jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maxim Papusha | Kai Leonhard
Source: ChemRxiv (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

StereoMolGraph: Stereochemistry-Aware Molecular and Reaction Graphs Conventional molecular graphs often are unable to reliably encode stereochemistry, especially for symmetric molecules, non-tetrahedral centers, and transition states. To overcome this, we present StereoMolGraph, an open source Python library implementing a stereochemistry-aware graph representation for molecules and condensed graphs of reactions. Our method uses permutation invariant local stereodescriptors, grounded in group theory, to provide an extensible representation of chirality. Based on this we introduce methods allowing for robust comparison of stereoisomers, including the identification of enantiomerism and diastereomerism, and supports fleeting stereochemistry in transition states. We demonstrate the library's utility for complex organic molecules and metal complexes and analysis of distinct chiral reaction pathways. With RDKit interoperability and visualization features, StereoMolGraph offers a practical and transparent tool for advanced stereochemically aware chemoinformatics workflows. Cheminformatics ; Visualization ; Invariant (physics) ; Representation (politics) ; Python (programming language) ; Vertex (graph theory) ; Interoperability ; Graph Machine Learning in Materials Science ; Origins and Evolution of Life ; Molecular spectroscopy and chirality
Authors: Kazi Rahman | Jonathan Bradford | Samiyah A Alghamdi | Benjamin T. Dewes | Adeem Ali M Alzeer | Nathan D. Cottam | Mustaqeem Shiffa | Tin S. Cheng | Sergei V Novikov | O. Makarovsky | Christopher J. Mellor | Manuel Mundszinger | Johannes Biskupek | Ute Kaiser | David Gonzalez | Teresa Ben | James N. O’Shea | A. Patanè
Source: Communications Materials (article, 2026-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

In operando synthesis of an ultrathin dielectric based on crystalline gallium oxide Abstract Dielectric materials are essential in modern science and technology, but their miniaturization represents a challenge. Herein, we synthesize a dielectric based on atomically thin crystalline gallium oxide (Ga 2 O 3 ) on conducting graphene. The oxide is obtained via the direct oxidation of a two-dimensional semiconductor (gallium selenide, GaSe) grown by molecular beam epitaxy on graphene and is probed in real time during the oxidation by near-ambient pressure X-ray photoelectron spectroscopy. The oxide possesses (i) a clean, atomically abrupt interface with graphene; (ii) a wide band gap (4.5 eV) with a weakly dispersed valence band; (iii) large conduction and valence band offsets ( &gt;1.5 eV) relative to the Dirac cone of graphene; and (iv) a large break down electric field, as probed locally by conductive atomic force microscopy. The direct oxidation of the semiconductor enables the integration of conducting and insulating building blocks for scalable nanoelectronics. Also, it provides a general route for synthesizing oxides from a variety of metal-based two-dimensional semiconductors. Dielectric ; Oxide ; Graphene ; Semiconductor ; Band gap ; Valence (chemistry) ; Gate dielectric ; Electrical conductor Ga2O3 and related materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Yuchen Hua | Xingang Peng | Jianzhu Ma | Muhan Zhang
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

