jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fakhrul H. Bhuiyan | Álvaro Vázquez Mayagoitia
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Graph Neural Network Approaches for Predicting Redox Potential of Transition Metal Complexes The redox potential (E⁰) is a fundamental property governing the utility of iron-based transition metal complexes (TMCs) in critical applications like electrocatalysis and energy storage. Accurately predicting E⁰ is therefore key to designing novel complexes, but traditional computational or experimental methods can be resource-intensive. To accelerate the design of TMCs with desired electrochemical behavior, we present a machine learning framework based on Graph Neural Networks (GNNs) for predicting E⁰. We curated a dataset of iron-containing TMCs by filtering the tmQM dataset (J. Chem. Inf. Model. 2020, 60, 12, 6135–6146) and computed their redox potentials. These computed potentials ranged from approximately -2 eV to +2 eV. Next, we developed an efficient data processing pipeline to construct detailed graph representations of the TMCs, encoding molecular structure and bonding information directly from the tmQM dataset. Using these graphs, we trained multiple distinct GNN models, such as Graph Convolutional Network (GCN), Graph Attention Network (GAT), and SchNet, to perform regression on the calculated E⁰ values. The GCN and GAT models yielded comparable and robust predictive performance, proving to be more effective than SchNet. Furthermore, GNN-based classifier models were developed to predict whether a complex exhibits a positive or negative redox potential. Classifier models based on GCN and GAT architectures demonstrated strong predictive power for determining the redox character sign. Overall, this work demonstrates an efficient GNN-based workflow for predicting redox potentials of iron complexes, thus offering a valuable tool for high-throughput screening and rational design in electrochemistry. The workflow is generalizable and can easily be extended to any type of TMCs available in the tmQM dataset. Graph ; Classifier (UML) ; Workflow ; Redox ; Artificial neural network ; Convolutional neural network ; Predictive modelling ; Predictive power Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xuan Li | Qiyue Zhao | Luyang Liu
Source:  (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Research on corrosion monitoring technology of carbon steel under marine atmospheric environment based on corrosion big data This study investigates the corrosion behavior of Q345B carbon steel and its LH-13 anti-corrosion coating in a marine atmospheric environment, while also employing machine learning techniques to predict long-term corrosion conditions. The two-channel corrosion monitoring system was developed, utilizing an MSP320 microcontroller as the central controller. Sensors were crafted from Q345B carbon steel coated with LH-13 anti-corrosion material. These sensors underwent natural environmental testing at Hainan's Wenchang test station to measure the corrosion current of both the Q345B carbon steel and its LH-13 coating, alongside environmental parameters like temperature and humidity. The application of machine learning techniques facilitated the development of robust corrosion prediction models leveraging empirical corrosion data. Analysis revealed that the cyclic wet-dry conditions on the sensor surface exacerbated the corrosion of Q345B carbon steel. Employing the random forest algorithm, an atmospheric corrosion prediction model for Q345B steel was constructed, exhibiting exceptional predictive performance with an R-squared value exceeding 0.9. Corrosion ; Carbon steel ; Corrosion monitoring ; Carbon fibers ; Coating Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Concrete Corrosion and Durability
Authors: Mati Ullah Khan | Limin Zhen | Jiaqi Tian | A.. Farid | Zhiwei Chen | Hongliang Liu
Source: Trends in Food Science & Technology (article, 2025-12-16)
Field: Chemistry — Analytical Chemistry

From detection to prevention: A cyber-secure AI ecosystem for mitigating milk adulteration and dairy fraud Resilience (materials science) ; Supply chain ; Big data ; Anomaly detection ; Analytics ; Backdoor ; Cloud computing ; Predictive analytics ; Adulterant ; Audit Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Identification and Quantification in Food
Authors: V.S. KONYSHEV | A.D. LIFANOV | K.YU. NIKITINA
Source: Herald of Technological University (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

