biomimetically informed design approaches. Overall, this review aims to define guiding principles that may accelerate the development of durable, regenerative, and clinically translatable tissue-engineered heart valves. We argue that successful TEHV translation requires synchronized control of scaffold anisotropy, immune modulation, and mechanical conditioning rather than incremental material optimization. Scaffold ; Mechanobiology ; Heart valve ; Biomimetics ; Biomimetic materials ; Biofabrication ; Native tissue Electrospun Nanofibers in Biomedical Applications ; Cardiac Valve Diseases and Treatments ; Tissue Engineering and Regenerative Medicine
Authors: Laio Oriel Seman | William Gouvêa Buratto | Gabriel Villarrubia González | Valderi Reis Quietinho Leithardt | Ademir Nied | Stefano  Frizzo Stefenon
Source: Results in Engineering (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Differentiable neural search architecture with zero-cost metrics for insulator fault prediction Reliable monitoring of high-voltage insulators is critical for maintaining the stability of electrical power systems, particularly under environmental contamination that can lead to flashover. Traditional inspection techniques struggle to anticipate degradation dynamics, while data-driven models often rely on fixed neural architectures that inadequately capture the complex temporal patterns in leakage current signals. This work proposes a Differentiable Neural Architecture Search (DARTS) framework, based on zero-cost metrics, tailored for time series forecasting in insulator monitoring. The method based on DARTS integrates a mixed encoder-decoder design with learnable selection over long short-term memory, gated recurrent units, and transformer components, coupled with a cross-attention bridge featuring temporal bias and gating mechanisms. To ensure efficient architecture exploration, the search leverages metrics such as SynFlow and Jacobian covariance for early candidate screening, followed by a bilevel optimization stage with entropy and diversity regularization. Experiments on real-world leakage current data demonstrate that the discovered architectures outperform manually designed baselines, offering improved forecasting performance. Artificial neural network ; Differentiable function ; Fault (geology) ; Software ; Fault detection and isolation High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Sun-Young Hwang | Yohan Noh | Chanyong Lee | Hangoo Cho | KyungSoo Jeon | Yongil Chung | Jaehyeong Lee
Source: Energies (article, 2026-02-24)
Field: Materials Science — Biomaterials

Structural Integration of CNF Films into Photovoltaic Modules and Comparative Analysis of Output Characteristics As photovoltaic modules advance toward higher efficiency, environmental sustainability and carbon emission reduction in materials have become important issues. In this study, large-area transparent films were fabricated using TEMPO-oxidized cellulose nanofibers (CNFs), and their feasibility as replacements for conventional petroleum-based polymer films in photovoltaic module materials was evaluated. The CNF films were stably fabricated under large-area processing conditions with uniform thickness. The CNF films exhibited high optical transmittance in the visible region comparable to commercial polymer films, sufficient mechanical stiffness, and thermal stability under module lamination conditions. Module-level performance analysis showed that the effect of CNF film application depended on the application position, with different output trends for front and rear configurations. These results demonstrate the potential of large-area CNF films as sustainable photovoltaic module materials and their contribution to carbon emission reduction through the use of renewable bio-based resources. Photovoltaic system ; Lamination ; Transmittance ; Carbon fibers ; Thermal stability ; Carbon nanofiber ; Polymer ; Renewable energy Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Anodic Oxide Films and Nanostructures
Authors: Mihaela Aradoaei | Romeo Cristian Ciobanu | Sebastian Teodor Arădoaei | Rolland Luigi Eva | Alina Ruxandra Caramitu | Adriana Mariana Borș
Source: Materials (article, 2026-04-01)
Field: Materials Science — Polymers and Plastics

Composites from Recycled Polyolefin and Waste Plant Biomass with Potential Uses in Electrical Insulation Applications This research investigates novel polymeric composite materials made from recycled polyolefin and waste plant biomass (poplar seeds and vegetable peels), which have potential applications in the relatively unexplored field of electrical insulation. For composites made from poplar seeds with low density polyethylene matrix, the structure appears more uniform, even with increased biomass content, in contrast to those utilizing high density polyethylene matrix, which displays notable heterogeneous areas where the polymer appears separated from the fibrous network at higher biomass levels. Concerning the composites of vegetable peels with high density polyethylene matrix, the fragments of vegetable peels are clearly recognizable, and their bond to the polymer matrix appears weaker. When incorporating vegetable peels into the polypropylene matrix, it results in a better distribution of the vegetable peel fragments within the polymer matrix, as well as enhanced structural homogeneity. Overall, the incorporation of biomass reduces the Shore hardness measurement for every polymer matrix. Regarding tear resistance, the inclusion of biomass reduces the values only for low density polyethylene with poplar seeds. For both high density polyethylene and polypropylene, regardless of the biomass type, the property seems to enhance marginally with the addition of biomass. The primary advantage of utilizing these composites is that their water absorption rate is at least twice as low as that of transformer board, while still offering a similar capacity for absorbing transformer oil. All composite types exceeded the minimum required threshold of 70 °C for service exposure, and adhered to insulation class A, similar to cellulose-based insulations. The addition of cellulose to polyolefin composites appears to slightly improve their breakdown strength. The conductivity for this type of composite is at least three times lower than that of cellulose insulation materials, rendering them beneficial for applications in electrical engineering as potential substitutes for cellulose-based materials in multiple electrical insulation uses, e.g., for insulating low voltage electrical machines, as well as serving as a substitute for pressboard in transformers. Additionally, their thermoplastic properties offer enhanced processing versatility, opening up new opportunities for electrical engineering technology, especially with regard to electrical insulation recyclability in the context of a circular economy. Polyolefin ; Polyethylene ; Biomass (ecology) ; Cellulose ; Polypropylene ; Composite number ; Polymer ; Low-density polyethylene Natural Fiber Reinforced Composites ; Power Transformer Diagnostics and Insulation ; Polymer Science and PVC
Authors: Tirthankar Bhandary
Source: Journal of Material Sciences & Manfacturing Research (article, 2026-01-28)
Field: Materials Science — Polymers and Plastics

Effect of Vegetable-Derived Linoleic Acid in Passenger Car Radial (PCR) Tyre Tread, Sidewall and Ply Compound Stearic acid, octadecanoic acid, is a primary ingredient used in the rubber industry as an activator for vulcanization. Linoleic acid, derived from vegetables, contains a similar number of carbon atoms and has a similar concentration of acid functional groups (193 mg KOH/g) with a higher unsaturation content, as indicated by its iodine number (134 g/100g) compared to stearic acid (2 g/100g). Gas chromatography mass spectrometry (GC-MS) analysis reveals the location of unsaturation at the 9th and 12th positions. When mixed with tread, sidewall, and ply compounds, linoleic acid exhibits lower activation energy (Ea) and a higher vulcanization reaction rate constant, and a higher order of the reaction compared to the control compounds. These unique properties of linoleic acid result in enhanced modulus at 300% strain with improved tensile strength. Dynamic mechanical analysis shows that the experimental tread compound achieves a higher modulus than the control compound. The fatigue properties of the experimental sidewall demonstrate a slightly longer lifespan (273 kc) compared to the control compound (268 kc), while both exhibit similar ozone protection. For the ply compound, the adhesion between the ply compound and the fabric is stronger in the experimental compound (16.25 Kgf) than in the control (15.51 Kgf). Stearic acid ; Degree of unsaturation ; Vulcanization ; Linoleic acid ; Ultimate tensile strength ; Dynamic mechanical analysis ; Natural rubber Polymer Nanocomposites and Properties ; Polymer Science and PVC ; Polymer composites and self-healing
Authors: Ikechukwu Iloh Udema
Source: International Journal of Advanced Multidisciplinary Research and Studies (article, 2026-01-10)
Field: Chemistry — Physical and Theoretical Chemistry

Biophysical Insights into Coronavirus Cell Membrane Interactions: The Role of Nonequilibrium Binding Energy Scientists are interested in the free energy associated with the binding of viral particles to cell membranes. However, the intrinsic force associated with virus-cell membrane interactions has yet to be investigated. While the kinetic energy may be a state function, it is not the type associated with the thermodynamic equilibrium constant. Thus, the main objective of this study was to derive an equation for non-equilibrium binding energy (NEBE) that depends on an intrinsic force capable of opposing kinetic energy. Some data in the literature were used to evaluate the derived equations. Though diffusivities increased with temperature, they were substantially lower than those far from the binding region for the D and G variants of SARS-CoV-2. With breath emission equal to 1.29 exp. (+7)/m³, the NEBE value for the Omicron variant was higher than that for the Delta variant. A similar trend with lower values was observed with a breath emission equal to 27.9 exp. (+7)/m³; the NEBE value with a breath emission rate of 9.31 exp. (+6)/hour is higher than with 201 exp. (+6)/hour for the Omicron variant only. The G variant of SARS-CoV-2 showed higher NEBEs (952-671 kcal/mol) than the Delta variant (834-588 kcal/mol), corresponding to 293.15-318.15 K. The virus's binding affinity may diminish at higher temperatures due to the decrease in maximal NEBE as temperature increases. Therefore, medication at temperatures higher than the normal body temperature is advised. Future research should be to definitively determine the size and molar mass of SARS-CoV-2 variants. PACS Number: 87.17.Jj; 05.70Ln. Kinetic energy ; Non-equilibrium thermodynamics ; Membrane ; Binding energy ; Kinetics ; Plasma protein binding ; Activation energy ; Cell membrane thermodynamics and calorimetric analyses ; Biofield Effects and Biophysics ; Chemical and Physical Studies