VecMol: Vector-Field Representations for 3D Molecule Generation Generative modeling of three-dimensional (3D) molecules is a fundamental yet challenging problem in drug discovery and materials science. Existing approaches typically represent molecules as 3D graphs and co-generate discrete atom types with continuous atomic coordinates, leading to intrinsic learning difficulties such as heterogeneous modality entanglement and geometry-chemistry coherence constraints. We propose VecMol, a paradigm-shifting framework that reimagines molecular representation by modeling 3D molecules as continuous vector fields over Euclidean space, where vectors point toward nearby atoms and implicitly encode molecular structure. The vector field is parameterized by a neural field and generated using a latent diffusion model, avoiding explicit graph generation and decoupling structure learning from discrete atom instantiation. Experiments on the QM9 and GEOM-Drugs benchmarks validate the feasibility of this novel approach, suggesting vector-field-based representations as a promising new direction for 3D molecular generation. Coherence (philosophical gambling strategy) ; Parameterized complexity ; Representation (politics) ; Quantum entanglement ; Dot product ; Field (mathematics) ; Graph ; Molecular graph ; Euclidean geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum many-body systems
Authors: Sarada Prasad Sarangi | K. Saravanan
Source: International Journal of Pharmaceutical Sciences and Drug Analysis (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Synthesis and evaluation of oxadiazole derivatives in drug design and development The current review summarizes synthesis and biological activity of the oxadiazole derivatives to date. The results described in the review are of high significance and priority for the development of new approaches in the drug design and discovery. Numerous advances have been achieved in the synthetic methodologies during the last years, which allowed obtaining a number of novel and diverse structural scaffolds. This has dramatically expanded the functionalization and derivatization potential of these heterocyclic compounds and translated them from a kind of laboratory product into an important part of the therapeutically valuable molecules. The review summarizes succintly information on newly emerging and traditional approach for development of small oxadiazoles and their biological activity and illustrates the impact of the oxadiazoles derivatives on human pharmaceutical development. Drug development ; Human use ; Drug discovery ; Drug ; Derivatization Synthesis and biological activity ; Structural and Chemical Analysis of Organic and Inorganic Compounds ; Synthesis of Tetrazole Derivatives
Authors: Jiangao Qiu | Xirui Zhang | Jie Guo | Tao He | Lina Zhang | Shaohua Fu | Ronglei Pan | Keyan Jia
Source: Agricultural Products Processing and Storage (article, 2026-01-14)
Field: Chemistry — Analytical Chemistry

Research progress of spectroscopic detection technology in agricultural product quality detection With the continuous improvement of smart agriculture and food safety requirements, accurate detection of agricultural product quality has become a key link in the entire agricultural industry chain. As an important non-destructive detection method for agricultural product quality, spectroscopic detection technology has achieved significant progress in detection accuracy, speed, and application scope in recent years. This paper systematically reviews the application research of technologies such as hyperspectral imaging (HSI), visible-near-infrared spectroscopy (Vis-NIR), near-infrared spectroscopy (NIRS), mid-infrared spectroscopy (MIRS), Raman spectroscopy (Raman), X-ray computed tomography (X-ray CT), surface-enhanced Raman spectroscopy (SERS), fluorescence imaging, terahertz time-domain spectroscopy (THz-TDS), and electrochemical impedance spectroscopy (EIS) in the quality detection of various agricultural products. It focuses on analyzing the latest achievements of these technologies in external quality evaluation, internal quality evaluation, nutrient component analysis, freshness and safety detection, and adulteration identification of agricultural products. This review also summarizes the challenges faced by existing spectroscopic detection technologies and proposes prospects for future development directions, including the development of miniaturized equipment, multi-technology integration, and intelligent analysis, aiming to provide comprehensive references for further research and application of spectroscopic technology in the field of agricultural product quality detection. Quality (philosophy) ; Agriculture ; Product (mathematics) ; Hyperspectral imaging ; Precision agriculture ; Agricultural machinery ; Identification (biology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Biosensors and Analytical Detection
Authors: Wenli Yuan | Jiayong Huang | Jianqiang Feng | Shengheng Yan | Lina Dong | Yangyang Song | Zikuan Wang | Xiaoyun Xiao | Lanteng Wang | Jiahai Zhou | Sason Shaik | Binju Wang
Source: ChemRxiv (article, 2026-03-11)
Field: Chemistry — Inorganic Chemistry

Interplay of Iron Coordination Dynamics and Second-Sphere H-Bonding Machinery Governs the Selective Halogenation by α-KG/Fe-dependent SyrB2 In this study, we elucidate the halogenation mechanism catalyzed by SyrB2 through an integrated computational approach, which combines simulated annealing (SA), molecular dynamics (MD) simulations, and quantum mechanical/molecular mechanical-molecular dynamics (QM/MM MD) simulations. Our investigations reveal an alternative binding mode of α-ketoglutarate (α-KG), which corroborates the inferred substrate positioning from 2 H-HYSCORE spectroscopic data and promotes the formation of a five-coordinate equatorial Fe(IV)-oxo intermediate. Key findings highlight that the coordination dynamics of the iron center governs SyrB2's chlorination selectivity. The transformation of the Fe(IV)oxo species from an equatorial to an axial conformation facilitates hydrogen-atom abstraction from the C−H bond of the substrate. Subsequent re-isomerization of the Fe(III)-OH intermediate to the equatorial conformation is critical for selective chlorination while minimizing competitive hydroxylation. This mechanistic scheme is further validated by the reactivity of the non-native substrate L-Nva. As L-Nva has an extended carbon chain and lacks the constraints of the hydroxyl-mediated hydrogen-bonding interactions, the corresponding C5 radical intermediate is positioned closer to the OH-ligand than the Clligand, leading to preferential hydroxylation, in accord with experimental observations. Our modeled structure is consistent with the resolved SyrB1-PPant-SyrB2 crystal structure, supporting the reliability of our modeling protocol. Altogether, our work reveals that an interplay between iron-ligand dynamics and second-sphere hydrogen-bonding networks governs selective chlorination in the SyrB1-SyrB2 complex. Halogenation ; Molecular dynamics ; Reactivity (psychology) ; Substrate (aquarium) ; Work (physics) ; Radical ; Dynamics (music) Metal-Catalyzed Oxygenation Mechanisms ; Porphyrin Metabolism and Disorders ; Enzyme Structure and Function
Authors: Krishna Prasad Nooralabettu | Laila Jaseela | Lahari Lakshmi Prasad | Vinayaka B. Shet | Ronald Valder | Nafeesath Irfana | Khadeejathe Shahla
Source: Fisheries and aquatic sciences (article, 2026-02-01)
Field: Materials Science — Biomaterials

Optimization of sequential solvent extraction of collagen and ommochrome from Indian squid, Uroteuthis duvauceli This study explores and compares the predictive efficiency of one-factor-at-a-time (OFAT), response surface methodology (RSM), and various machine learning (ML) techniques in optimizing ultrasound and enzyme-assisted sequential extraction of collagen and ommochrome from Indian squid skin. A 3-level, 2-factor experimental design was implemented, focusing on two key variables: ultrasound exposure time and buffer temperature. Ultraviolet-visible absorbance readings at 230 and 280 nm served as the primary response indicators. Initial extraction conditions were identified using the OFAT approach. RSM modeling effectively captured the interactive effects of the two independent variables, confirming the suitability of a quadratic model. To further evaluate predictive accuracy, ML models including artificial neural networks (ANN), k-nearest neighbors, and Random Forest were trained and compared against the RSM model. Among these, the ANN model demonstrated superior performance, as evidenced by the highest coefficient of determination (R2), indicating its enhanced capability for accurate prediction of extraction outcomes. Extraction (chemistry) ; Solvent ; Solvent extraction ; Organic solvent ; Quantitative analysis (chemistry) Collagen: Extraction and Characterization ; Cephalopods and Marine Biology ; Protein Hydrolysis and Bioactive Peptides
Authors: Susana Blanco | Juan Carlos López | Arturo Alcorta-Sánchez | J. Elguero | Maxime Ferrer | Ibon Alkorta
Source: The Journal of Chemical Physics (article, 2026-01-26)
Field: Chemistry — Spectroscopy

Rotational spectrum and theoretical calculations of pyrazole⋯CO2 complex: Tetrel and hydrogen bond interactions The complex between pyrazole and carbon dioxide has been generated in a supersonic jet and characterized using Fourier transform microwave spectroscopy and state of the art CCSD(T) theoretical calculations. The complex presents a planar configuration showing a simultaneous N⋯C=O n → π* tetrel bond and a NH⋯O hydrogen bond. The TS internal rotation barrier that interconverts the oxygen atoms of CO2 has been calculated to be 10 kJ mol-1 at the CCSD(T) level. The electronic characteristics of the minimum and TS have been analyzed. Machine learning methods have been applied to predict the potential energy surface of the pyrazole⋯carbon dioxide complex. Hydrogen ; Rotational spectroscopy ; Hydrogen bond ; Spectroscopy ; Fourier transform ; Supersonic speed ; Spectral line ; Jet (fluid) ; Spectrum (functional analysis) ; Potential energy surface Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Astrophysics and Star Formation Studies
Authors: Gregory K. Hodgson | Stefania Impellizzeri
Source: BioEssays (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Metal‐Enhanced Fluorescence for the Biologist's Cellular Imaging Toolkit: Design Principles and Recent Applications Fluorescence microscopy is essential in modern cell biology but remains constrained by photobleaching, autofluorescence, and the intrinsic quantum yields of emitters. Metal-enhanced fluorescence (MEF) is a photophysical phenomenon in which interactions between luminescent species and metal nanostructures markedly increase emission, enabling a route to brighter, high-contrast, noninvasive bioimaging by reshaping photophysical pathways without modifying fluorophore chemistry. This review translates MEF fundamentals into an experimental playbook for biologists, distinguishing MEF mechanisms and explaining how distance, spectral overlap, and nanoparticle morphology govern whether emission is boosted or quenched. We synthesize recent live-cell applications-using gold and silver nanoparticles-to illustrate gains in signal-to-noise at lower excitation power, improved photostability, and opportunities where small-molecule dyes often suffer low quantum yield, and provide practical guidelines for pairing dyes with metal nanostructures to lower the barrier to adopting MEF in cellular imaging. Fluorophore ; Fluorescence ; Design elements and principles ; Quantum dot ; Nanostructure ; Fluorescence microscope ; Luminescence Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Fluorescence Microscopy Techniques
Authors: Ljiljana Fruk | Andrew N. Baker | Anna Erdinger | Adaobi Chike | Andrew Te Water Naude | Adrian Pui Ting Ho | Laia Hernandez-Marco | Farzin Chougani | John McGinty | Cheng Wei | Heather F. Greer | D Ian Wilson | Oliver Vanderpoorten
Source: Research Square (preprint, 2026-01-22)
Field: Materials Science — Biomaterials

Nano-dynamic imaging: NanoSpacer as a low-cost optical method for real-time visualisation of nanoparticle disassembly and functionalisation Nanorod ; Nanomaterials ; Nanoscopic scale ; Supramolecular chemistry ; Nanoparticle ; Atomic force microscopy ; Polydimethylsiloxane Supramolecular Self-Assembly in Materials ; Crystallography and molecular interactions ; Luminescence and Fluorescent Materials
Authors: Lane E. Schultz | Nickolas Gantzler | N. Scott Bobbitt | Dorina F. Sava Gallis | Rémi Dingreville
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Graph neural network-based multi-objective Bayesian optimization for enhanced screening of metal–organic frameworks with optimal separation performance Metal–organic frameworks (MOFs) are porous crystalline materials with applications in gas capture, drug delivery, and molecular separations. Bayesian optimization ; Bayesian probability ; Graph ; Artificial neural network ; Separation (statistics) ; Minification ; Pattern recognition (psychology) Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Machine Learning in Materials Science
Authors: Marilia Inês Soares Ferrante | Juan Philippe-Teixeira | Kátia Kalko Schwarz | Daniel P. Willemann | Paulo Cezar Bastianello Campagnol | Márcio Vargas-Ramella
Source: Processes (article, 2026-04-05)
Field: Materials Science — Biomaterials

Protocol Proposal and Molecular Docking Mechanistic Elucidation of an Ecological Tanning Process for Fish Skin Chrome tanning of fish skins generates hazardous effluents and carcinogenic Cr(VI) residues; chromium-free routes to valorize collagen-rich by-products from aquaculture and coastal fisheries are therefore needed. We report a 12-stage ecological protocol employing acetic acid/NaCl pickling, Acacia mearnsii tannin, A. podalyriifolia retanning, mashed-papaya enzymatic bating, and cinnamon as antimicrobial/odor adjunct, scaled from bench to pilot using exclusively locally sourced inputs, for Nile tilapia (Oreochromis niloticus) and Patagonian flounder (Paralichthys patagonicus). Three trained operators evaluated macroscopic quality against five predefined criteria adapted from SATRA and ISO 3376 grading conventions, providing a structured feasibility baseline that does not substitute for the standardized instrumental testing designated as priority future work. Both species achieved satisfactory grain stability, complete tannin penetration, pliable handle, and cinnamon-dominant odor without residual amines; dark-brown coloration is a recognized practical limitation for fashion applications. In silico molecular docking (GNINA v1.0) was used to explore the mechanistic plausibility of each ecological substitution, generating testable hypotheses rather than definitive mechanistic conclusions: the multidentate polyphenol proxy (PGG) exhibited consistently superior collagen engagement over the flavanol monomer across both collagen constructs and all three scoring metrics (1CAG: Vina affinity −5.51 ± 0.13 vs. −3.54 ± 0.35 kcal/mol; CNNscore 0.874 ± 0.009 vs. 0.771 ± 0.010; 7CWK: Vina affinity −6.98 ± 1.43 vs. −4.37 ± 0.16 kcal/mol; CNNscore 0.858 ± 0.024 vs. 0.635 ± 0.094). Dipeptide probes were reproducibly accommodated in the papain catalytic cleft, with the closest configuration reaching 3.997 Å from the catalytic nucleophile (OCS25-SG). Trans-cinnamaldehyde occupied the quorum-sensing pocket with reproducible placement (CNNscore 0.718 ± 0.034) but without score-based selectivity over structural decoys, a result interpreted as hypothesis-generating for future microbiological validation. The protocol is reproducible from bench to pilot and generalizable across two species with distinct dermal architectures. Quantitative physical-mechanical testing (shrinkage temperature, tensile strength, elongation, tear load), CIELab colorimetric analysis, and effluent characterization (COD, BOD5, total phenolics) are designated as priorities for future validation. In silico ; Morone saxatilis ; Acacia mearnsii ; Fish <Actinopterygii> ; Tannin Collagen: Extraction and Characterization ; Dyeing and Modifying Textile Fibers ; Skin Protection and Aging
Authors: Preet Kaur
Source: Discover Electronics (article, 2026-03-24)
Field: Materials Science — Ceramics and Composites

Enhancing performance of photovoltaic cell by spectral modification: a review The spectral mismatch between solar cells and incident radiation fundamentally limits their efficiencies as photons outside the optimal absorption range of the cell are lost due to transmission or thermalization. This challenge has driven research into materials and processes capable of modifying the solar spectrum to align better with the absorption characteristics of photovoltaic devices. Rare-earth-doped glasses and glass ceramics have emerged as promising candidates for addressing this limitation, exploiting energy up-conversion and down-conversion mechanisms to enhance solar cell performance. This review provides a systematic comparison of rare-earth-doped glasses, nanoparticle-embedded glass ceramics and transparent glass ceramics as spectral modification materials for photovoltaic applications, with emphasis on their underlying energy conversion mechanism. Alternative emerging approaches such as perovskite quantum dot-embedded glasses and organic luminophores are reviewed to provide a broader perspective on spectral conversion technologies. Device-level challenges and future opportunities are also discussed. It is observed that rare-earth-doped materials offer a transformative approach to overcoming the spectral mismatch problem, paving the way for more efficient and sustainable solar energy technologies. Photovoltaic system ; Absorption (acoustics) ; Solar energy ; Solar cell ; Perovskite (structure) ; Plasmonic solar cell ; Photovoltaics ; Photon ; Thermophotovoltaic Glass properties and applications ; Perovskite Materials and Applications ; TiO2 Photocatalysis and Solar Cells
Authors: Advait Gore | Xander A. Gouws | Conrard Giresse Tetsassi Feugmo
Source: ChemRxiv (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

TorchDisorder: A Differentiable Framework for Generating Physically Realistic Disorder Structures from Experimental Diffraction Data Determining the atomic structure of amorphous materials remains a fundamental challenge in condensed-matter physics and materials science. Unlike crystalline solids, disordered systems lack long-range periodicity, which makes conventional diffraction analysis insufficient for resolving the three-dimensional atomic arrangements. Existing reverse Monte Carlo (RMC) approaches rely on stochastic sampling, limiting both computational efficiency and the ability to enforce chemically realistic local environment. Here we present TorchDisorder, a PyTorch-based framework that replaces stochastic moves with gradient-based optimization via automatic differentiation, built on three tightly integrated components: GPU-accelerated neighbor list construction via torch-sim, augmented Lagrangian constrained optimization via the Cooper library, and a differentiable structure factor engine that propagates gradients through the full Faber-Ziman weighted Fourier transform. Coordination constraints for tetrahedral, octahedral, and other geometries are specified via JSON configuration files generated automatically from crystalline precursors, and requires no manual parameter tuning. We apply TorchDisorder to three glass systems relevant to energy technology, namely silica (SiO2), germania (GeO2), and lithium thiophosphate (Li2S-P2S5) solid electrolytes, and obtain structural models in quantitative agreement with experimental scattering data ($R^2 \geq 0.955$) within 5000 gradient steps, outperforming stochastic RMC in both convergence speed, and constraint satisfaction Reverse Monte Carlo ; Differentiable function ; Diffraction ; Convexity ; Hessian matrix ; Monte Carlo method ; Constraint (computer-aided design) ; Convergence (economics) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis