jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A.G. Dmitrienko | V.V. Kikot | G.A. Koshkin | P.A. Maksimov | B.V. Tsypin
Source: SHILAP Revista de lepidopterología (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Minimizing the error of dynamic pressure measurement with piezoelectric sensors in autonomous devices Background. The study examines the temperature and time dependencies of the properties of piezoelements included in dynamic pressure piezosensors, with a focus on conditions close to real-world operation, such as in power plants operating at high pressures and temperatures. The purpose of this work is to analyze mechanisms behind measurement errors, including thermosensitivity, material aging, thermoelastic stresses, pyroelectric effects, and discrepancies in the coefficients of linear thermal expansion of structural materials. Materials and methods. A modified bismuth titanate, NFI-TV-3, which is used in hightemperature piezosensors, was selected as the primary object of study. Results. The results of field tests of NFI-TV-3 piezoelements under cyclic temperature and pressure exposure, as well as the temporal stability of their electrophysical characteristics, are presented. It is shown that the main changes in the piezomodule occur within the first five cycles of thermomechanical treatment, reaching up to 15 %, after which stabilization comparable to artificial aging processes is achieved. Aging coefficients were determined for the NFI-TV-3 material, making it possible to predict changes in characteristics over time. Conclusions. Based on the obtained data, a hybrid method for compensating temperature and temporal error is proposed, employing physical-mathematical modeling and elements of machine learning; this approach makes it possible to account for individual nonlinear effects, enhance measurement accuracy, increase the calibration interval, and reduce requirements for the amount of experimental data. The obtained results and the developed method have practical value for the creation of highly accurate and reliable piezosensors for power plants and autonomous devices. Thermoelastic damping ; Calibration ; Temperature measurement ; Nonlinear system ; Power (physics) ; Observational error ; Work (physics) ; Piezoelectricity ; Stability (learning theory) Ferroelectric and Piezoelectric Materials ; Innovative Energy Harvesting Technologies ; Ultrasonics and Acoustic Wave Propagation
Authors: Woonbae Sohn | Hionsuck Baik | Hyojung Kim
Source: Materials & Design (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Recent advances in AI-aided TEM image analysis: Emerging trends and applications • Artificial intelligence is transforming electron microscopy from a qualitative imaging tool into a quantitative platform for discovering structure–property relationships in complex materials. • Recent self-supervised and physics-guided learning strategies overcome data scarcity and poor transferability, enabling robust analysis of noisy experimental data across diverse material systems. • Unsupervised identification of atomic-scale motifs and domains introduces a new paradigm for linking microstructural heterogeneity directly to functional performance without manual annotation. • Machine learning–driven interpretation of high-dimensional diffraction data now allows direct mapping of strain, phase evolution, and internal fields that were previously inaccessible using conventional methods. Artificial intelligence (AI) is revolutionizing the analysis of (scanning) transmission electron microscopy ((S)TEM) data by enabling high-throughput, objective, and scalable interpretation of atomic-scale structures. Although initial AI applications often relied on large, labeled datasets and extensive training, exhibiting limited generalizability across diverse materials systems, recent advancements have introduced more efficient and physically meaningful approaches. However, challenges persist in achieving broad applicability and extracting interpretable insights from data-driven outputs. This concise review highlights emerging strategies that address these limitations, including unsupervised learning, physics-informed models, and domain-adaptive frameworks. By showcasing representative examples, we discuss how these approaches enhance the robustness, efficiency, and scientific relevance of AI-aided STEM analysis, ultimately advancing our capacity to uncover structure–property relationships in complex materials. Identification (biology) ; Generalizability theory ; Scalability ; Relevance (law) ; Interpretation (philosophy) ; Interpretability Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Wei‐Guo Yin
Source: Physical review. B./Physical review. B (preprint, 2026-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Site-decorated model for unconventional frustrated magnets: Ultranarrow phase crossover and two-dimensional spin reversal transition The site-decorated Ising model is introduced to advance the understanding and experimental realization of the recently discovered one-dimensional (1D) finite-temperature ultranarrow phase crossover in an external magnetic field, while mitigating the geometric complexities of traditional bond-decorated models. The unconventional frustration and physics are clarified by exactly mapping the 1D site-decorated Ising model in a magnetic field onto a zero-field bond-decorated $J_1$-$J_2$ Ising model with conventional geometrical frustration. Furthermore, although higher-dimensional Ising models in an external field remain unsolved exactly, an exact solution for a spin-reversal transition -- driven by an exotic, hidden half-ice, half-fire state induced by site decoration -- is derived. This transition, triggered by a slight variation in temperature or magnetic field -- without changing its direction -- even in the weak-field limit, offers a promising route toward energy-efficient applications such as data storage and processing. The results suggest that site decoration offers an avenue for materials and device design, particularly in systems such as mixed $d$-$f$ compounds, optical lattices, and neural networks, calling for further studies with site-decorated Heisenberg models. In addition, the site-decorated model offers a rigorous test ground for artificial intelligence (AI) in science, as the analytic derivation of the present results was not only validated but also improved by a general-purpose large language model, inspiring the use of AI as scientific discoverer. Crossover ; Magnet ; Condensed matter physics ; Frustration ; Phase transition ; Spin crossover ; Phase (matter) ; Spin (aerodynamics) ; Physics ; Computer science ; Quantum mechanics Magnetic Properties of Alloys ; Magnetic properties of thin films ; Advanced Condensed Matter Physics
Authors: Ranadip Kundu
Source: Solid State Communications (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Smart optimization of rare-earth-based perovskite solar cells using machine learning approaches Perovskite (structure) ; Photovoltaic system ; Non-blocking I/O ; Band gap ; Fabrication ; Absorption (acoustics) ; Doping ; Resistive touchscreen ; Energy conversion efficiency Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chemical and Physical Properties of Materials
Authors: Aniruddha Bora | Isabel K. Alvarez | Julie Chalfant | Chryssostomos Chryssostomidis
Source: Open MIND (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation In this work, we present a methodology using Physics Informed Neural Networks (PINNs) to determine the required velocity of a coolant, given inlet and outlet temperatures for a given heat flux in a multilayered metal-oxide-semiconductor field-effect transistor (MOSFET). MOSFETs are integral components of Power Electronic Building Blocks (PEBBs) and experiences the majority of the thermal load. Effective cooling of MOSFETs is therefore essential to prevent overheating and potential burnout. Determining the required velocity for the purpose of effective cooling is of importance but is an ill-posed inverse problem and difficult to solve using traditional methods. MOSFET consists of multiple layers with different thermal conductivities, including aluminum, pyrolytic graphite sheets (PGS), and stainless steel pipes containing flowing water. We propose an algorithm that employs sequential training of the MOSFET layers in PINNs. Mathematically, the sequential training method decouples the optimization of each layer by treating the parameters of other layers as constants during its training phase. This reduces the dimensionality of the optimization landscape, making it easier to find the global minimum for each layer's parameters and avoid poor local minima. Convergence of the PINNs solution to the analytical solution is theoretically analyzed. Finally we show the prediction of our proposed methodology to be in good agreement with experimental results. Coolant ; Artificial neural network ; Heat sink ; MOSFET ; Curse of dimensionality ; Overheating (electricity) ; Thermal ; Heat flux ; Inverse Thermal properties of materials ; Silicon Carbide Semiconductor Technologies ; Heat Transfer and Optimization
Authors: Xiaorong Gan | Weiwei Wu | R. An | Kangheng Huang | Laiba Khalid | Jianlin Zhang | Sunjia Xu | Yanjun Dang | Leilei Ye | Mengzhen Sun | Wanxi Xie | Ge Song | Jiayue Yu | C. Z. Li | Muhan Shi | Ting Chen | YANHUI AO
Source: ACS ES&T Engineering (article, 2026-01-22)
Field: Materials Science — Polymers and Plastics

Mechanistic Insights into N-Organic Electrochemical Probes for Heavy Metal Ions via DFT and Machine Learning Nitrogen-containing organic compounds are widely used in sensing, membrane separation, and adsorption for heavy metal ion detection and removal, yet their selective recognition mechanisms remain unclear. Here, we systematically investigated the interactions between representative nitrogen-containing compounds and various heavy metal ions. The results reveal that Cu2+ preferentially coordinates with nitrogen atoms rather than oxygen or sulfur, independent of external electric fields. Supervised machine learning (accuracy = 0.9449) identified three key descriptors including LUMO energy, hardness, and softness, governing selectivity based on the hard–soft acid–base principle and molecular orbital theory. Guided by these insights, an electrochemical probe derived from oxidized p-aminothiophenol was developed, exhibiting high Cu2+ selectivity with a linear detection range of 1 nM–100 μM and sensitivity of 28.157 μA/(nM·cm2), confirming the proposed mechanism. Selectivity ; Electrochemistry ; HOMO/LUMO ; Molecular descriptor ; Metal ions in aqueous solution ; Metal ; Adsorption ; Ion ; Molecular recognition Conducting polymers and applications ; Electrochemical Analysis and Applications ; Molecular Sensors and Ion Detection
Authors: Umut Songur | Sertuğ Fidan | Ezgi Alaca Yıldırım | Fatih Kahrıman | Ali Murat Tiryaki
Source: Sensors (article, 2026-01-25)
Field: Chemistry — Analytical Chemistry

Prediction of Total Anthocyanin Content in Single-Kernel Maize Using Spectral and Color Space Data Coupled with AutoML The non-destructive and chemical-free determination of anthocyanin content in single maize kernels is of great importance for plant-breeding programs. Previous studies have mainly relied on Near-Infrared Reflectance (NIR) spectroscopy and color-based approaches, often using conventional or randomly selected modeling techniques. In this study, an Automated Machine Learning (AutoML) framework was employed to predict anthocyanin content using spectral and digital image data obtained from individual maize kernels measured in two orientations (embryo-up and embryo-down). Forty colored maize genotypes representing diverse phenotypic characteristics were analyzed. Digital images were acquired in RGB, HSV, and LAB color spaces, together with NIR spectral data, from a total of 200 kernels. Reference anthocyanin content was determined using a colorimetric method. Ten datasets were constructed by combining different color space and spectral features and were grouped according to kernel orientation. AutoML was used to evaluate nine machine learning algorithms, while Partial Least Squares Regression (PLSR) served as a classical benchmark method, resulting in the development of 1918 predictive models. Kernel orientation had a notable effect on model performance and outlier detection. The best predictions were obtained from the RGB dataset for embryo-up kernels and from the combined RGB+HSV+LAB+NIR dataset for embryo-down kernels. Overall, AutoML outperformed conventional modeling by automatically identifying optimal algorithms for specific data structures, demonstrating its potential as an efficient screening tool for anthocyanin content at the single-kernel level. RGB color model ; Anthocyanin ; Content (measure theory) ; Kernel (algebra) ; Partial least squares regression ; Benchmark (surveying) ; Pattern recognition (psychology) ; Outlier Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Satish G. Prajapati | Prasanta Kumar Sinha
Source: Research Square (preprint, 2026-03-31)
Field: Chemistry — Physical and Theoretical Chemistry

Fractal-Memory Polymers: A New Universality Class with 𝝂 = 𝟏/𝟏. 𝟕 Exponent ; Renormalization group ; Scaling ; Monte Carlo method ; Entropy (arrow of time) ; Universality (dynamical systems) ; Fractal dimension Advanced Physical and Chemical Molecular Interactions ; Theoretical and Computational Physics ; Fractal and DNA sequence analysis
Authors: Omid Mirzaei | Ahmet Ilhan | Boran Sekeroglu
Source: Concurrency and Computation Practice and Experience (article, 2026-01-01)
Field: Materials Science — Biomaterials

Interpretable Machine Learning Framework for Predicting Spider Silk Toughness and Tensile Strength From Physicochemical and Genetic Features ABSTRACT Spider silk has exceptional mechanical properties, notably its high toughness, which is a measure of a material's ability to absorb energy before failure. Predicting toughness using physical, biochemical, and genetic features is a challenging task due to the nonlinear and multivariate interactions involved. This study presents a comprehensive machine learning framework to predict the toughness and the tensile strength of spider silk fibers using interpretable and high‐performing models. A curated dataset with varied physicochemical and structural features was used to train, tune, and evaluate multiple machine learning models, including Decision Tree, support vector machines, Random Forest, Gradient Boosting, and XGBoost. Feature engineering steps introduced domain‐specific constructs such as a toughness proxy and modulus transformations. Hyperparameter tuning was conducted via Bayesian optimization to enhance model performance. Among all tested models, the tuned XGBoost regressor achieved the highest predictive accuracy ( and 0.765), outperforming all other models. Feature importance analysis highlighted several key predictors among Young's modulus, aligning with known biological mechanisms for both toughness and tensile strength. This work demonstrates how machine learning can be used not only for accurate prediction but also to uncover the underlying determinants of silk toughness. The proposed framework sets the stage for data‐driven design of bioinspired synthetic fibers and represents a significant step toward the computational modeling of high‐performance biomaterials. Toughness ; Support vector machine ; Ultimate tensile strength ; Feature engineering ; Random forest ; Hyperparameter ; Genetic programming ; Feature (linguistics) Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Collagen: Extraction and Characterization
Authors: Zekiyos Abayneh Bochera | Solomon Gizaw | Clement Onime
Source: Data in Brief (article, 2026-03-07)
Field: Chemistry — Analytical Chemistry

Images dataset of pure and adulterated Teff (Eragrostis tef (Zucc.) Trotter) flour with wood flour (sawdust) and gypsum (calcium sulfate) powder for machine learning based identification of Teff flour adulteration and prediction of the level adulteration This data article presents an image dataset compiled for the purpose of machine learning-based identification and prediction of adulteration levels in Teff (Eragrostis tef (Zucc.) Trotter) flour. The dataset includes images of pure white, mixed, and red Teff flour varieties, as well as these flours adulterated with wood flour (sawdust) and gypsum (calcium sulfate) powder. Adulteration levels range from 10% to 40% in 5% increments. The data collection process involved preparing Teff flour from naturally dried and milled Teff grains. Samples of 100 grams of each Teff flour variety were then mixed with the adulterants at the specified concentrations. A total of 5,000 raw images were captured using an 18-megapixel Canon EOS 7D camera under controlled studio lighting (300 W incandescent lamps), with the samples placed 30 cm from the camera lens in a 10 cm x 10 cm plastic box. To enhance the dataset's diversity and quantity, 25,000 augmented images were generated by shuffling image pixels' locations with various block sizes (1 × 1, 2 × 2, 4 × 4, 8 × 8, and 16 × 16). This dataset is a valuable resource for researchers and students in Teff adulteration using image processing and feature extraction. It also holds potential for use by Food and Drug Administration Authorities and law enforcement to develop automated methods for detecting Teff flour adulteration, offering an alternative to time-consuming physio-chemical laboratory tests. The dataset's structure and augmentation methods are detailed to ensure reproducibility and encourage further research into robust machine learning models for food quality control. Gypsum ; Identification (biology) ; Wheat flour ; Predictive modelling Spectroscopy and Chemometric Analyses ; Food composition and properties ; Metabolomics and Mass Spectrometry Studies
Authors: Yucai Chen
Source:  (article, 2026-03-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Temperature-enhanced Steinmetz modeling and multifactor interaction analysis of high-frequency magnetic core loss To address the limited prediction accuracy of Steinmetz Equation (SE) under multi-temperature operating conditions, this study proposes an improved model that explicitly incorporates temperature effects, namely Steinmetz model (SET). On the basis of preserving the power-law structure and physical interpretability of SE, SET introduces an exponential temperature correction term to describe the nonlinear response of core loss to temperature variations, and PSO is employed to achieve global optimization of model parameters. Experimental results based on the MagNet dataset show that, under identical optimization conditions, SET achieves improvements of 58.72% in MAE and 26.83% in MAPE, with the coefficient of determination increasing to 0.9928, and exhibits enhanced robustness under high-loss and extreme operating conditions. Furthermore, by combining statistical analysis with interpretable machine learning methods, the dominant effects of excitation waveform and core material, as well as their significant interaction effects on core loss, are systematically revealed. Interpretability ; Robustness (evolution) ; Core (optical fiber) ; Nonlinear system ; Exponential function ; Particle swarm optimization ; Set (abstract data type) Magnetic Properties and Applications ; Magnetic properties of thin films ; Physics of Superconductivity and Magnetism
Authors: Yuanxia Fu
Source: Human and nation s health (article, 2026-03-22)
Field: Chemistry — Analytical Chemistry

HYPERSPECTRAL IDENTIFICATION OF COMPOSITE CHINESE SAUSAGES <ns0:p>With the increasing diversity of compound sausage types, the development of a rapid, accurate, and non-destructive identification model has become essential. The aim is to develop a robust model that operates without damaging the sausage surface or structure, meeting real-time, lossless, and high-throughput recognition requirements in practical applications. “Lossless recognition” refers to non-destructive analysis of surface spectra using hyperspectral imaging technology – without chemical treatment, grinding, or slicing into the interior of the sausage. Hyperspectral imaging technology was employed to collect spectral data over 400 – 1000 nm for eight types of compound sausages. For each image, 50 sampling regions were randomly selected, and the average reflectance values were extracted. First, three preprocessing algorithms – Multiplicative Scatter Correction (MSC), Savitzky-Golay smoothing (SG), and Neighborhood Averaging (NA) – were applied to the raw hyperspectral data. To enhance modeling efficiency, Principal Component Analysis (PCA) was used to reduce the dimensionality of the original 328 spectral bands, retaining the first 10 principal components, which explained over 95% of the total variance, as the new feature set. The classification results demonstrate that hyperspectral imaging combined with machine learning algorithms can effectively distinguish between the eight compound sausage types. Among all methods, the SVM model exhibited the highest classification accuracy, highlighting its excellent discriminative ability and robustness in high-dimensional hyperspectral data analysis. Classification models that combined MSC preprocessing with any of the three algorithms achieved prediction accuracies of over 99%.Received30.12.2025Accepted1.03.2026</ns0:p> Hyperspectral imaging ; Principal component analysis ; Pattern recognition (psychology) ; Preprocessor ; Discriminative model ; Smoothing ; Robustness (evolution) ; Support vector machine ; Dimensionality reduction Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Qiujie Zhao | Jun Xie | Wenshuai Wang | Xing Li | Pengpeng Shi
Source: Applied Mathematical Modelling (article, 2026-03-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of magnetoelectric effects of graded piezoelectric/piezomagnetic composite cylinders under parameter uncertainty: a machine learning approach for volume fraction design Volume fraction ; Coupling (piping) ; Computation ; Artificial neural network ; Optimal design ; Volume (thermodynamics) ; Reliability (semiconductor) ; Design of experiments ; Fraction (chemistry) ; Uncertainty quantification Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Composite Material Mechanics