jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fenghong Cao | Xiaoman Chen | Ailan Huang | Jing Hu | Dayu Yang
Source: International Food Research Journal (article, 2025-04-01)
Field: Chemistry — Analytical Chemistry

Optimisation of ultrasonic-assisted enzyme extraction to analyse total flavonoids and antioxidant activity of purple potato using response surface and artificial neural networks model The present work utilised purple potatoes as the raw material to perform response surface methodology (RSM) and an artificial neural network (ANN) model. The objectives of the present work were to enhance the efficiency of ultrasound-assisted enzymatic extraction of total flavonoids from purple potatoes, and evaluate their antioxidant activity. The results demonstrated that the ANN model achieved a higher predictive accuracy, with a correlation coefficient of 0.99553 than the RSM model (R2 = 0.9919). The optimal extraction process conditions were the addition of 51.34 U/mL enzyme, extraction duration of 36.21 min, and extraction temperature of 53.12°C. The total flavonoid yield was 9.81 mg/g under these conditions, suggesting higher prediction ability of ANN. The scavenging rates of OH and DPPH(2,2-diphenyl-1-picrylhydrazyl) were 81.6 and 61.8%, respectively, for the purple potato extract concentration of 0.24 mg/mL. The present work proposes a novel approach integrating ANN with ultrasonic-assisted enzymatic extraction to predict and optimise flavonoid yields, demonstrating superior accuracy over traditional methods. The findings advance the extraction of bioactive compounds, and highlight ANN's potential for modelling complex non-linear relationships in food science. Response surface methodology ; Extraction (chemistry) ; Flavonoid ; Correlation coefficient ; Raw material ; Yield (engineering) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities
Authors: Vasiliy S. Krasnikov | K.D. Manukhina | Fanil T. Latypov | D.S. Voronin | Alexander E. Mayer
Source: International Journal of Plasticity (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Mechanical response of metastable grain boundaries under shear deformation: A multi-scale study Misorientation ; Grain boundary ; Deformation mechanism ; Dislocation ; Plasticity ; Grain boundary strengthening ; Grain Boundary Sliding ; Nanocrystalline material ; Critical resolved shear stress Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Microstructure and Mechanical Properties of Steels
Authors: M. Sornalakshmi | M Sakthimohan | Geeta Rani | K. Karthigadevi | S Tamil Selvan | Vaibhav Kumar
Source:  (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

An Automated Microstructural Analysis of SEM Images for Multiple Domain Sets Using Improved CURE (i-CURE) Algorithm in Machine Learning In real time materials plays a vital role in all fields. Scanning Electron Microscopic (SEM) images are used for imaging the materials and it provides microstructural information. Scientifically the material strength is analyzed only by microstructural and it helps to understand the applicability range. The demand of good and quality material is increasing day by day. Mostly microscopic images give the result as a qualitative analysis of its material which is not in the form of quantitative. Currently most of the reported work says SEM image analysis was done physically so it takes so many days to complete the process and the result is qualitative. So the material need quantitative analysis which is done only by microstructure. The SEM microstructural analysis helps to predict the materials behavior and strength. Materials behavior cannot be understood without characterizing microstructure because of its heterogeneity characteristics. In proposed work developed an automated tool with CURE (Clustering Using Representatives) algorithm in machine learning is to represent statistical analysis of microstructure predict the performance of the materials. There are different microstructural features in terms of size, shapes, position, or orientation. The proposed automated microstructural image analysis methods give the result in statistical/quantitative so that user/experts can predict the result accurately. The different statistical analysis was analyzed like black particle extraction, histogram, scatterplot, 1D gaussian distribution. The quantitative analysis has made a great solution in finding material or tensile strength of material behavior. Microstructure ; Process (computing) ; Domain (mathematical analysis) ; Statistical analysis ; Work (physics) ; Ultimate tensile strength ; Gaussian ; Quality (philosophy) Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Metallurgy and Material Forming
Authors: Dmitry Potapov | Sergei Rogovoi | Kuzma Khrabrov | Konstantin Ushenin | А. Н. Коровин | Anton Ber | Artur Kadurin | Artem Tsypin
Source: Research Square (preprint, 2025-11-02)
Field: Materials Science — Materials Chemistry

nablaColors: A 3D Benchmark for Optical Property Prediction with Solvent-aware Graph Neural Networks Benchmark (surveying) ; Artificial neural network ; Property (philosophy) ; Fidelity ; Graph ; Invariant (physics) ; Deep neural networks Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yinxin Yang | Jie Lü
Source: Crystal Research and Technology (review, 2025-07-02)
Field: Chemistry — Physical and Theoretical Chemistry

Cocrystal Technology in the Screening of Pharmaceutical Solid Forms and Chiral Resolution of Drugs: a Review Abstract Pharmaceutical cocrystal technology has gained significant attention for its potential to enhance drug performance, particularly by improving solubility and bioavailability. Various preparation methods, characterization techniques, and screening tools are developed, including Hansen Solubility Parameters (HSP), COSMO‐RS theory, Molecular Electrostatic Potentials (MEPs) for identifying hydrogen bond donors and acceptors, molecular dynamics simulations (MD), and machine learning (ML) techniques. These predictive tools facilitate virtual screening, expediting the discovery of promising cocrystals. In addition to solubility enhancement, cocrystal technology plays a crucial role in the drugs separation. Recent studies have explored enantiospecific and diastereomeric cocrystals, among other strategies, to improve the purity and efficacy of pharmaceutical compounds. These innovations offer valuable solutions for optimizing drug performance and further advancing the application and exploration of cocrystal technology in drug development. Cocrystal ; Resolution (logic) ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Computer science ; Organic chemistry ; Artificial intelligence ; Molecule Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Molecular spectroscopy and chirality
Authors: Pjotrs Žguns | Inga Pudza | Alexei Kuzmin
Source: Journal of Chemical Theory and Computation (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Benchmarking CHGNet Universal Machine Learning Interatomic Potential against DFT and EXAFS: The Case of Layered WS<sub>2</sub> and MoS<sub>2</sub> Universal machine learning interatomic potentials (uMLIPs) deliver near ab initio accuracy in energy and force calculations at a low computational cost, making them invaluable for materials modeling. Although uMLIPs are pretrained on vast ab initio data sets, rigorous validation remains essential for their ongoing adoption. In this study, we use the CHGNet uMLIP to model thermal disorder in isostructural layered 2H<sub><i>c</i></sub>-WS<sub>2</sub> and 2H<sub><i>c</i></sub>-MoS<sub>2</sub>, benchmarking it against ab initio data and extended X-ray absorption fine structure (EXAFS) spectra, which capture thermal variations in bond lengths and angles. Fine-tuning CHGNet with compound-specific ab initio (density functional theory (DFT)) data mitigates the systematic softening (i.e., force underestimation) typical of uMLIPs and simultaneously improves the alignment between molecular dynamics-derived and experimental EXAFS spectra. While fine-tuning with a single DFT structure is viable, using ∼100 structures is recommended to accurately reproduce EXAFS spectra and achieve DFT-level accuracy. Benchmarking the CHGNet uMLIP against both DFT and experimental EXAFS data reinforces confidence in its performance and provides guidance for determining optimal fine-tuning data set sizes. Extended X-ray absorption fine structure ; Density functional theory ; Ab initio ; Benchmarking ; Ab initio quantum chemistry methods ; Materials science ; Interatomic potential ; Bond length ; Computational chemistry ; Computer science ; Absorption spectroscopy ; Molecular dynamics ; Chemistry ; Physics ; Crystallography ; Molecule ; Quantum mechanics ; Crystal structure Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Talal M. Althagafi | Zeeshan Zubair | Muhammad Ramzan Saeed Ashraf Janjua | Beriham Basha | A. Badshah | M.S. Al-Buriahi
Source: Journal of Physics and Chemistry of Solids (article, 2025-11-24)
Field: Chemistry — Inorganic Chemistry

Machine learning-driven design of near-infrared absorbing dyes for high-performance photovoltaics Python (programming language) ; Random forest ; Absorption (acoustics) ; Maxima and minima ; Photovoltaics ; Maxima Pigment Synthesis and Properties ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Gonzalo García-Vidable | Nicolás Amigó | Francisco E. Palay | Rafael I. González | Franco Aquistapace | Eduardo M. Bringa
Source: Diamond and Related Materials (article, 2025-03-08)
Field: Materials Science — Materials Chemistry

Simulation of the mechanical properties of crystalline diamond nanoparticles with an amorphous carbon shell Nanoparticle ; Amorphous carbon ; Amorphous solid ; Shell (structure) ; Materials science ; Carbon fibers ; Diamond ; Nanotechnology ; Diamond-like carbon ; Carbon Nanoparticles ; Chemical engineering ; Composite material ; Chemistry ; Crystallography ; Composite number ; Thin film ; Engineering Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Advanced materials and composites
Authors: Fan Shen | Gui Chen | Mingjie Lin | Rong Luo | Jialiang Zhang | Changshui Chen
Source: Optics Communications (article, 2025-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and performance study of a full-bandwidth mid-wave infrared metasurface absorber based on deep learning optimization Optics ; Bandwidth (computing) ; Infrared ; Computer science ; Materials science ; Optoelectronics ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Yansheng Zhao | Nuo Li | J.Z. Bai | Xiang Xiao
Source: Journal of Food Composition and Analysis (article, 2025-09-24)
Field: Chemistry — Spectroscopy

Mass spectrometry imaging: Applications and advances in analyzing bioactive components in foods Mass spectrometry imaging ; Workflow ; Functional food ; Food products ; Systems biology ; Metabolomics ; Visualization ; Component (thermodynamics) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Jian-Kuen Wu | Weipeng Lu | J. Wu | Bing Zhang | Yanchuan Guo
Source: The Journal of Physical Chemistry A (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Graph Transformer Model Integrating Physical Features for Projected Electronic Density of States Prediction The prediction of the projected density of states (PDOS) in materials has traditionally relied on deep learning models based on graph convolutional networks (GCN) and Graph Attention Networks (GAT). In this study, utilizing PDOS data from the Materials Project, we demonstrate that the Graph Transformer (GT)─which employs multi-head scaled dot-product attention over each node and its local neighborhood─consistently outperforms both GAT and GCN models in prediction accuracy under identical energy-level-corrected conditions. Furthermore, by incorporating the valence electron counts of the s, p, d, and f orbitals for the entire structure as an additional feature into a GT-based model, we further enhance the model's predictive performance. The novel framework and physics-informed feature enhancements offer valuable insights into improving the prediction of PDOS and potentially other electronic properties. Computer science ; Transformer ; Graph ; Data mining ; Theoretical computer science ; Artificial intelligence ; Engineering ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Memory and Neural Computing
Authors: Betty Bowe Acquah | John‐Lewis Zinia Zaukuu
Source: Food Frontiers (article, 2025-06-06)
Field: Chemistry — Analytical Chemistry

A Critical Overview of Common Foodborne Toxicants and Methods for Their Reliable Detection ABSTRACT Food is an essential commodity for human health. However, toxicants such as mycotoxins, heavy metals, acrylamide, and pesticides can be present in food either naturally or through processing, packaging, or some anthropogenic activities. A thorough review of carefully selected studies revealed that climate change and pandemics have led to an increase in food intoxication that present both long‐term and short‐term toxicity with potentials of resulting in fertility issues, heart diseases, allergic reactions, and immune system risks. Consequently, regulatory bodies have set permissible limits for the ingestion of these toxicants. Therefore, analytical techniques are employed to detect and quantify these toxicants ensuring that they are within safe limits for consumption. This article critically outlines causes of emerging toxicants in food, their impact on consumer health as well as recent advances (novel approaches) in analytical methods and principles behind their selection, operation, and application for food toxicant determination. Chromatographic techniques, spectroscopy, biosensors, and vision systems are among the most used techniques for toxicant determination due to their sensitivity, but spectroscopy presents additional advantages of non‐invasive analysis with cost‐effective benefits. The use of algorithms, such as neural networks in emerging techniques such as vision systems and spectroscopy enhance data processing, results in improved outputs for different tasks. These analytical techniques play crucial roles in ensuring that food toxicant levels remain within permissible limits, thereby directly contributing to Sustainable Development Goal 3 (SDG 3) which is good health and well‐being. Biochemical engineering ; Environmental science ; Risk analysis (engineering) ; Computer science ; Business ; Engineering Dye analysis and toxicity ; Identification and Quantification in Food ; Pesticide Residue Analysis and Safety
Authors: Ivan Malashin | Dmitry Martysyuk | Vladimir Nelyub | А. С. Бородулин | Andrei Gantimurov | В С Тынченко
Source: Advanced Manufacturing Polymer & Composites Science (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites Machine learning approaches that integrate physical laws with data-driven models are transforming process optimization and quality assurance in polymer matrix composite manufacturing. This review synthesizes recent developments in neural metamodels for injection molding, spatio-temporal digital twins for resin infusion, and symbolic-regression surrogates for vacuum networks. Article identifies remaining challenges—such as extension to semicrystalline systems, uncertainty quantification under real-world noise, and deployment on industrial platforms—and outline strategies for addressing them. Building on these insights, a unified physics-informed surrogate concept is proposed that leverages temporal encoders, recurrent propagation, and multi-output decoders with embedded conservation constraints. This model is designed for rapid prediction of part quality metrics, cure state, flow front progression, and temperature fields, and supports gradient-based inversion for closed-loop control in advanced composite processing. Matrix (chemical analysis) ; Composite material ; Polymer ; Process (computing) ; Materials science ; Mechanical engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Manufacturing Process and Optimization
Authors: Ge Song | Hwanjo Chung | Reza Adhitama Putra Hernanda | Jung‐Hyun Lee | Hoonsoo Lee
Source: Chemosensors (article, 2025-04-25)
Field: Chemistry — Analytical Chemistry

Nondestructive Discrimination of Plant-Based Patty Containing Traditional Medicinal Roots Using Visible–Near-Infrared Hyperspectral Imaging and Machine Learning Techniques The interest in traditional meat being replaced by plant-based food has increased throughout the years. Some agricultural products, such as root crops, could be incorporated into alternative meat products due to the health benefits. However, relevant studies have discovered that some roots are considered allergen materials, necessitating further identification to maintain consumer safety. Aside from high accuracy, the limitations offered by traditional identification methods are a reason to employ nondestructive methods. This study aimed to develop a hyperspectral imaging system measuring the 400 nm to 1000 nm spectral range for the nondestructive identification of roots in soybean-based patty. Four thin-sliced traditional medicinal roots (tianma (Gastrodia elata), balloon flower root (Platycodon grandiflorum), deodeok (Codonopsis lanceolata), and ginseng (Panax ginseng)) were incorporated in a soybean-based patty with a concentration of 5% w/w. Moreover, support vector machine (SVM) learning and one-dimensional convolutional neural networks (1D-CNN) were realized for the discrimination model in tandem with spectral data extracted from the hyperspectral image. Our study demonstrated that SVM learning effectively discriminates between original patty and patty with root addition, with an F1-score, precision, and recall beyond 96.77%. This optimum model was achieved by using the standard normal variate (SNV) spectra. Hyperspectral imaging ; Infrared ; Artificial intelligence ; Nondestructive testing ; Plant roots ; Remote sensing ; Computer science ; Pattern recognition (psychology) ; Computer vision ; Optics ; Geology ; Biology ; Botany ; Physics Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Phytochemicals and Antioxidant Activities
Authors: Guilherme Fidelis Peixer | Urban Tomc | Andrej Kitanovski | Jaime A. Lozano | Jader R. Barbosa
Source: International Journal of Refrigeration (article, 2025-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-driven Monte Carlo uncertainty analysis of Curie temperature effects on active magnetic regenerator performance Magnetic refrigeration ; Monte Carlo method ; Curie temperature ; Cooling capacity ; Regenerative heat exchanger ; Current (fluid) ; Refrigeration ; Curie Magnetic and transport properties of perovskites and related materials ; Catalytic Processes in Materials Science ; Inertial Sensor and Navigation
Authors: Wenqian Chen | Di Fan | Weiyi Ren | H. Song | Yong Lu
Source: The Journal of Chemical Physics (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Structural, mechanical, and thermodynamic properties of solid BeF <b>2</b> at finite temperatures based on machine learning force field simulations As a primary component of molten salt reactor (MSR) fuel and coolant mixtures, BeF2 exhibits critical mechanical and thermodynamic behaviors at high temperatures that are essential for MSR design. This study systematically investigates the structural, mechanical, and thermodynamic properties of solid BeF2 at finite temperatures using first-principles calculations and machine learning force field molecular dynamics simulations. BeF2 is a wide-bandgap transparent compound with a bandgap of 10.4 eV, slightly lower than that of LiF. At low temperatures, BeF2 exhibits anisotropic and brittle elastic properties, with lower resistance to external pressure and shear compared to LiF, attributed to its lower symmetry and weaker bonding strength. As the temperature rises, the elastic moduli show enhanced isotropy and ductility. The Be-F bond demonstrates strong stability at high temperatures, revealed by the atomic vibration displacement distribution and the pair correlation function, while the Be-Be and F-F distributions become more dynamically averaged. Due to the tetrahedral structure and covalent bonding characteristics, BeF2 displays a significantly higher thermal expansion coefficient than the ionic crystal LiF. Furthermore, BeF2 has a broader phonon vibration frequency distribution compared to LiF, with a notable density of states peak in the high-frequency region of 20-28 THz. This provides additional contributions to the heat capacity in the elevated temperature range, making the isobaric heat capacity of BeF2 much higher than that of LiF. These findings deepen the understanding of the fundamental properties of BeF2 and provide theoretical insights for the design and optimization of MSR fuel mixtures. Thermal expansion ; Heat capacity ; Phonon ; Anisotropy ; Force field (fiction) ; Isotropy ; Isobaric process ; Intermolecular force ; Ionic bonding ; Antiferromagnetism Nuclear Materials and Properties ; Molten salt chemistry and electrochemical processes ; Nuclear reactor physics and engineering