jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siqi Chen | Zhiqiang Wang | Yili Shen | Xianqi Deng | Xi Cheng | Cheng-Wei Ju | Jun Yi | Guo Ling | Dieaa Alhmoud | Hui Guan | Zhou Lin
Source: ArXiv.org (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system exceeds hundreds of atoms, first-principles quantum mechanical (QM) modeling becomes impractical. In this study, we developed FB-GNN-MBE by integrating a fragment-based graph neural network (FB-GNN) into the many-body expansion (MBE) theory and demonstrated its capacity to reproduce first-principles potential energy surfaces (PES) for hierarchically structured systems with manageable accuracy, complexity, and interpretability. Specifically, we divided the entire system into basic building blocks (fragments), evaluated their one-fragment energies using a QM model, and addressed many-fragment interactions using the structure-property relationships trained by FB-GNNs. Our investigation shows that FB-GNN-MBE achieves chemical accuracy in predicting two-body (2B) and three-body (3B) energies across water, phenol, and mixture benchmarks, as well as the one-dimensional dissociation curves of water and phenol dimers. To transfer the success of FB-GNN-MBE across various systems with minimal computational costs and data demands, we developed and validated a teacher-student learning protocol. A heavy-weight FB-GNN trained on a mixed-density water cluster ensemble (teacher) distills its learned knowledge and passes it to a light-weight GNN (student), which is later fine-tuned on a uniform-density (H2O)21 cluster ensemble. This transfer learning strategy resulted in efficient and accurate prediction of 2B and 3B energies for variously sized water clusters without retraining. Our transferable FB-GNN-MBE framework outperformed conventional non-FB-GNN-based models and showed high practicality for large-scale molecular simulations. Complex system ; Artificial neural network ; Dissociation (chemistry) ; Transfer of learning ; Cluster (spacecraft) ; Quantum chemical ; Energy transfer ; Graph Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Elisa Giovanna Faggioli | Carlo Punta | Davide Ghirardello | Roberto Aguado | Gabriela A. Bastida | Delgado-Aguilar Marc | Alessandro Sacchetti
Source: Carbohydrate Polymers (article, 2026-02-17)
Field: Materials Science — Biomaterials

Production of TEMPO-oxidized cellulose nanofibers: Tuning the physio-chemical properties by controlling the process Cellulose ; Viscosity ; Nanofiber ; Rheology ; Nanomaterials ; Pulp (tooth) ; Suspension (topology) ; Fibril Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Lignin and Wood Chemistry
Authors: Wei Zeng | Qian Li | He Deng | Kang‐Da Zhang | Meimei Zhang | Yongfei Yin | Shigui Chen | Lu Wang
Source: Chinese Chemical Letters (article, 2026-02-01)
Field: Chemistry — Organic Chemistry

Guest-regulable Pt(II)···Pt(II) interactions in hydrogen-bonded aromatic macrocycle based rotaxanes enables tunable NIR emission Supramolecular chemistry ; Luminescence ; Pyridinium ; Intermolecular force ; Supramolecular assembly ; Excited state Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials ; Synthesis and Properties of Aromatic Compounds
Authors: Egor V. Musin | A. A. Dubrovskiĭ | Aleksandr L. Kim | Sergey A. Tikhonenko
Source: Polymers (article, 2026-03-12)
Field: Materials Science — Surfaces, Coatings and Films

Polyelectrolyte Microcapsule Stability: Non-Monotonic Layer-Dependent Desorption Kinetics of Poly(allylamine hydrochloride) Polyelectrolyte microcapsules (PMCs) fabricated by layer-by-layer assembly require predictable shell stability for applications in drug delivery, biosensing, and environmental remediation. While core template type is known to influence stability, the role of polyelectrolyte layer number in governing poly(allylamine hydrochloride) (PAH) desorption remains poorly understood. This study quantitatively assessed PAH desorption from fluorescein isothiocyanate (FITC)-labeled shells of PMCs templated on CaCO<sub>3</sub> or MnCO<sub>3</sub> cores with 7, 9, or 13 layers under varying ionic conditions (distilled water, NaCl 0.2-3.0 M, Na<sub>2</sub>SO<sub>4</sub> 0.005-1 M) over 168 h. Short-term incubations revealed no significant layer-dependent desorption differences for either core type. However, prolonged exposure uncovered a non-monotonic relationship for CaCO<sub>3</sub>-templated PMCs: 7-layer capsules exhibited high initial but limited subsequent release (<50% increase), 9-layer capsules showed minimal initial dissociation followed by maximal kinetic amplification (up to 2000% increase), and 13-layer capsules displayed intermediate behavior. In contrast, MnCO<sub>3</sub>-templated PMCs demonstrated uniformly low initial dissociation with gradual time- and concentration-dependent release irrespective of layer number. These findings establish core template nature as the dominant factor controlling dissociation kinetics, while layer number enables fine-tuning of release profiles-particularly for CaCO<sub>3</sub> systems-providing design principles for controlled-release applications requiring delayed or sustained payload delivery. Desorption ; Polyelectrolyte ; Dissociation (chemistry) ; Kinetics ; Ionic bonding ; Layer by layer ; Ionic strength Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Mingxue Shao | Huadong Guo | Chunyu Liu | Rao Bao | Song Wang | Xianmin Guo
Source: Inorganic Chemistry (article, 2026-01-31)
Field: Chemistry — Inorganic Chemistry

Electron Donor Regulation in A-D-A Type Metal–Organic Frameworks for Enhancing Visible-Light Driven Photocatalysis Charge separation efficiency is the determinant factor for reactive oxygen species (ROS) involved in photocatalytic aerobic oxidation reaction. Metal-organic frameworks (MOFs) provided a formidable platform for designing a donor-acceptor (D-A) system with tunable photoexcited carrier dynamics. Herein, we reported a series of UiO-68 type MOFs (termed as UiO-68-A-D<sub>1</sub>-A, UiO-68-A-D<sub>2</sub>-A, and UiO-68-A-D<sub>3</sub>-A) based on linearly bridged bis-benzothiadiazole as A-D-A linkers. Experimental and density functional theory calculations revealed that regulating the donor units from benzene to naphthalene and anthracene with gradually improved electron-donating capacity significantly enhanced the photogenerated charge separation and transfer efficiency and hence facilitated the ROS generation. As expected, anthracene-containing UiO-68-A-D<sub>3</sub>-A displayed the highest activity for the visible-light driven aerobic oxidation of benzylamines coupling. This work demonstrated that linker engineering of D-A molecular junctions in MOFs by fine-tuning the electronic nature of donor and acceptor units and their connection mode is a promising strategy to develop novel photocatalysts for efficient organic transformation. Photocatalysis ; Linker ; Density functional theory ; Naphthalene ; Anthracene ; Electron donor ; Electron transfer ; Benzene Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Zhiwen Shen | Xuetong He | Huicai Xie | Xinqi Zheng | Jie Shao | Junfeng Wang | Zhaojun Mo | Guihua Liu | Jing Shen
Source: Advanced Engineering Materials (article, 2026-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing the Magnetocaloric Effect of Trigonal Eu <sub>3</sub> P <sub>2</sub> O <sub>8</sub> Phosphate Near Liquid Helium Temperature via Ionic Substitution Strategy Magnetic refrigeration at liquid helium temperatures (4.2 K) serves as a critical technology for frontier scientific applications, yet its advancement is constrained by performance limitations of magnetocaloric materials (MCMs). Therefore, it is imperative to develop MCMs with large magnetocaloric effects (MCE) driven by a low magnetic field near liquid helium temperature. In this study, Sr 2+ ‐doped Eu 3 −x Sr x P 2 O 8 ( x = 0, 1/3, 2/3, and 1) series compounds were synthesized via solid‐state reaction and their magnetization characteristics were investigated. As a result, we demonstrate for the first time that nonmagnetic Sr 2+ substitution in ferromagnetic Eu 3 P 2 O 8 enables effective regulation of the magnetic ordering temperature from 6.4 K to 3.5 K. Remarkably, the maximum magnetic entropy change () remains almost constant between 10.0 and 11.1 J·kg −1 ·K −1 at μ 0 Δ H = 0–1 T despite the reduced Eu 2+ concentration upon Sr 2+ substitution; the fine‐tuning of the crystal structure and reconstruction of the electronic structure compensate for the negative impact of mere magnetic dilution on magnetocaloric performance. These compounds exhibit giant reversible MCEs near liquid helium temperature, positioning them as promising candidates for helium‐free cryogenic refrigeration. This work establishes a novel paradigm for synergistic optimization of magnetocaloric performance and phase transition temperature through single‐elementsubstituting. Magnetic refrigeration ; Liquid helium ; Magnetization ; Ferromagnetism ; Magnetic field ; Phase transition ; Ionic liquid ; Work (physics) Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis
Authors: Xinyu Li | Zhen Zhang | Qi Chen | Anton van den Hengel | Lina Yao | Javen Qinfeng Shi
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding Existing chemical understanding tasks primarily rely on static molecular representations, limiting their ability to model inherently dynamic phenomena such as bond breaking or conformational changes, which are essential for a chemist to understand chemical reactions. To address this gap, we introduce Chemical Dynamics Understanding (ChemDU), a new task that translates 4D molecular trajectories into interpretable natural-language explanations. ChemDU focuses on fundamental dynamic scenarios, including gas-phase and catalytic reactions, and requires models to reason about key events along molecular trajectories, such as bond formation and dissociation, and to generate coherent, mechanistically grounded narratives. To benchmark this capability, we construct Chem4DBench, the first dataset pairing 4D molecular trajectories with expert-authored explanations across these settings. We further propose Chem4DLLM, a unified model that integrates an equivariant graph encoder with a pretrained large language model to explicitly capture molecular geometry and rotational dynamics. We hope that ChemDU, together with Chem4DBench and Chem4DLLM, will stimulate further research in dynamic chemical understanding and multimodal scientific reasoning. Construct (python library) ; Key (lock) ; Limiting ; Task (project management) ; Vulnerability (computing) ; Molecular dynamics ; Benchmark (surveying) ; Bond graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zhu Wu | Dong Mei Wang | Jingtao Huang | Cheng Zhi Huang
Source: TrAC Trends in Analytical Chemistry (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Chiral carbon dots: Strategies for chirality control, long-wavelength emission, and their biomedical applications Chirality (physics) ; Asymmetric carbon ; Carbon fibers ; Enantioselective synthesis ; Carbon nanotube ; Characterization (materials science) Carbon and Quantum Dots Applications ; Supercapacitor Materials and Fabrication ; Electromagnetic wave absorption materials
Authors: Shi Yin | Junyi Mu | Xudong Zhu | Lei He
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

Symmetry-Driven Generation of Crystal Structures from Composition Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition in a unit cell, existing methods struggle with the NP-hard combinatorial challenge of rigorous symmetry enforcement or rely on retrieving known templates, which inherently limits both physical fidelity and the ability to discover genuinely new materials. To solve this, we propose a symmetry-driven generative framework. Our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from atomic stoichiometry, effectively circumventing the limitations inherent to database lookups. Crucially, to overcome the exponentially complex problem of combinatorial site assignments, we incorporate domain knowledge through an efficient, linear-complexity heuristic beam search algorithm that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space which can be previously uncharted, with no reliance on a priori structural knowledge. Consistency (knowledge bases) ; Heuristic ; Semantics (computer science) ; Matching (statistics) ; String (physics) ; Symmetry (geometry) ; Inference ; Template Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Feiyang Cai | Guijuan He | Yi Hu | Jingjing Wang | Joshua Luo | Tianyu Zhu | Srikanth Pilla | Gang Li | Ling Liu | Feng Luo
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential for enabling large language models (LLMs) to reason about downstream chemical tasks. However, the substantial cost of human annotation makes it infeasible to construct large-scale, high-quality datasets of structure-grounded descriptions. In this work, we propose a fully automated annotation framework for generating precise molecular structure descriptions at scale. Our approach builds upon and extends a rule-based chemical nomenclature parser to interpret IUPAC names and construct enriched, structured XML metadata that explicitly encodes molecular structure. This metadata is then used to guide LLMs in producing accurate natural-language descriptions. Using this framework, we curate a large-scale dataset of approximately $163$k molecule-description pairs. A rigorous validation protocol combining LLM-based and expert human evaluation on a subset of $2,000$ molecules demonstrates a high description precision of $98.6\%$. The resulting dataset provides a reliable foundation for future molecule-language alignment, and the proposed annotation method is readily extensible to larger datasets and broader chemical tasks that rely on structural descriptions. Annotation ; Metadata ; Construct (python library) ; Parsing ; XML ; Natural language ; Function (biology) ; Interface (matter) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yin, Shi | Mu, Jinming | Zhu, Xudong | He, Lixin
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Overcoming the Combinatorial Bottleneck in Symmetry-Driven Crystal Structure Prediction Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition and atomic counts in a unit cell, existing methods struggle with the NP-hard combinatorial challenge of rigorous symmetry enforcement or rely on retrieving known templates, which inherently limits both physical fidelity and the ability to discover genuinely new materials. To solve this, we propose a symmetry-driven generative framework. Our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from atomic stoichiometry and counts, effectively circumventing the limitations inherent to database lookups. Crucially, to overcome the exponentially complex problem of combinatorial site assignments, we incorporate domain knowledge through an efficient, linear-complexity heuristic beam search algorithm that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry and counts. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space which can be previously uncharted, with no reliance on existing databases or a priori structural knowledge. Heuristics ; Heuristic ; Consistency (knowledge bases) ; Matching (statistics) ; Bottleneck ; Semantics (computer science) ; Domain (mathematical analysis) ; Probabilistic logic ; Concurrency Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Vishal Kumbhar, Vivek B. Panchbhai, *Moein S. Attar, Ram S. Sakhare, Hanuman Hendge, Kedar Dongre, Amar Fulsundar
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-04)
Field: Chemistry — Analytical Chemistry

ANALYTICAL PROFILING OF CEFEPIME AND TAZOBACTAM: A REVIEW OF RP-HPLC METHODS Developing and validating reliable analytical techniques is a cornerstone of pharmaceutical research, directly safeguarding drug safety, quality, and clinical efficacy. This review examines a collection of studies dedicated to creating robust, stability-indicating chromatographic methods capable of simultaneously measuring multiple drugs in both bulk and combination dosage forms. Across the evaluated literature, Reverse Phase High-Performance Liquid Chromatography (RP-HPLC) consistently emerges as the preferred technique, valued for its sensitivity, reliability, and practicality in routine quality control. Methodologically, these studies predominantly utilized C18 columns. Effective separation was typically achieved by fine-tuning mobile phases—combining aqueous buffers with organic solvents like methanol or acetonitrile—and pairing them with UV or Photodiode Array (PDA) detection at optimal absorption wavelengths. Researchers focused heavily on optimizing these methods to ensure clean separation of active analytes from potential impurities, while maintaining sharp peak symmetry and practical retention times. Crucially, all reviewed methods were rigorously validated against standard regulatory guidelines, successfully meeting criteria for accuracy, precision, linearity, robustness, and limits of detection and quantification. The collective results demonstrated excellent reproducibility and high accuracy. Furthermore, by subjecting formulations to forced degradation—including thermal, photolytic, acidic, alkaline, and oxidative stress—these studies proved that the developed RP-HPLC methods can easily distinguish intact active drugs from their degradation products. Ultimately, this body of research underscores RP-HPLC as an exceptionally dependable, fast, and cost-effective tool, ideally suited for everyday pharmaceutical quality control and stability monitoring. Analyte ; Pharmaceutical formulation ; Forced degradation ; Active ingredient ; Drug ; Analytical technique ; Pharmaceutical drug ; Analyser Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: Saagar Devassy Chaklathy | Tanu Choudhary | G Sundaram Venkatesan | Satyam Suwas | Raju K. Biswas | Ramesh Chandra Mallik
Source: ACS Applied Energy Materials (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Exploring the Role of Interstitial Occupation of Cobalt on the Thermoelectric Properties of Co-Excess Te-Substituted Skutterudite CoSb <sub>3</sub> The introduction of excess cobalt in skutterudite CoSb3 causes the excess cobalt atoms to preferentially occupy an interstitial position over the conventional lattice sites, enabling additional phonon scattering mechanisms in addition to mass fluctuations and rattlers, which collectively help in fine-tuning the thermal conductivity. The presence of the interstitial cobalt atom is reflected in the high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) image viewed along the [111] axis of the Co1.02Sb3 sample. This occupation of interstitial cobalt is also detected by the introduction of additional Raman active modes (123.82 and 164.38 cm–1) observed by Raman spectroscopy. The electronic, structural, and vibrational properties of pristine, Co-excess, and Te-substituted Co-excess CoSb3 are studied using density functional theory. The Co-excess sample Co1.02Sb3 displays a p-type semiconducting nature with an enhanced Seebeck coefficient compared to that of pristine CoSb3, driven by the conventional and unconventional band convergence present in the valence band. Te substitution into Co1.02Sb3 forms an n-type degenerate semiconductor, accompanied by the emergence of strong Co 3d and Te 5p orbital interactions as revealed by crystal orbital Hamilton population analysis, exhibiting a significant antibonding state just below the Fermi level. The introduction of additional vibrational modes due to interstitial cobalt, concurrent with the mass fluctuations in Te-substituted Co1.02Sb3, maintains a low thermal conductivity (3.60 W/m·K at 675 K). The maximum zT achieved is 0.76 at 773 K for the composition Co1.02Sb2.90Te0.10. X-ray photoelectron spectroscopy studies on a cleaned and desiccated sample revealed the oxidation of the sample surface at room temperature to form native oxides Sb2O3 and Co(OH)2. Etching the sample surface by 41 nm effectively removed the oxides, enabling accurate analysis of the chemical state of elements. Skutterudite ; Cobalt ; Thermoelectric effect ; Thermoelectric materials ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Semiconductor materials and interfaces ; Heusler alloys: electronic and magnetic properties
Authors: Abdul Hafeez | H. M. Naeem Ullah | S. S. Ali | S. A. Zahra | Imed Boukhris | Ali El-Said Hassan El-Rayyes
Source: Journal of Materials Science Materials in Electronics (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

2D Ti3C2Tx MXene sheets composited with ZnFe2O4, structural, morphological, temperature-dependent optical and magnetic features MXenes ; Crystallite ; Coercivity ; Lamellar structure ; Spinel ; Annealing (glass) ; Ferrimagnetism ; Band gap MXene and MAX Phase Materials ; Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites