jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eppel, Sagi | Strugatski, Alona
Source: Open MIND (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

SciTextures: Collecting and Connecting Visual Patterns, Models, and Code Across Science and Art The ability to connect visual patterns with the processes that form them represents one of the deepest forms of visual understanding. Textures of clouds and waves, the growth of cities and forests, or the formation of materials and landscapes are all examples of patterns emerging from underlying mechanisms. We present the SciTextures dataset, a large-scale collection of textures and visual patterns from all domains of science, tech, and art, along with the models and code that generate these images. Covering over 1,270 different models and 100,000 images of patterns and textures from physics, chemistry, biology, sociology, technology, mathematics, and art, this dataset offers a way to explore the deep connection between the visual patterns that shape our world and the mechanisms that produce them. Built through an agentic AI pipeline that autonomously collects, implements, and standardizes scientific and generative models. This AI pipeline is also used to autonomously invent and implement novel methods for generating visual patterns and textures. SciTextures enables systematic evaluation of vision language models (VLM's) ability to link visual patterns to the models and code that generate them, and to identify different patterns that emerge from the same underlying process. We also test VLMs ability to infer and recreate the mechanisms behind visual patterns by providing a natural image of a real-world phenomenon and asking the AI to identify and code a model of the process that formed it, then run this code to generate a simulated image that is compared to the reference image. These benchmarks reveal that VLM's can understand and simulate physical systems beyond visual patterns at multiple levels of abstraction. The dataset and code are available at: https://zenodo.org/records/17485502 Pipeline (software) ; Code (set theory) ; Process (computing) ; Generative grammar ; Source code ; Generative model ; Visual language ; Feature (linguistics) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Data Visualization and Analytics
Authors: Eppel, Sagi | Strugatski, Alona
Source: ArXiv.org (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

SciTextures: Collecting and Connecting Visual Patterns, Models, and Code Across Science and Art The ability to connect visual patterns with the processes that form them represents one of the deepest forms of visual understanding. Textures of clouds and waves, the growth of cities and forests, or the formation of materials and landscapes are all examples of patterns emerging from underlying mechanisms. We present the SciTextures dataset, a large-scale collection of textures and visual patterns from all domains of science, tech, and art, along with the models and code that generate these images. Covering over 1,270 different models and 100,000 images of patterns and textures from physics, chemistry, biology, sociology, technology, mathematics, and art, this dataset offers a way to explore the deep connection between the visual patterns that shape our world and the mechanisms that produce them. Built through an agentic AI pipeline that autonomously collects, implements, and standardizes scientific and generative models. This AI pipeline is also used to autonomously invent and implement novel methods for generating visual patterns and textures. SciTextures enables systematic evaluation of vision language models (VLM's) ability to link visual patterns to the models and code that generate them, and to identify different patterns that emerge from the same underlying process. We also test VLMs ability to infer and recreate the mechanisms behind visual patterns by providing a natural image of a real-world phenomenon and asking the AI to identify and code a model of the process that formed it, then run this code to generate a simulated image that is compared to the reference image. These benchmarks reveal that VLM's can understand and simulate physical systems beyond visual patterns at multiple levels of abstraction. The dataset and code are available at: https://zenodo.org/records/17485502 Pipeline (software) ; Code (set theory) ; Process (computing) ; Generative grammar ; Source code ; Generative model ; Visual language ; Feature (linguistics) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Data Visualization and Analytics
Authors: Sudarshan Babu | P Lo | Xiao Zhang | Avanish Kumar Srivastava | Ali Davariashtiyani | Jason Perera | Michael Maire | Aly A. Khan
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fields We introduce HyperDiffusionFields (HyDiF), a framework that models 3D molecular conformers as continuous fields rather than discrete atomic coordinates or graphs. At the core of our approach is the Molecular Directional Field (MDF), a vector field that maps any point in space to the direction of the nearest atom of a particular type. We represent MDFs using molecule-specific neural implicit fields, which we call Molecular Neural Fields (MNFs). To enable learning across molecules and facilitate generalization, we adopt an approach where a shared hypernetwork, conditioned on a molecule, generates the weights of the given molecule's MNF. To endow the model with generative capabilities, we train the hypernetwork as a denoising diffusion model, enabling sampling in the function space of molecular fields. Our design naturally extends to a masked diffusion mechanism to support structure-conditioned generation tasks, such as molecular inpainting, by selectively noising regions of the field. Beyond generation, the localized and continuous nature of MDFs enables spatially fine-grained feature extraction for molecular property prediction, something not easily achievable with graph or point cloud based methods. Furthermore, we demonstrate that our approach scales to larger biomolecules, illustrating a promising direction for field-based molecular modeling. Property (philosophy) ; Field (mathematics) ; Feature (linguistics) ; Artificial neural network ; Function (biology) ; Feature vector ; Point (geometry) ; Space (punctuation) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Computational Drug Discovery Methods
Authors: Tao Bo | Li Cheng | Yuxin He | Shuo Zhang | Wenlong Xu | Ruijin Liao
Source: Materials Chemistry and Physics (article, 2025-05-13)
Field: Materials Science — Surfaces, Coatings and Films

Modeling concentration-dependent oligosiloxane diffusion for tangential hydrophobicity transfer Diffusion ; Materials science ; Chemical engineering ; Polymer chemistry ; Chemistry ; Chemical physics ; Thermodynamics ; Physics Surface Modification and Superhydrophobicity ; High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation
Authors: Gleb Solovev | Ivan Gurev | Anastasia Vepreva | Ivan Dubrovsky | Alina Borisovna Zhidkovskaya | Kamil Fatkhiev | Elizaveta Aleksandrovna Lutsenko | Anastasia Orlova | Nina Gubina | Nikolay O. Nikitin | Andrei Dmitrenko | Anna V. Kalyuzhnaya
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

ChemCoScientist: LLM-Based Multi-Agent Assistant for Automated Solving of Chemical Tasks Using Data-Driven Tools This paper introduces a multi-agent system designed to automate data-intensive machine learning workflows. Using drug discovery as a case study, we deploy specialized agents to execute a pipeline comprising: (1) targeted information retrieval from scientific literature, (2) automated data preparation informed by the extracted knowledge, and (3) the training of both predictive and generative models. Our results demonstrate that the system successfully automates complex data curation and model training, achieving performance comparable to that of manually engineered pipelines. This work underscores the critical role of automated data mining in enabling robust, end-to-end problem-solving in data-scarce domains. The ChemCoScientist is available in https://github.com/ITMO-NSS-team/CoScientist. Task (project management) ; Automation ; Software ; Process (computing) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: S. J. Macwan | Satish K. Warrier | Vipul K. Dabhi | Harshadkumar B. Prajapati
Source:  (article, 2025-07-25)
Field: Chemistry — Analytical Chemistry

Maize Grain Classification Using Machine Learning Automated maize grain classification represents a critical technological challenge in precision agriculture, addressing fundamental requirements for food quality assessment and agricultural productivity. This paper proposes a hybrid framework integrating YOLO-based object detection, a two-stage Support Vector Machine (SVM) classifier, and U<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-Net semantic segmentation to enhance classification accuracy and efficiency. The system achieves 95% accuracy while systematically categorizing maize grains into six quality classes: healthy, broken, damaged, fungus-affected, organic, and inorganic, with F1-scores between 0.93-0.98. An initial classification stage identifies primary grain groups—good grains, bad grains, and foreign materials—which are then refined through detailed sub-classification using advanced feature extraction techniques. To ensure robustness, a custom high-resolution dataset was developed using Segment Anything Model (SAM) grain isolation and Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) super-resolution. The research implements a Gradio-based interactive frontend, enabling real-time agricultural quality assessment and demonstrating the framework’s practical utility. Experimental results demonstrate 13-second processing time per image, with U<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>-Net segmentation requiring 8 seconds and classification operations 5 seconds, establishing a practical balance between accuracy and computational efficiency. Segmentation ; Support vector machine ; Quality (philosophy) ; Feature (linguistics) ; Feature extraction ; Pattern recognition (psychology) ; Image segmentation ; Object (grammar) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Soline Quilliard | Djibril Rouzaud | Cassandre Kouvatas | Philippe Bazin | Arnaud Prigent | Arnaud Travert | Benoı̂t Duchemin
Source: Research Square (preprint, 2025-08-14)
Field: Materials Science — Biomaterials

Cellulose–TBPH interactions from diffusion NMR, rheology and SAXS studies Small-angle X-ray scattering ; Diffusion ; Radius of gyration ; Polymer ; Thermal diffusivity ; Rheology ; Dissolution ; Cellulose Advanced Cellulose Research Studies ; NMR spectroscopy and applications ; Lignin and Wood Chemistry
Authors: Tianshu Li | H Park | A. T. Walsh
Source: Chemistry of Materials (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Data-Driven Crystal Structure Prediction for Ternary Metal Chalcogenides The efficient design and discovery of stable inorganic crystal structures is central to materials innovation. Here, we compare data-driven approaches for accelerated crystal structure prediction: substitution into known prototype structures, generative artificial intelligence (genAI) using denoising diffusion as implemented in Chemeleon, and an evolutionary global optimization search. Candidate structures are optimized using an ensemble of machine-learned interatomic potentials, providing both energy estimates and uncertainty quantification. Applied to a large set of known and hypothetical ternary metal chalcogenide compositions, including technologically relevant sulfides such as Na<sub>2</sub>SiS<sub>3</sub>, RbPS<sub>3</sub>, and KMo<sub>2</sub>S<sub>4</sub>, our analysis reveals that the genAI approach not only matches but can surpass traditional methods in identifying diverse, low-energy structures. These findings highlight the promise of generative models for scalable structural exploration of inorganic materials space. Crystal structure prediction ; Ternary operation ; Chalcogenide ; Crystal structure ; Scalability ; Crystal (programming language) ; Set (abstract data type) ; Generative grammar Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Hongbo Chen (512850) | Jingrui Shi (21533961) | Ming Hu (38573) | Anni Shi (9310522) | Daniel K. Schwartz (1303779) | Dapeng Wang (250459)
Source: Figshare (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Anomalous versusFickian-but-Not-Gaussian Diffusionin Heterogeneous Systems Depends on the Time Scale of Observation Both Fickian-but-not-Gaussian diffusion (FnGD) and anomalous diffusion have been observed in various heterogeneous systems. It remains unclear whether FnGD is an independent diffusion process and what factors determine whether anomalous diffusion or FnGD will be observed. Here, we studied two well-defined heterogeneous systems. The first system involves the simulation of polymer diffusion within polymer–nanoparticle mixtures (exhibiting temporal and population heterogeneity), and the second system involves single-molecule fluorescence tracking experiments of nanoparticle diffusion in inverse opals (exhibiting spatial heterogeneity). Despite the qualitative physical differences between these two systems, different types of diffusion, including anomalous diffusion, FnGD, and Brownian diffusion can emerge at varying time scales provided the system is ergodic. The observed phenomena are contingent upon the specific observation time window. Within the same system, diffusion that emerged at different time scales can be interpreted through different diffusion models. These observations suggest that future research should consider characterization across multiple time scales to fully elucidate the underlying mechanisms of heterogeneous systems. Anomalous diffusion ; Diffusion ; Diffusion process ; Population ; Tracking (education) ; Brownian motion ; Characterization (materials science) ; Scale (ratio) Material Dynamics and Properties ; Advanced Fluorescence Microscopy Techniques ; Quantum optics and atomic interactions
Authors: B. Abirami | Krishna Madhavan | R. Ramesh
Source:  (article, 2025-03-28)
Field: Chemistry — Analytical Chemistry

Optimized Hyperspectral Crop Classification using Coot Bird Algorithm Hyperspectral remote sensing (HSRS), also known as imaging spectroscopy, is a cutting-edge method for capturing spectral data across numerous spatial locations in a wide array of narrow spectral wavelengths, enabling the generation of detailed and coherent images. HSRS involves collecting digital images across contiguous spectral bands, spanning the visible, Near Infrared (NIR), Thermal Infrared (TIR), and MidInfrared (MIR) regions of the electromagnetic spectrum. For agricultural applications, remote sensing techniques facilitate continuous and quantitative monitoring, providing crucial physical and chemical information about vegetation. This study introduces the Agricultural Crop Classification using Coot Bird Optimization with EntropyBased Feature Fusion (ACC-CBOEFF) technique for hyperspectral imagery (HSI). The ACC-CBOEFF approach leverages feature fusion and parameter tuning to classify various crop types effectively. It incorporates Gabor filtering as a preprocessing step and employs entropy-based feature fusion using ShuffleNet, EfficientNet, and DenseNet models for robust feature extraction. Additionally, the Deep Variational Autoencoder (DVAE) system is utilized for crop type classification, with the Coot Bird Optimization (CBO) method optimizing DVAE hyperparameters. Simulations on benchmark datasets demonstrate the superior performance of the ACC-CBOEFF technique compared to other state-of-theart methods Hyperspectral imaging ; Computer science ; Statistical classification ; Crop ; Artificial intelligence ; Remote sensing ; Geography ; Forestry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Algorithms and Applications
Authors: C.M. Kane (21359774) | A. Banisafar (21359873) | T.P. Dougherty (21359876) | Len Barbour (20810465) | K.T. Holman (21359651)
Source: Figshare (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Enclathration and Confinement of Small Gases by the Intrinsically 0D Porous Molecular Solid, Me,H,SiMe2 The stable, guest-free crystal form of the simple molecular cavitand, Me,H,SiMe2, is shown to be intrinsically porous, possessing discrete, zero-dimensional (0D) pores/microcavities of about 28 Å3. The incollapsible 0D pores of Me,H,SiMe2 have been exploited for the enclathration and room temperature (and higher) confinement of a wide range of small gases. Over 20 isostructural x(gas/guest)@Me,H,SiMe2 (x ≤ 1) clathrates (guest = H2O, N2, Ar, CH4, Kr, Xe, C2H4, C2H6, CH3F, CO2, H2S, CH3Cl, CH3OCH3, CH3Br, CH3SH, CH3CH2Cl, CH2Cl2, CH3I, CH3OH, BrCH2Cl, CH3CH2OH, CH3CN, CH3NO2, I2), and a propyne clathrate (CH3CCH@Me,H,SiMe2·2CHCl3), have been prepared and characterized, and their single crystal structures determined. Gas enclathration is found to be highly selective for gases that can be accommodated by the predefined, though slightly flexible 0D pore. The structure determinations provide valuable insight, at subangstrom resolution, into the factors that govern inclusion selectivity, gas accommodation, and the kinetic stability of the clathrates, which has been probed by thermal gravimetric analysis. The activation (emptying) of several clathrates (guest = H2O, N2, CO2, Kr, CH3F) is shown to occur in a single-crystal-to-single-crystal (SC → SC) fashion, often requiring elevated temperatures. Akin to open pore materials, water vapor and CO2 gas are shown to be taken up by single crystals of empty Me,H,SiMe2 at room temperature, but sorption rates are slow, occurring over weeks to months. Thus, Me,H,SiMe2 exhibits very low, but measurable, gas permeability, despite there being no obvious dynamic mechanism to facilitate gas uptake. The unusually slow exchange kinetics has allowed the rates of gas (water vapor and CO2) sorption to be quantified by single crystal X-ray diffraction. The data are well fit to a simple three-dimensional diffusion model. © 2016 American Chemical Society. Clathrate hydrate ; Sorption ; Gravimetric analysis ; Propyne ; Water vapor ; Kinetics ; Thermal stability ; Porous medium ; Crystal (programming language) ; Isostructural Mesoporous Materials and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics
Authors: Nadjat Chouat | Mohammed Hachemaoui | Boumediéne Bensafi | Merouane Hadjij | Rihab Si Ali | Fatiha Djafri
Source: Fuel (article, 2025-12-05)
Field: Chemistry — Inorganic Chemistry

