jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoyang Deng | Zihao Wan | Huan Xin | Xiaoguang Wang | Wenjuan Wang | Feixiong Chen | Liying Ma | Naiqin Zhao
Source: Advanced Science (article, 2026-01-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Vapor‐Assisted Catalysis Enables Precise Construction of MOF‐Derived Hierarchical Carbon Nanoarrays for High‐Performance Supercapacitors The synthesis of MOF-derived carbons with high surface area and conductivity is challenged by a fundamental trade-off between architectural preservation and catalytic graphitization. Here, we introduce a spatially decoupled, vapor-assisted pyrolysis strategy where a Co-MOF precursor array is physically separated from a vapor-generating Zn-MOF auxiliary. During pyrolysis, a remote Zn vapor flux simultaneously preserves the precursor's nanosheet morphology by suppressing Co nanoparticle aggregation and catalytically grows dense carbon nanotube (CNT) arrays. This process yields an integrated 0D-1D-2D hierarchical carbon architecture with high surface area and graphitization. As a self-supporting supercapacitor electrode, the optimized material delivers a specific capacitance of 360 F g<sup>-1</sup>, excellent rate capability (53% retention at 50 A g<sup>-1</sup>), and robust cycling stability. Mechanistic studies and density functional theory calculations confirm the pivotal role of Zn vapor in modulating Co catalysis for morphology control and reveal a synergy between heteroatoms and cobalt that optimizes K<sup>+</sup> storage kinetics. This remote-regulation strategy establishes a generalizable platform for designing hierarchical carbon materials for advanced energy storage. Supercapacitor ; Heteroatom ; Nanosheet ; Catalysis ; Carbon fibers ; Capacitance ; Specific surface area ; Pyrolysis ; Carbon nanotube Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Electrocatalysts for Energy Conversion
Authors: Saeedeh Karimlar | Abdollah Ghasemi Pirbalouti | Zahra Teymuori | Maryam Moslehishad | Zohreh Hamidi‐Esfahani
Source: Food Science & Nutrition (article, 2026-01-01)
Field: Materials Science — Biomaterials

Applications of Chitosan, an Eco‐Friendly Biopolymer in Agricultural Systems, Herbal Products, and Functional Foods: A Review Chitin, the natural biopolymer of the world next to cellulose, is a modified biodegradable polysaccharide. Chitosan, as the major derivative of chitin, is the product of N-deacetylated chitin. Chitosan is an important biopolymer in nature and the only positively charged (cationic) polysaccharide. Chitosan has many utilizations in sustainable agriculture and food systems, in particular, improving plant resistance to environmental stresses like water deficit, salt, high temperature, cold, heavy metal, etc., as well as biotic stresses such as pest and plant pathogens. In addition, this natural biopolymer is used in different industries such as paper, food (processing, packaging, and preservation), pharmaceuticals, biodiesel, and other uses like wastewater treatment and environmental protection. Chitosan gained significant interest for its safety, antifungal, antibacterial, biodegradability, biocompatibility, and antioxidant activities due to its rich amino and hydroxyl groups. The commercial value of chitosan is due to the valuable properties of its soluble derivatives, which are suitable in food processing, cosmetics, nano and biotechnology, environmental, and textile production. In this review, we will consider the effectiveness of chitosan in the performance of agriculture, herbal products, nutraceuticals, and food systems, like improving biologically active compounds in herbal plants as elicitor; the characteristics of chitosan and chitosan-based biopolymers have been mentioned. Biopolymer ; Chitosan ; Food industry ; Agriculture ; Antioxidant ; Food additive Nanocomposite Films for Food Packaging ; Hibiscus Plant Research Studies ; Advanced Cellulose Research Studies
Authors: Siqi Dong | B. Zhang | Mohammad Wahiduzzaman | Chuting Yang | Qiang Liu | Guillaume Maurin | Shunshun Xiong | Sujing Wang | Xiaolin Wang
Source: Nature Communications (article, 2026-01-26)
Field: Chemistry — Inorganic Chemistry

Krypton/Xenon separation at room temperature in a flexible coordinative framework sorbent The separation of xenon from krypton holds significant importance across various fields, including high-tech industries, national defence, and aerospace. Adsorptive separation via porous solids is considered one of the most promising alternatives to conventional energy-intensive cryogenic processes. While traditional adsorbents selectively capture xenon over krypton under ambient conditions, their desorption energy penalties for producing pure xenon impede practical implementation. Here, we present a metal‒organic framework characterized by synergistic structural and local flexibility that reverses conventional selectivity, thereby enabling preferential adsorption of krypton at room temperature. This material demonstrates a krypton uptake of 36.8 cm<sup>3 </sup>cm<sup>-3</sup> (298 K, 1 bar) and achieves a Kr/Xe selectivity of 10.4 in 1/99 Kr/Xe mixture breakthrough experiments. Moreover, this material exhibits commendable radioactive stability and effectively captures trace amounts of krypton (40 ppm). Mechanistic studies indicate that dynamic adjustments in cavity windows and localized ligand vibrations work synergistically to exploit subtle size differences between krypton and xenon. This enables kinetically controlled sieving of krypton through transiently expanded channels. With an energy-efficient approach to krypton-centric separation, our research redefines the design paradigm for noble gas purification and offer new possibilities for separating other dynamically matched molecules through adaptive host‒guest interactions. Krypton ; Xenon ; Adsorption ; Sorbent ; Noble gas ; Desorption ; Work (physics) Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Inorganic Fluorides and Related Compounds
Authors: Erick H. Ochoa-Chaparro | Luis U. Castruita‐Esparza | Esteban Sánchez
Source: Plants (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Eco-Physiological and Molecular Roles of Zinc Oxide Nanoparticles (ZnO-NPs) in Mitigating Abiotic Stress: A Comprehensive Review Mitigation of abiotic stress of crops is currently one of the primary issues for modern agriculture to secure food supply. On that point, it is acknowledged that climate change is leading to an increase in temperature and solar radiation, while also contributing to prolonged drought events. In contrast, saline soil and heavy metal pollution have been globally problematic, affecting a large part of crops. In this review, we have provided an overview of the eco-physiological and molecular aspects of zinc oxide nanoparticles (ZnO-NPs) as a novel technology for alleviating abiotic stress in plants. It is reported that the presence of ZnO-NPs has positive benefits in physiological processes, such as photosynthetic efficiency, osmotic regulation, ionic homeostasis, and the activation of antioxidant defense systems through gene modifications and the regulation of genes that are regulated under stress conditions. These are positive results for yields, nutrition, and resistance levels in cereals, legumes, and horticultural crops. Furthermore, essential details are reported, suggesting that the addition of ZnO-NPs to crops may be involved in regulating plant metabolism. Nonetheless, we recognize that this technology poses significant challenges for validation on a large scale, particularly in uncontrolled environments. Abiotic component ; Abiotic stress ; Zinc ; Photosynthesis ; Agriculture ; Environmental pollution ; Osmotic shock Nanoparticles: synthesis and applications ; Silicon Effects in Agriculture ; Plant Stress Responses and Tolerance
Authors: Jiayu Jia | Q. N. Xu | Lingling Yang | Bin Cai | Yongzhi Cheng | Hui Luo | Xiangcheng Li
Source: Advanced Photonics Research (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrabroad and Wide‐Angle High‐Temperature‐Stability Infrared Metamaterial Absorber Based on All‐Dielectric Silicon Nitride (Si <sub>3</sub> N <sub>4</sub> ) Ultrabroadband absorption across the long‐wave and ultralong‐wave infrared (LWIR/ULWIR) spectrum is essential for applications ranging from thermal imaging to spectroscopy, yet remains a persistent challenge. Herein, a refractory metamaterial absorber (MMA) composed of four vertically tapered silicon nitride (Si 3 N 4 ) rods on a tungsten substrate is proposed. Finite element method (FEM) simulations show that the MMA achieves an absorbance of over 90% from 7.15 to 31.62 μm, with a relative bandwidth of 126.23%. Notably, the absorbance peak of the MMA is up to 99.97%, 97.92%, 98.42%, and 99.96% at 7.653, 9.045, 16.762, and 26.731 μm, respectively. An equivalent circuit model corroborates these results and elucidates the underlying impedance matching. The ultra‐broadband performance arises from synergistic coupling of multiple resonant modes, including waveguide resonance (WGR) with higher‐order, Fabry–Pérot (F–P) cavity resonance (FPCR), local surface/propagating plasmon resonance (LSPR/PPR), as well as intrinsic losses of Si 3 N 4 material. The MMA also exhibits wide‐angle stability, maintaining high efficiency for incidence up to 50° under both TE and TM polarizations. It's simple, tunable geometry and fully dielectric composition facilitate fabrication and ensure high‐temperature resilience. This design offers a robust platform for advanced infrared systems, including thermal energy harvesting and spectral imaging. Silicon nitride ; Metamaterial ; Silicon ; Infrared ; Resonance (particle physics) ; Absorbance ; Dielectric ; Metamaterial absorber ; Fabrication ; Guided-mode resonance Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Quintina L. Campbell | Sam Cox | Jorge Medina | Brittany Watterson | Andrew White
Source: Machine Learning Science and Technology (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

MDCrow: automating molecular dynamics workflows with large language models Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLMs) have demonstrated success in automating complex scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an agentic LLM assistant capable of automating MD workflows for proteins. MDCrow uses chain-of-thought over 40 expert-designed tools for handling and processing files, setting up simulations, analyzing the simulation outputs, and retrieving relevant information from literature and databases. We assess MDCrow's performance across 25 common tasks of varying complexity, and we evaluate the agent's robustness to difficulty and prompt style. gpt-4o is able to complete increasingly complex tasks with low variance, followed closely by llama3-405b, a compelling open-source model. While prompt style does not influence the best models' performance, it has significant effects on smaller models. Workflow ; Robustness (evolution) ; Automation ; Language model ; Dynamics (music) ; Modeling language Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Guoao Li | Haobo Ling | Chaoxu Su | Zhengxuan Liu | G. H. Wang | Manyi Yang | Shuhua Li
Source: ChemRxiv (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

A data-efficient reactive machine learning potential to accelerate automated exploration of complex reaction networks Reactive machine learning potentials (MLPs) significantly benefits high-throughput exploration of complex reaction networks. However, the development of such MLPs is currently constrained by the scarcity of datasets generated through efficient sampling methods that adequately capture elusive non-equilibrium configurations. To address this, we introduce an integrated molecular dynamics/coordinate driving-active learning (MD/CD-AL) framework that strategically samples reactive configurations, yielding a compact but comprehensive dataset, MDCD20, with ~1.4 million neutral and radical H/C/N/O structures. The MDCD-NN MLP trained on MDCD20 surpasses existing available MLPs trained on much larger datasets in reconstructing 181 elementary reactions with widespread types. It also accelerates automatic reaction network exploration by 10^4-fold in real-world systems relative to its reference quantum chemistry (QM) calculations, tackling complex scenarios such as multiple reaction centers, enantioselectivity and dynamic free-energy landscapes that are beyond the reach of traditional QM methods. This work establishes an efficient paradigm for constructing reactive datasets, enabling the training of reliable MLPs for complex reactive systems at an affordable cost. Complex system ; Scarcity ; Work (physics) ; Training set ; Complex network Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts
Authors: Rongsheng Chen | Feilong Yu | Yì Wáng | Jianzhong Chen | Rong Jin | Jiuxu Wang | Guanhai Li | Xiaoshuang Chen | Wei Lu
Source: Advanced Photonics (article, 2026-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Möbius metasurface for fully decoupled bidirectional light control Simultaneous and independent manipulation of light’s propagation direction, polarization state, and wavelength remains a fundamental challenge in photonics, due to the intrinsic entanglement of optical degrees of freedom imposed by reciprocity and structural symmetry. Here, we present a single-layer dielectric Möbius metasurface that achieves fully decoupled control across these three dimensions by leveraging a topology-inspired polarization-path inversion mechanism. Inspired by Möbius topology, our design maps forward and backward polarization trajectories onto a unified Poincaré sphere via a synthetic phase-driven coordinate transformation, enabling direction-selective phase responses for arbitrary elliptical polarization states. To overcome the trade-offs between dispersion and polarization functionality, we introduce a neural network framework that co-optimizes Jones matrix responses across multiple polarizations, wavelengths, and directions. Experimentally, we demonstrate six completely independent holographic channels—defined by three polarization–wavelength combinations under opposite propagation directions—with high-fidelity image reconstructions and inter-channel crosstalk below 6.4%. In contrast to Janus-type or multilayer metasurfaces limited to interleaved or orthogonal polarization encoding, our approach offers a compact, lossless, and fabrication-compatible solution for multidimensional optical multiplexing. Polarization (electrochemistry) ; Orthogonal polarization spectral imaging ; Holography ; Phasor ; Phase modulation ; Waveplate ; Reciprocity (cultural anthropology) ; Inverse problem Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Plasmonic and Surface Plasmon Research
Authors: Hui Hu | Chaobiao Zhou | Yukang Zhang | Meng-Xia Hu | Juntian Peng | Huawei Tang | Liang Huang | Jianlong Liu | Chunying Guan | Z. Q. Zhu | Xiaoshuang Chen | Wei Lu | xiangping Li | Zi‐Lan Deng | Cheng-Wei Qiu | Guanhai Li | Jinhui Shi
Source: eLight (article, 2026-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Robust chirality via merging accidental BICs with net zero topological charge Abstract Chiral metasurfaces leveraging bound states in the continuum (BICs) offer a powerful route for enhancing light–matter interactions. However, existing quasi-BIC architectures typically face a fundamental trade-off between high quality (Q) factors and wide-angle chiral operation. Most designs confine strong circular dichroism (CD) to isolated points in momentum space (k-space) and often rely on intricate three-dimensional (3D) meta-atoms, rendering them highly sensitive to fabrication imperfections and angular misalignment. Here, we experimentally realize a planar dielectric metasurface that supports a wide k-domain chirality arising from accidental BICs with a net zero-topological-charge (ZTC). By lifting a Dirac-type degeneracy through controlled in-plane and out-of-plane symmetry breaking, we induce a deterministic topological evolution in which the same-handed circularly polarized (C) points migrate toward and accumulate near the Γ point, while the opposite-handed singularities annihilate or shift to higher k-space. This mechanism delivers record-level performance—an ultrahigh Q-factor (~ 10 4 ), near-unity linear and nonlinear CD (0.99/0.999), and robust angular coverage (| k x P /2π, k y P /2π|&lt; 0.06)—all within a fabrication-friendly, single-layer dielectric platform. These results establish a new regime for chiral photonics, unifying high chiral purity, angular robustness, and topological stability in a scalable planar architecture. Topology (electrical circuits) ; Chirality (physics) ; Angular momentum ; Gravitational singularity ; Metamaterial ; Topological quantum number ; Degeneracy (biology) ; Polarization (electrochemistry) ; Isotropy Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Topological Materials and Phenomena
Authors: Jiazhen He | Helen Lai | Lakshidaa Saigiridharan | Gian Marco Ghiandoni | K. Jenei | Umur Gokalp | Ajsa Nukovic | Ola Engkvist | Jon Paul Janet | Samuel Genheden
Source: Drug Discovery Today (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Democratising real-world drug discovery through agentic AI Agentic systems that are based on large language models (LLMs) have emerged as promising tools in the chemistry domain over the past few years. Early examples included work on CoScientist, Chemcrow, and LLM-RDF, which showcased the potential of agentic systems to assist in chemical research, in the orchestration of cheminformatics tools, and in synthetic reaction development. Despite this, the current literature lacks examples of the real-world adoption of such systems in drug discovery. We present such an example by describing our work on an agentic system called ChatInvent, which has been integrated into the discovery pipeline at AstraZeneca to aid in molecular design and synthesis planning. We discuss how the system evolved from a proof-of-concept single agent into an extensible, robust, and scalable multi-agent architecture with a graphical user interface. We emphasize the lessons learnt and the challenges that persist as we continue to work on this project, and share our perspectives on the future of agentic systems in our domain. Drug discovery ; Cheminformatics ; Pipeline (software) ; Domain (mathematical analysis) ; Work (physics) ; Scalability ; Orchestration ; Scope (computer science) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Multi-Agent Systems and Negotiation
Authors: Baohua Zhang | Xin Li | Huangchao Xu | Zhong Jin | Quansheng Wu | Ce Li
Source: Materials Genome Engineering Advances (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

TopoMAS: Large Language Model Driven Topological Materials Multi‐Agent System ABTRACT The discovery of topological materials is severely hampered by fragmented research workflows that cause information loss, inconsistent reasoning, and frequent computational failures. To overcome these barriers, we present TopoMAS, an interactive multi‐agent framework that unifies the entire discovery pipeline through human–AI collaborative intelligence. TopoMAS seamlessly integrates natural language processing, knowledge retrieval from literature and databases, crystal structure generation, and automated first‐principles validation. At its core, is a multi‐level reasoning and coordination mechanism coupled with a self‐refining knowledge graph. This architecture enhances query understanding and ensures computational robustness by adaptively allocating tasks, monitoring execution, and recovering from failures. In collaboration with human experts, TopoMAS has accelerated the identification of candidate topological phases and successfully guided the discovery of new materials. Benchmark evaluations show that TopoMAS's coordinated intelligence enables smaller, more efficient models to rival or even surpass the performance of substantially larger counterparts at a fraction of the computational cost. Ultimately, TopoMAS offers not only a powerful accelerator for materials research but also a transferable blueprint for building next‐generation, AI‐augmented discovery platforms across scientific disciplines. Robustness (evolution) ; Workflow ; Pipeline (software) ; Scientific discovery ; Identification (biology) ; Computational model ; Blueprint ; Boosting (machine learning) ; Benchmark (surveying) Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Catalysis and Oxidation Reactions
Authors: Kritesh Kumar Gupta | Subrata Barman | Anjana Sankar | Sudip Dey | Tanmoy Mukhopadhyay
Source: Archives of Computational Methods in Engineering (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Materials by Design: Critical Review And Perspectives on Materials Informatics to Generative and Agentic Intelligence For millennia, discovering new classes of materials has depended on intuition, experimentation, and often serendipity. However, recent technological advancements have established a more systematic, efficient and robust framework for materials design and discovery. Lately, there has been a surge in studies utilizing artificial intelligence (AI) to identify anomalies, predict stable compounds, material compositions and forecast effective properties. Such computational screening can help avoid unnecessary expenses, save time, and conserve resources during the initial stages of finding optimal solutions. With this motivation, the current review article consolidates research on the broad area of materials informatics, wherein the primary objective is to systematically highlight how the emerging AI frameworks are increasingly being exploited in materials design and discovery. It chronologically discusses online materials databases, data-driven design and optimization, machine learning (ML) and deep learning (DL) frameworks, computer vision-based approaches, natural language processing (NLP) in materials science, and transformer-based foundation models, along with generative and agentic intelligence. This review aims to present cutting-edge AI-driven frameworks in the context of materials science and engineering, providing the research community with critical insights into the progress, caveats and direction of research in this field. Generative grammar ; Context (archaeology) ; Materials informatics ; Foundation (evidence) ; Emerging technologies Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Selection and Properties