APPLICATION OF MACHINE LEARNING METHODS TO PREDICT THE FLASH POINT OF ORGANIC COMPOUNDS The increase in safety requirements for industrial entities has led to a number of technological changes in the direction of strengthening industrial safety measures. Currently, it is mandatory to develop a safety data sheet for production facilities, especially if chemical production is associated with the use of flammable liquids. At the same time, the use of digital technologies is a priority in creating safe chemical technology systems. This area is part of Industry 4.0. In many countries, the flash point is a measure of the separation of liquids into flammable and flammable. Flash point data for organic compounds were taken from the PubChem database. Information on the flash point for 1,741 organic substances was included in the database for this work. To simplify the analysis of the representation of organic compounds, we used MACC keys, as they are among the best descriptors for predicting the properties of chemical compounds. These descriptors are created based on the shared keys of the substructure. In addition, the models were calculated using Morgan molecular prints, also known as circular prints with a radius of 2. Within the framework of this work, ridge regression, the random forest algorithm, the kNN nearest neighbor method, the support vector machine (SVM) method, and artificial neural networks were implemented. For the training sample, the resulting classification model of a random forest showed an error-free classification, the prediction error for it is 0. The statistical characteristics of the constructed RF model for the sample have the following values: accuracy ACC = 0.83, sensitivity SPC = 0.81, specificity SEN = 0.86, balanced accuracy BA = 0.83, Matthews correlation coefficient MCC = 0.72. On the basis of the developed model, a forecast was made for the belonging of compounds to a certain class ("1" class combustible or "0" flammable) for organic compounds for which there is no experimental information regarding belonging to class "1" or "0". Flash point ; Flash (photography) ; Point (geometry) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Mathematics ; Organic chemistry ; Physics ; Optics Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: L. Stoch
Source: Applied Sciences (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

An Interpretable XGBoost Framework for Predicting Oxide Glass Density Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reveal underlying physical principles. Using a dataset of 76,593 oxide glasses from the SciGlass database, three machine learning (ML) models (ElasticNet, XGBoost, MLP) were trained and evaluated. Four distinct feature sets were constructed with increasing physical complexity, ranging from simple elemental composition to the advanced Magpie descriptors. The best model was further analyzed for interpretability using feature importance and SHapley Additive exPlanations (SHAP) analysis. A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R2) of 0.97. Interpretability analysis revealed that the model’s predictions were overwhelmingly driven by physical attributes, with mean atomic weight being the most influential predictor. The model learns to approximate the fundamental density equation using mean atomic weight as a proxy for molar mass and electronic structure features to estimate molar volume. This demonstrates that a data-driven approach can function as a scientifically valid and interpretable tool, accelerating the discovery of new materials. Interpretability ; Feature (linguistics) ; Sophistication ; Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Pattern recognition (psychology) Machine Learning in Materials Science ; Pigment Synthesis and Properties ; Glass properties and applications
Authors: Eva Seknazi | Elad Yoshai | Angela Kravstov | Rotem Mor Yosef | Arie Levant
Source:  (article, 2025-02-21)
Field: Materials Science — Surfaces, Coatings and Films

Foundation deep learning model for accurate SEM image segmentation for CD-SEM measurements As an inline metrology tool, CD-SEM (Critical Dimension Scanning Electron Microscope) needs to provide accurate segmentation of SEM images to ensure precise measurement of semiconductor layers. The segmentation task in CD-SEM typically requires manual tuning of parameters, which can be time-consuming and cumbersome. Additionally, the segmentation process heavily relies on information from recipe creation. In the case of process variation during runtime, the existing parameters and information may not be adapted, leading to segmentation failures. To overcome these limitations, we propose a deep learning-based segmentation algorithm. Our algorithm is trained on a large and broad dataset, making it a foundation model that can segment any new layer and does not require onsite training. The trained model possesses the ability to segment any type of layer without parameter tuning and can effectively handle process variation. For challenging cases, the model can be retrained, reducing time to market compared to entering a new development cycle as for classical algorithms. Our algorithm incorporates advanced techniques, including innovative loss functions and applicative augmentations, which effectively mimic real-world variations. It performs well under diverse SEM imaging conditions, even in scenarios with low signal-to-noise ratio (SNR), ensuring accurate identification and segmentation of nanoscale critical features. Here, we showcase the capabilities of our foundation deep learning model algorithm for segmentation of CD-SEM images. Foundation (evidence) ; Image segmentation ; Artificial intelligence ; Segmentation ; Deep learning ; Image (mathematics) ; Computer science ; Computer vision Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Xin Zhong | Yifan Li | T. John
Source:  (preprint, 2025-03-14)
Field: Materials Science — Materials Chemistry

Prediction of phase diagram using machine learning interatomic potential and implication for equilibrium under nonhydrostatic stress The stable pressure-temperature (P-T) ranges of metamorphic minerals are crucial for the reconstruction of geological history. Conventionally, phase diagrams were constructed using thermodynamic databases fitted to experimental measurements of e.g. heat capacity, elasticity, and volume. Kinetic processes such as phase transition and chemical diffusion cannot be directly accessed without knowing the corresponding parameters such as reaction energy barrier and diffusivity. In this work, we developed a machine learning interatomic potential trained with density functional theory (DFT) calculation for metamorphic minerals within Mg-Al-Si-O system. Molecular dynamics simulations were performed and combined with thermodynamic integration to obtain the free energy of a series of metamorphic minerals at high P-T conditions. The resulting phase relations match reasonably well with experimental data. We show that the aluminosilicate system is challenging due to the tiny energy difference among kyanite, andalusite and sillimanite. The coexistence P-T point for the three polymorphs is strongly dependent on the used exchange-correlation functionals. Silica system shows less dependency on different functionals and the complex polymorphs can be predicted with a good accuracy. Alpha-beta quartz transition is directly simulated using molecular dynamics without thermodynamic integration technique due to its low activation energy barrier. The developed interatomic potential has many potential usages, one being tested is the effect of nonhydrostatic stress on phase equilibrium. Preliminary result on alpha-beta quartz transition shows that the transition is mainly controlled by the mean stress, i.e. pressure. Under high differential stress up to 2 GPa, the transition pressure is shifted by only a few kbar. The finding has petrological implications on e.g. phase transition under confined environment such as mineral inclusion, or phase transition within shear zone under nonhydrostatic stress. More work will be focused on the nonhydrostatic stress effect on other minerals, the equation of state at high P-T conditions, and the effect on nuclear quantum effect on phase transition at lower temperature regime. Phase diagram ; Phase equilibrium ; Stress (linguistics) ; Diagram ; Interatomic potential ; Phase (matter) ; Thermodynamics ; Statistical physics ; Computer science ; Physics ; Philosophy ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science
Source: RAD Conference Proceedings. (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

ADVANCED DIGITAL IMAGE PROCESSING FOR MATERIAL EVALUATION IN NUCLEAR APPLICATIONS The integration of digital image processing (DIP), neural networks, and artificial intelligence (AI) is transforming materials science by enabling automated, high-resolution analysis of microscopic structures and supporting applications across nuclear research. At Research Centre Řež (CVR), DIP and AI have improved efficiency and reproducibility in workflows such as the classification of microstructural precipitates—both manufacturing- related secondary phase particles and radiation-induced precipitates—whose morphology and distribution affect key mechanical properties. These methods are also applied to the structural assessment of biological shielding concrete, where DIP detects and quantifies crack development, complementing traditional non-destructive testing. In fuel inspections and post-irradiation examinations, DIP enhances video analysis and microscopy, reducing manual bias while ensuring consistent evaluation of cladding and structural components. Overall, DIP technologies developed at CVR strengthen long-term operation (LTO) strategies in nuclear energy and offer versatile solutions for other sectors requiring precise microstructural or surface characterization. Workflow ; Artificial neural network ; Cladding (metalworking) ; Image processing ; Electromagnetic shielding ; Key (lock) ; Digital image processing Radiation Shielding Materials Analysis ; Erosion and Abrasive Machining ; Advanced X-ray and CT Imaging
Authors: Yanming Wang | Dalin Li | Xiaoyu Chen | Xiangyu Zou | Ruijie Liu
Source: Energy (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Transient multi-field catalytic mechanism of derived CO in deep energy extraction and optimization of synergistic conversion efficiency Catalysis ; Transient (computer programming) ; Extraction (chemistry) ; Field (mathematics) ; Energy transformation ; Mechanism (biology) ; Energy (signal processing) ; Process engineering ; Chemistry ; Materials science ; Chemical engineering ; Computer science ; Engineering ; Chromatography ; Physics ; Thermodynamics ; Organic chemistry ; Mathematics Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Eman A. Mohamed | Angelina Sarapulova | Hossam F. Nassar | Ahmed G. El‐Deen
Source: Egyptian Journal of Chemistry (article, 2025-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Free-standing 3D Co3O4/CuCo-based Nanocomposite Electrodes for Boosting Hybrid Supercapacitors: A Combined Experimental and Machine Learning Insights Developing a rational design of binder-free 3D nanocomposites is critical for substantially enhancing the performance of hybrid supercapacitors. In this study, Co₃O₄/CuCo-LDH, Co₃O₄/CuCo₂O₄, and Co₃O₄/CuCo₂S₄ hybrid nanocomposites were synthesized and directly grown on nickel foam via multiple hydrothermal processes. The synthesized hybrid electrodes exhibit abundant active sites and enhanced ion/electron diffusion, leading to superior electrochemical performance. The study is characterized by the fact that the metal impurities and metal used are constant to study the effect of functional groups on the composites. Particularly, Co₃O₄/CuCo-LDH and Co₃O₄/CuCo₂O₄ revealed excellent specific capacitances of 1558 F g⁻¹ (701 C g⁻¹ at 1 A g⁻¹) and 753.2 F g⁻¹ (376.6 C g⁻¹ at 1 A g⁻¹), respectively. Notably, Co₃O₄/CuCo₂S₄ achieved the highest specific capacitance of 2582.2 F g⁻¹ at 1 A g⁻¹. All of the fabricated materials combine and test in a full HSC device, and the Co₃O₄/CuCo₂S₄//CNF device achieves an impressive energy density of 84.05 Wh.kg-1, outperforming all other constructed devices at a power density of 390 W.kg-1. In artificial intelligence, the experimental galvanostatic charging-discharging (GCD) dataset is optimized to successfully predict capacitance and enhance energy optimization in AI-powered settings using numerous machine learning (ML) models. Six different ML models were applied: Least Absolute Shrinkage and Selection Operator (LASSO), k-nearest neighbors (KNN) regression, Extra Trees (ETs), Random Forests (RFs), and double models of deep neural networks (DNN). These models enable optimizing systems that will improve performance and maximize system utilization. The Shapley plot indicates that both time and current density can strongly affect each other positively and negatively, respectively. Nevertheless, it found that setting the maximum potential has a significant, though moderate, effect on prediction. Capacitance ; Boosting (machine learning) ; Artificial neural network ; Nanocomposite ; Power density ; Electrode ; Characterization (materials science) Supercapacitor Materials and Fabrication ; Nanoporous metals and alloys ; Nanomaterials for catalytic reactions
Authors: Ruiqing Sun (15341836) | Yujuan Xie (8107013) | Xinpeng Zhou (10146290) | Qilong Zhan (22767563) | Haoran Fan (5104511) | Xiaorong Sun (445026) | Dongsheng Bai (4331347) | Yangyang Wen (2867615) | Hongyan Li (25306) | Jing Wang (6206297) | Baoguo Sun (1639966)
Source: Figshare (article, 2025-12-03)
Field: Chemistry — Spectroscopy

Machine Learning-AssistedRatiometric FluorescenceElectrospun Nanofiber Films for Portable and Intelligent Monitoringof Multiple Alkylresorcinol Homologues in Whole Wheat Foods The intelligent authentication of whole wheat products remains a significant challenge due to the difficulty in simultaneously monitoring multiple alkylresorcinol (AR) homologues within complex food matrices. To address this, we have developed a novel sensing platform integrating machine learning (ML) algorithms with advanced ratiometric fluorescence (FL) materials. The core component is a dual-emission g-C<sub>3</sub>N<sub>4</sub>/Ru, leveraging the blue fluorescence from g-C<sub>3</sub>N<sub>4</sub> nanosheets as the analytical signal and the red fluorescence from [Ru­(bpy)<sub>3</sub>]<sup>2+</sup> as an internal reference. Upon interaction with AR homologues, a visible color change from blue-violet to pink occured due to multiple synergistic effects of IFE, a-PET, electrostatic attraction, and π-π interactions. The system exhibited exceptional sensitivity for quantitative detection of five AR homologues (C17:0, C19:0, C21:0, C23:0, C25:0) within the concentration range of 0–60 μg·mL<sup>–1</sup>, achieving ultralow limit of detections (LODs) ranging from 2.1 to 9.9 ng·mL<sup>–1</sup>. For precise and portable quantitative analysis, a random forest-back-propagation neural network (RF-BPNN) algorithm-assisted FL electrospun film (RuCN PAN NFs) was employed, enabling reliable on-site monitoring. RGB features were extracted using the OpenCV library, and 252 samples were evenly divided through stratified sampling to maintain data balance, yielding exceptional prediction accuracy (<i>R</i><sup>2</sup> = 0.9822) and robust performance by training with the RF-BPNN. Application to commercial wheat samples confirms the system’s utility for AR detection and whole wheat authentication. This integrated approach overcomes traditional analytical limitations by combining ML algorithms with advanced materials, enabling intelligent, rapid, and on-site detection of AR homologues. It provides a promising tool for verifying the authenticity of whole wheat products, offering significant potential for food safety monitoring and quality control applications. RGB color model ; Fluorescence ; Detection limit ; SIGNAL (programming language) ; Sampling (signal processing) ; Artificial neural network ; Sensitivity (control systems) Molecular Sensors and Ion Detection ; Carbon and Quantum Dots Applications ; Spectroscopy and Chemometric Analyses
Authors: Yuan Li | Jiayu Lu | Y.Z. Jia | Zitao Guo | Ruichang Gao
Source: Foods (article, 2025-12-16)
Field: Materials Science — Biomaterials

Preparation and Characterization of Eel (Anguilla) Bone Collagen Based on Intelligent Algorithm Eel (<i>Anguilla</i>) is an aquatic animal with high nutritional value and multiple health benefits for the human body. To fully utilize its processing by-products fish bone, this study optimized the enzymatic preparation process of using BP neural network and GA genetic algorithm, with collagen extraction yield as the key evaluation metric, and characterized the properties of the obtained collagen. The results demonstrated that the optimal extraction conditions for eel bone collagen were as follows: enzyme dosage of 2%, hydrolysis time of 2.65 h, solid-to-liquid ratio of 1:22, and ultrasonic pretreatment for 21 min at 250 W power, achieving an extraction yield of 57.6%. The main amino acids identified were glycine, glutamic acid, proline, and arginine. SDS-PAGE electrophoresis revealed that eel bone collagen exhibited structural characteristics of type I collagen. Raman spectroscopy and X-ray diffraction indicated an intact triple-helix structure with partial ordered features. The DSC and TGA results demonstrated good thermal stability, with a denaturation temperature of 106.73 °C. SEM imaging displayed a loose, porous fibrous network structure, while rheological analysis suggested potential biomedical material properties. The findings of this study provide fundamental data for the high-value utilization and development of eel bone resources. Extraction (chemistry) ; Yield (engineering) ; Characterization (materials science) ; Enzymatic hydrolysis ; Type I collagen ; Denaturation (fissile materials) ; Raman spectroscopy Collagen: Extraction and Characterization ; Protein Hydrolysis and Bioactive Peptides ; Seaweed-derived Bioactive Compounds