jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bingrong Huang | Yifan Wu | Shihang Wang | Shengtao Li | Yuanwei Zhu | Weiwang Wang
Source: High Voltage (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Effect of Aggregation Structure on DC Electrical Performance of Thermo‐Oxidatively Aged Crosslinked Polyethylene Insulation ABSTRACT Thermal ageing is an important factor leading to the deterioration of crosslinked polyethylene (XLPE) cable insulation. In this paper, XLPE insulating material was prepared in the laboratory and treated by thermal ageing at 130°C for durations of up to 40 days. Space charge and DC electrical breakdown characteristics were investigated to evaluate the DC electrical performance of differently aged XLPE. Furthermore, to gain deeper insight into the underlying mechanisms, the charge trap characteristics along with the chemical and crystalline structures were examined. The results show that, during the effective protection period of the antioxidant, the XLPE samples exhibit enhanced DC electrical performance, specifically higher DC breakdown strength and reduced space charge accumulation. This improvement is attributed to the evolution of the microstructure. In the initial ageing stage, the degree of crystallisation increases, whereas physicochemical defects are reduced, accompanied by a more concentrated trap energy distribution. As a result, charge injection and migration are effectively inhibited. In the later stage of ageing, the antioxidant efficacy weakens and molecular chain scission occurs, resulting in the disappearance of spherulitic structure and deterioration of electrical properties. These findings are essential for the understanding of the XLPE cable service performance and the enhancement of the XLPE DC electrical performance. Polyethylene ; Space charge ; Service life ; Electrical breakdown ; Crystallization ; Activation energy ; Ageing ; Electrical treeing High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Jacob Ede Levine | Yun Lyan Luo | Sai Kosaraju
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

TSSR: Two-Stage Swap-Reward-Driven Reinforcement Learning for Character-Level SMILES Generation The design of reliable, valid, and diverse molecules is fundamental to modern drug discovery, as improved molecular generation supports efficient exploration of the chemical space for potential drug candidates and reduces the cost of early design efforts. Despite these needs, current chemical language models that generate molecules as SMILES strings are vulnerable to compounding token errors: many samples are unparseable or chemically implausible, and hard constraints meant to prevent failure can restrict exploration. To address this gap, we introduce TSSR, a Two-Stage, Swap-Reward-driven reinforcement learning (RL) framework for character-level SMILES generation. Stage one rewards local token swaps that repair syntax, promoting transitions from invalid to parseable strings. Stage two provides chemistry-aware feedback from RDKit diagnostics, rewarding reductions in valence, aromaticity, and connectivity issues. The reward decomposes into interpretable terms (swap efficiency, error reduction, distance to validity), is model agnostic, and requires no task-specific labels or hand-crafted grammars. We evaluated TSSR on the MOSES benchmark using a GRU policy trained with PPO in both pure RL (P-RL) from random initialization and fine-tuning RL (F-RL) starting from a pretrained chemical language model, assessing 10,000 generated SMILES per run. In P-RL, TSSR significantly improves syntactic validity, chemical validity, and novelty. In F-RL, TSSR preserves drug-likeness and synthesizability while increasing validity and novelty. Token-level analysis shows that syntax edits and chemistry fixes act jointly to reduce RDKit detected errors. TSSR converts a sparse terminal objective into a denser and more interpretable reward, improving both syntactic and chemical quality without reducing diversity. TSSR is dataset-agnostic and can be adapted to various reinforcement learning approaches. Security token ; Initialization ; Chemical space ; Reinforcement learning ; Syntax ; Benchmark (surveying) ; Quality (philosophy) ; Reinforcement Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Gabriele Mezzadra | Xiangwei Guo | Marco Ravalli | Clemens Ritter | Davide Ceresoli | Oliver Clemens | Cristina Tealdi
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

The role of oxygen excess on fluoride intercalation in Ruddlesden–Popper electrodes for fluoride ion batteries: the case of LaSrMnO <sub>4</sub> LaSrMnO 4 is an intercalation material for fluoride ion batteries. Oxygen over-stoichiometry in the sample affects fluoride intercalation mechanisms and energetics, unveiling new migration pathways that hinder fluoride mobility. Fluoride ; Intercalation (chemistry) ; Ion ; Oxygen ; Electrode Inorganic Fluorides and Related Compounds ; Luminescence Properties of Advanced Materials ; Crystal Structures and Properties
Authors: Raghvendra Prasad Dwivedi | Vikrant Jain
Source: International Journal of Chemical Studies (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Quinoline as a privileged scaffold in modern drug discovery: Structural versatility, molecular targeting, and multidomain therapeutic potential Quinoline has emerged as one of the most powerful and adaptable heterocyclic scaffolds in modern drug discovery, offering exceptional chemical flexibility and pharmacological breadth. Its rigid bicyclic framework, combined with multiple sites amenable to substitution, enables precise tuning of physicochemical and biological properties. Advances in structure-based design, hybrid molecule engineering, stereochemical modification, and computational chemistry have collectively expanded the therapeutic reach of quinoline derivatives. These compounds display high affinity and selectivity toward a wide range of clinically significant targets, including kinases, microbial enzymes, ion channels, and neurodegenerative pathways. Recent developments also highlight quinoline’s capacity to integrate multifunctional activities such as combining antiproliferative effects with radiosensitization or pairing neuroprotective and antioxidant properties within a single molecule. The scaffold’s compatibility with both empirical medicinal chemistry and AI-driven virtual screening further accelerates the discovery of optimized analogs with favorable pharmacokinetics and target engagement profiles. Overall, quinoline continues to serve as a privileged and indispensable scaffold for the development of next-generation therapeutics across oncology, infectious disease, inflammation, and central nervous system disorders. Quinoline ; Drug discovery ; Drug ; Scaffold ; Small molecule ; Rational design ; Virtual screening Synthesis and biological activity ; Protein Degradation and Inhibitors ; Synthesis of heterocyclic compounds
Authors: Yujia Guo | Tat Hong Duong Le | Samuel Genheden | Marco V. Mijangos | Varvara Voinarvoska | Giulia Bergonzini | Ola Engkvist | Mikhail Kabeshov | Samuel Kaski
Source: ChemRxiv (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

An Expert-Augmented Deep Learning Approach for Interpretable Synthesis Route Evaluation Selecting efficient multi-step synthetic routes is a central challenge in organic synthesis, particularly in medicinal and process chemistry, where route choice directly impacts feasibility, cost, and development efficiency. While computer-aided synthesis planning (CASP) tools can generate diverse pathways, the evaluation of these generated routes remains challenging. Existing heuristic or data-driven scoring systems often oversimplify the multi-objective nature of synthesis design and rely on proxy datasets (e.g., patent routes) rather than universal grounded criteria. To address this, we introduce an expert-augmented, data-driven scoring framework that integrates machine learning with chemists’ domain knowledge for both numerical and explainable route assessment. A DeepSets-based model is trained using tree edit distance between reference and machine-generated routes, then fine-tuned with expert evaluations to produce both quantitative scores and interpretable qualitative categories (Good, Plausible, Bad). Validated against expert assessments, the resulting system achieves a Spearman correlation coefficient of 0.78 ± 0.05 and a Pearson correlation of 0.77 ± 0.06 regarding its category assessment prediction. When ranking routes by predicted score, the model achieves an overall 60.2% top-1 accuracy (vs. a 17.5% baseline) relative to reference routes, while expert validation confirms that 75% of sampled top-1 selections are feasible, demonstrating the model correctly identifies diverse, expert-validated alternatives beyond the reference. Ranking (information retrieval) ; Deep learning ; Domain (mathematical analysis) ; Expert system ; Process (computing) ; Heuristic ; Domain knowledge ; Heuristics Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Hui Liu | Jie Li | Xinfang Zhang | Qing Wang | Yuekang Zhu | Xiang Hua An | Yueyang Zhang
Source: Research Square (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

The influence of Co doping on the structure and magnetic properties of Ni0.5Zn0.5-xCoxFe2O4 ferrites Coercivity ; Ferrite (magnet) ; Doping ; Analytical Chemistry (journal) ; Magnetometer ; Spinel ; Magnetization ; Lattice constant Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Metallic Glasses and Amorphous Alloys
Authors: George Fratian | Maya Ramesh | Xinyan Li | Evangelos Golias | Yousra Nahas | Sebastian Maria Ulrich Schultheis | Julian Skolaut | Martí Checa | Arundhati Ghosal | Jan Priessnitz | F. C. Fobasso Mbognou | Shashank Kumar Ojha | Shiyu Zhou | Alexander Qualls | Kai Litzius | Christoph Klewe | Peter Meisenheimer | Laurent Bellaiche | Libor Šmejkal | Darrell G. Schlom | Han, Yimo | Sergei Prokhorenko | Ramamoorthy Ramesh | Paul Stevenson | Angela Wittmann | Lucas Caretta
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Topological textures and emergent altermagnetic signatures in ultrathin BiFeO3 Magnetoelectric multiferroics, materials with intrinsically coupled electric polarization and magnetic order, promise ultralow-power switching, nonvolatile memory, and energy-efficient signal transduction. Yet practical deployment demands ultrathin films down to the atomic limit, where both orders typically degrade. Maintaining both order parameters at the thinnest scales in complex oxides remains a tremendous challenge, as uncompensated bound charge drives nanoscale depolarization in most ferroelectrics, while off-stoichiometry, reduced anisotropy, and charge transfer can produce magnetic dead layers in ultrathin oxides at substrate interfaces. Here, we realize a multiferroic phase of BiFeO3 that not only sustains both order parameters at room temperature with no dead layer but also exhibits signatures of emergent altermagnetism in the four-unit-cell, ultrathin limit. First-principles calculations, spin symmetry analysis, atomic-resolution imaging, and angle-resolved magnetic imaging reveal that short-circuit electrostatic boundary conditions, together with epitaxial strain, drive a continuous second-order, thickness-driven phase transition that enables the formation of multiferroic topological textures. Moreover, the imposed boundary conditions stabilize a d-wave altermagnetic time-reversal symmetry breaking, with corresponding signatures observed in magnetic circular dichroism. Collectively, these results establish a pathway to stabilize unconventional multiferroicity at device-relevant thicknesses, reframing scaling limits for oxide electronics. Multiferroics ; Phase boundary ; Nanoscopic scale ; Nanoelectronics ; Topology (electrical circuits) ; Polarization (electrochemistry) ; Scaling ; Phase transition ; Symmetry (geometry) ; Substrate (aquarium) Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Ahmet Arıhan Erözden | Nalan Tavşanlı | Gamze Demirel | Nazmiye Ozlem Sanli | Mahmut Çalışkan | Muzaffer Arıkan
Source: PeerJ (article, 2026-03-27)
Field: Chemistry — Spectroscopy

MetaPepticon: automated prediction of anticancer peptides from microbial genomes and metagenomes MetaPepticon enables scalable and reproducible ACP prediction from raw sequences through integration of multiple predictors within a configurable agreement framework. Applied to 41,171 microbial genomes and 4,072,884 peptides, MetaPepticon identified 10,725 moderate-agreement ACP candidates, including 4,590 novel, non-toxic peptides. MetaPepticon expands the practical applicability of existing ACP prediction methods to high-throughput sequencing data and is freely available at: https://github.com/arikanlab/MetaPepticon. Pipeline (software) ; Genome ; DNA sequencing ; Contig ; Scalability ; Metagenomics ; Deep sequencing Advanced Proteomics Techniques and Applications ; Antimicrobial Peptides and Activities ; Machine Learning in Bioinformatics
Authors: Linxiao Li | Zhixiang Lu
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents As the Web transitions from static retrieval to generative interaction, the escalating environmental footprint of Large Language Models (LLMs) presents a critical sustainability challenge. Current paradigms indiscriminately apply computation-intensive strategies like Chain-of-Thought (CoT) to billions of daily queries, causing LLM overthinking, a redundancy that amplifies carbon emissions and operational barriers. This inefficiency directly undermines UN Sustainable Development Goals 13 (Climate Action) and 10 (Reduced Inequalities) by hindering equitable AI access in resource-constrained regions. To address this, we introduce EcoThink, an energy-aware adaptive inference framework designed to reconcile high-performance AI intelligence with environmental responsibility. EcoThink employs a lightweight, distillation-based router to dynamically assess query complexity, skipping unnecessary reasoning for factoid retrieval while reserving deep computation for complex logic. Extensive evaluations across 9 diverse benchmarks demonstrate that EcoThink reduces inference energy by 40.4% on average (up to 81.9% for web knowledge retrieval) without statistically significant performance loss. By mitigating algorithmic waste, EcoThink offers a scalable path toward a sustainable, inclusive, and energy-efficient generative AI Agent. Inference ; Scalability ; Generative grammar ; Inefficiency ; Semantic Web ; RDF ; Sustainability ; Causal inference Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Big Data and Digital Economy
Authors: Gabriele Mezzadra | Xiangwei Guo | Marco Ravalli | Clemens Ritter | Davide Ceresoli | Oliver Clemens | Cristina Tealdi
Source: ChemRxiv (article, 2026-01-16)
Field: Chemistry — Inorganic Chemistry

The Role of Oxygen Excess on Fluoride Intercalation in Ruddlesden–Popper Electrodes for Fluoride Ion Batteries: The Case of LaSrMnO4 Ruddlesden–Popper–type compounds are particularly attractive electrode materials for fluoride-ion batteries. Among them, LaSrMnO4 has received significant attention due to its high fluoride incorporation capability and lower environmental impact compared to nickel- and cobalt-based analogues. In this work, neutron diffraction data are used to provide an experimental visualization of fluoride-ion diffusion in this class of materials, through Maximum Entropy Method (MEM) and Bond Valence Site Energy (BVSE) analysis. Additionally, since oxygen excess is well known in Ruddlesden–Popper oxides but its impact on fluoride-ion transport has not been previously investigated, molecular dynamics (MD) simulations were employed to reveal how oxygen over-stoichiometry affects fluoride intercalation mechanisms and energetics, unveiling new migration pathways that hinder fluoride mobility. These findings have direct implications for fluoride-ion battery performance, highlighting the critical role of oxygen content in determining anion transport and the electrochemical performance of this class of materials. Fluoride ; Intercalation (chemistry) ; Electrochemistry ; Valence (chemistry) ; Oxygen ; Electrode ; Ion Inorganic Fluorides and Related Compounds ; Fluoride Effects and Removal ; Fiber-reinforced polymer composites
Authors: Daniel Simões Lopes | Eduardo J. Solteiro Pires | Vítor Manuel de Jesus Filipe | Manuel F. Silva | Luís F. Rocha
Source: Technologies (article, 2026-03-27)
Field: Materials Science — Polymers and Plastics

Intelligent and Automated Technologies for Textile Recycling Pre-Processing: A Systematic Literature Review Textile-to-textile recycling is strongly constrained by upstream pre-processing, where post-consumer clothing must be identified, separated, and prepared under high variability in materials, appearance, and contamination. This paper presents a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic literature review of intelligent and automated technologies for textile recycling pre-processing covering the interval between 2015 to 2025. After screening and quality assessment, 21 primary studies published between 2020 and 2025 were included. The literature is synthesized across three task families: (i) identificationof fiber/material, composition, or color; (ii) sorting, considered only when explicit separation strategies are defined to operationalize identification outcomes into routing actions or output streams; and (iii) contaminant detection and/or removal, targeting non-recyclable items. Results show that identification dominates the field (19/21 studies), supported by Red–Green–Blue (RGB) and red–green–blue plus depth (RGB-D) imaging and material-signature sensing, including near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and Raman spectroscopy. In contrast, sorting as a defined separation stage is less frequent (4/21), and contaminant-related automation remains sparse (3/21). Most studies are validated in laboratory conditions, with limited semi-industrial evidence, highlighting a persistent perception-to-action gap. Overall, the review indicates that robust separation strategies, representative datasets, and end-to-end system integration remain key bottlenecks for scalable automated textile recycling pre-processing. Automation ; Identification (biology) ; Systematic review ; Operationalization ; Sorting ; Task (project management) ; Textile ; Field (mathematics) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Recycling and Waste Management Techniques
Authors: Guillaume Godin
Source: arXiv (Cornell University) (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

SCORE: Replacing Layer Stacking with Contractive Recurrent Depth Residual connections are central to modern deep neural networks, enabling stable optimization and efficient information flow across depth. In this work, we propose SCORE (Skip-Connection ODE Recurrent Embedding), a discrete recurrent alternative to classical layer stacking. Instead of composing multiple independent layers, SCORE iteratively applies a single shared neural block using an ODE (Ordinary Differential Equation)-inspired contractive update: ht+1 = (1 - dt) * ht + dt * F(ht) This formulation can be interpreted as a depth-by-iteration refinement process, where the step size dt explicitly controls stability and update magnitude. Unlike continuous Neural ODE approaches, SCORE uses a fixed number of discrete iterations and standard backpropagation without requiring ODE solvers or adjoint methods. We evaluate SCORE across graph neural networks (ESOL molecular solubility), multilayer perceptrons, and Transformer-based language models (nanoGPT). Across architectures, SCORE generally improves convergence speed and often accelerates training. SCORE is reducing parameter count through shared weights. In practice, simple Euler integration provides the best trade-off between computational cost and performance, while higher-order integrators yield marginal gains at increased compute. These results suggest that controlled recurrent depth with contractive residual updates offers a lightweight and effective alternative to classical stacking in deep neural networks. Ode ; Residual ; Artificial neural network ; Block (permutation group theory) ; Convergence (economics) ; Recurrent neural network ; Ordinary differential equation ; Backpropagation ; Stability (learning theory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Mujia Li | Murat Kücükdisli | Florian Braun | David W. Will | Nadine Meier | Bettina Wehrle | Larissa Chiara Meyer | Melanie Christine Föll | Oliver Schilling
Source: Rapid Communications in Mass Spectrometry (article, 2026-01-20)
Field: Chemistry — Spectroscopy

Pyrene‐Conjugated, 2‐Pyridinecarboxaldehyde Derivatives as N‐Terminus‐Specific Tags for MALDI‐ and LALDI‐MS This study presents the first presentation and characterization of this novel pyr-2PCA tag, thereby laying the groundwork and demonstrating its future potential for MALDI/LALDI-based in situ spatial N-terminomics to study proteolytic processes. Characterization (materials science) ; In situ ; Proteolytic enzymes ; Presentation (obstetrics) Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Forensic Fingerprint Detection Methods
Authors: J. Plášil | R. Škoda | A.R. Kampf | I. Němec | N. Meisser | G. Favreau
Source: Journal of Geosciences (article, 2026-02-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Crystal structure of synthetic “magnesiorietveldite”, Raman spectroscopy data and bond-valence approach to the svornostite group of minerals The new synthetic counterpart of a potentially new member of the svornostite group of minerals, "magnesiorietveldite", ideally Mg(UO 2 )(SO 4 ) 2 (H 2 O) 5 , is orthorhombic, space group Pmn2 1 , a = 12.7950( 9), b = 8.3288(4), c = 11.2962(4), V = 1203.80(11) 3 and Z = 2.The crystal structure (R 1 = 0.0168 for 3113 I > 3I reflections) contains uranyl-sulfate chains that are linked into sheets by M1O 2 (H 2 O) 4 and M2O 2 (H 2 O) 4 octahedra, where Mg is a dominant cation over Zn at both sites.The structure is well known and has already been described for other members of the svornostite group; the fundamental building unit -an infinite [(UO 2 )(SO 4 ) 2 (H 2 O)] 2-chain -is common in several uranyl minerals and synthetic compounds.Synthetic "magnesiorietveldite" was obtained after treatment of a specimen containing pitchblende with sulfuric acid.Electron microprobe analyses yielded the empirical formula (Mg 0.63 Zn 0.22 Cu 0.09 Fe 0.05 ) 0.99 (UO 2 )(SO 4 ) 2 5H 2 O, confirming the predominance of Mg over the other divalent cations.Infrared and Raman spectroscopy confirmed the presence of structurally non-equivalent molecular H 2 O and sulfate tetrahedra.We append an extensive discussion on the role of molecular H 2 O in the svornostite-group of minerals based on the bond-valence approach. Raman spectroscopy ; Crystal structure ; Group (periodic table) ; Mineral ; Spectroscopy ; Crystal (programming language) Crystal Structures and Properties ; Nuclear materials and radiation effects ; High-pressure geophysics and materials