Novel NH4Cl-assisted post-synthesis grafting of N-methyldiethanolamine on mesoporous MCM-41: Towards efficient hydrocarbon removal from real LNG wastewater Adsorption ; Mesoporous material ; Wastewater ; Chemisorption ; Hydrocarbon ; Industrial wastewater treatment ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis ; Carbon Dioxide Capture Technologies
Authors: Vittorio Cossu | Carmine Risi | Francesco Galleni | Marica Eboli | Alessandro Del Nevo | Nicola Forgione | Koji Morita
Source: Nuclear Fusion (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

A novel chemical reaction model for safety analysis of lithium–lead/water interaction in water cooled lithium lead breeding blankets using SIMMER code Abstract The water cooled lithium lead breeding blanket (BB) stands out as one of the most promising designs for future fusion reactor BBs. However, the significant interaction between water and lithium–lead, particularly triggered by an In-Box LOCA (Loss of Coolant Accident), presents a critical safety concern. This concern has spurred the scientific community to develop a sophisticated numerical analysis tool capable of simulating such a complex interaction. The SIMMER code family from Japan Atomic Energy Agency has emerged as the most suitable tool for conducting safety analysis evaluations to aid in the design of the WCLL-BB at a system level. Extensive efforts have been devoted in recent years to enhancing the accuracy of simulations for such accidents. Specifically, the SIMMER code was modified by UNIPI to accurately model the chemical interaction between water and lithium–lead. However, further refinement of the chemical model is deemed necessary for an accurate representation of the interaction between these two fluids. This refinement involves modeling lithium–lead as a liquid alloy and monitoring the concentration of the chemically active element, namely lithium. The modification has been implemented using a feature of SIMMER capable of distinguishing between Fertile and Fissile components of liquid fuel. Moreover, an initial endeavor to introduce a chemical reaction kinetic rate controlled by diffusion has been undertaken within SIMMER, drawing on the analogy between Heat Transfer and Mass Transfer. This work shows the verification of the new chemical model against the stoichiometry and the results of a SIMMER-III simulation using the new implemented diffusion model, highlighting the importance of developing the chemical kinetic for the safety evaluation of an In-Box LOCA. Coolant ; Blanket ; Fissile material ; Diffusion ; Inherent safety ; Chemical process ; Code (set theory) ; Fusion power Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Feng Zhang | Zhanpeng Zhou | Dasheng Zhu | Guo Li | Mingjun Li | Aijun Zhang
Source: JOM (article, 2025-05-22)
Field: Materials Science — Polymers and Plastics

Influence of Graphene on the Sealing Performance of Nitrile Butadiene Rubber in CH3OH, C2H5OH and H2O Using Molecular Dynamics Simulation Nitrile ; Molecular dynamics ; Graphene ; Natural rubber ; Nitrile rubber ; Materials science ; Dynamics (music) ; Composite material ; Chemical engineering ; Computational chemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Physics ; Engineering Polymer Nanocomposites and Properties ; Tribology and Wear Analysis ; Silicone and Siloxane Chemistry
Authors: Shotaro Yamada | Gaku Kurogi | Keita Ebine | Tomoyuki Ogawa | Yen‐Yu Lin | Kosuke Ino | Shan Liu | Chen-Zhong Li | Hitoshi Shiku | Hiroya Abe
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Electrochemistry

Analysis for Diffusion of Redox Species in 3-Dimensional Cultured Cells Using Microelectrodes Microelectrode ; Redox ; Diffusion ; Chemistry ; Biophysics ; Nanotechnology ; Materials science ; Biology ; Electrode ; Inorganic chemistry ; Physics ; Physical chemistry ; Thermodynamics Electrochemical Analysis and Applications ; Neuroscience and Neural Engineering