jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tian Xu | Jing Bai | Xing Gong | Ruichao Lei | Yu Yan
Source: Computational Materials Science (article, 2025-08-12)
Field: Materials Science — Biomaterials

Leveraging causal discovery and causal inference to improve interpretability of Charpy impact toughness Interpretability ; Charpy impact test ; Causal inference ; Inference ; Toughness ; Causality (physics) ; Computer science ; Econometrics ; Artificial intelligence ; Materials science ; Mathematics ; Composite material ; Physics Calcium Carbonate Crystallization and Inhibition ; Offshore Engineering and Technologies ; Drilling and Well Engineering
Authors: Shixiang Tang | Haolun Yuan | Ruihan Wang | Dingguo Xu
Source: Separation and Purification Technology (article, 2025-06-04)
Field: Chemistry — Inorganic Chemistry

Accelerating the discovery of stable metal–organic frameworks for ethane/ethylene separation using data mining and multitarget screening Ethylene ; Separation (statistics) ; Metal-organic framework ; Chemistry ; Environmental science ; Computer science ; Organic chemistry ; Catalysis ; Machine learning ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Chinedu Christian Ahia | Hassan O. Shoyiga | Nicholas Rono | Stephen Okiemute Akpasi | Judith Letsoalo | Naledi Raleie | Lebogang Katata‐Seru | Sammy Lewis Kiambi | Omolola E. Fayemi | Edson L. Meyer
Source: Advances in Physics X (article, 2025-09-10)
Field: Materials Science — Polymers and Plastics

Carbon-based transition metal chalcogenides for wearable sensors, energy storage, and photodetector applications Transition metal chalcogenides (TMCs) have attracted a lot of scientific research interests due to their extraordinary properties. Several studies indicate that TMCs are suitable candidates for electrocatalytic processes, energy storage, and optoelectronic device applications. The concept of integrating carbon into TMCs with other materials and devices can lead to improved performance and enable innovative applications in different areas, while, simultaneously unlocking new functionalities. As a result of the increasing global need for energy production, which requires advanced solutions, the quest for high-performance energy storage systems with enhanced electrical conductivity, optical properties, and electrochemical stability has gained research interest over the years. This review focuses on recent advances in carbon-based TMCs for wearable sensors, energy storage, and photodetector applications. It also highlights the extension of deep learning techniques towards the investigation of processes whereby carbon materials can be integrated with TMCs. The present review explores the latest advancements in carbon-based transition metal chalcogenides and their applications. The study provides a fundamental understanding of the theoretical tools and methods that can be implemented towards optimizing material performance of carbon-based TMCs while at the same time investigating avenues for new applications and enhanced sustainable energy storage methodologies. Photodetector ; Wearable computer ; Energy storage ; Wearable technology ; Energy (signal processing) ; Sustainable energy ; Carbon fibers Conducting polymers and applications ; 2D Materials and Applications ; Electrochemical sensors and biosensors
Authors: Hange Wang | Hongyu Liu | Xiaolin Liu | Peng Lin | Jiang Wu | Jia Lin
Source: Solar Energy (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Active learning driven multi-property inverse design of spinel solar cells Spinel ; Inverse ; Property (philosophy) ; Materials science ; Computer science ; Inverse problem ; Process engineering ; Environmental science ; Mathematics ; Mathematical analysis ; Geometry ; Engineering ; Metallurgy Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Nanowire Synthesis and Applications
Authors: Guozheng Fan | Yu Jing | Thomas Frauenheim
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advancing band structure simulations of complex systems of C, Si and SiC: a machine learning driven density functional tight-binding approach We present a machine learning (ML) workflow for optimizing electronic band structures using density functional tight binding (DFTB) to replicate the results of costly hybrid functional calculations. The workflow is trained on carbon, silicon, and silicon carbide systems, encompassing bulk, slab, and defect geometries. Our method accurately reproduces hybrid functional results by applying a DFTB-ML scheme to train and predict the scaling parameters of two-center integrals and on-site energies, which is particularly accurate for electronic band structures near the Fermi energy. The DFTB-ML model demonstrates excellent scaling transferability, enabling training on smaller systems while maintaining hybrid functional-level accuracy when predicting larger systems. The high accuracy and adaptability of our model highlight its potential for precise band structure predictions across diverse chemical environments. Tight binding ; Scaling ; Workflow ; Density functional theory ; Hybrid functional ; Computer science ; Transferability ; Electronic structure ; Electronic band structure ; Silicon carbide ; Statistical physics ; Materials science ; Machine learning ; Physics ; Computational chemistry ; Condensed matter physics ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Tiren Peng | Zhikai Gao | Zhiguo Wang | Xi Sun | Hang Zhang | Yuhang Zhou | Zishan Luo | Zepeng Jia | Pei Song | Sen Lu | Hong Cui | Weizhi Tian | Feng Rong | Lingxia Jin | Hongkuan Yuan
Source: Journal of Material Science and Technology (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Geometric feature-based machine learning-assisted exploration of ort-M2B2 structures for room-temperature hydrogen storage Feature (linguistics) ; Hydrogen storage ; Artificial intelligence ; Computer science ; Hydrogen ; Materials science ; Pattern recognition (psychology) ; Chemistry ; Philosophy Hydrogen Storage and Materials ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Anna Katharina Picha | Sara Tkaczyk | Marcus Wieder | Stefan Boresch
Source: ChemRxiv (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

Architecture independent absolute solvation free energy calculations with neural network potentials Allowing atoms or molecules to disappear is a critical step in alchemical free energy simulations (FES). The necessary tricks are well understood when using force fields. Over the past few years, neural network potentials (NNPs) have seen rapid development. Their potentially higher accuracy compared to force fields makes them attractive for use in FES. Here, we outline a method for gradually decoupling atoms and molecules in systems that are fully described by NNPs. Specifically, we show that manipulating the neighbor list is equivalent to using soft-core potentials in force-field-based FES. Since constructing the neighbor list is a central step, regardless of the NNP's inner workings, our approach is agnostic to NNP architecture. We validate the correctness of our methodology by demonstrating cycle closure for a model problem and report solvation free energies obtained with the MACE-OFF23(S/M) NNP. Solvation ; Artificial neural network ; Absolute (philosophy) ; Energy (signal processing) ; Statistical physics ; Physics ; Computer science ; Artificial intelligence ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies
Authors: Yihang Feng | Yi Wang | A. G. Purohit | Jiayi Liang | Xinhao Wang | Mingyu Qiao | Zhenlei Xiao | Yangchao Luo
Source: Journal of Future Foods (article, 2025-05-01)
Field: Chemistry — Analytical Chemistry

Classification of Strawberry Maturity Using Swin Transformer V2: Integrating Near-Infrared Spectral, Chemical, and Optical Properties • Swin Transformer V2 achieves perfect accuracy in strawberry maturity assessment. • Novel multi-modal dataset combines visual, chemical, and spectral measurements. • Brix-to-acidity ratio effectively distinguishes three maturity stages. • Optimized NIR preprocessing reveals distinct maturity-based clustering. • Visual features strongly correlate with chemical indicators of fruit maturity. This study presents a comprehensive approach to automated strawberry maturity classification by integrating computer vision techniques with chemical and spectral analyses. We developed and evaluated three deep learning models—RegNet_Y_800MF, RegNet_Y_8GF, and Swin Transformer V2 Tiny—for classifying strawberry maturity using a novel dataset of 270 images from 90 strawberry samples. The Swin Transformer V2 Tiny achieved perfect classification accuracy (100%) on the test set, outperforming both RegNet architectures while maintaining a balanced computational footprint. Chemical analysis revealed that the Brix-to-acidity ratio provided the most distinct separation between maturity stages (4.92 ± 0.80 for immature, 6.18 ± 0.98 for semi-mature, and 8.12 ± 2.40 for mature fruits). Near-infrared spectral analysis, optimized through 1,220 preprocessing pipelines, achieved a Silhouette score of 0.5744 using localized standard normal variate with seven windows and flat-window smoothing filtering. Strong correlations were demonstrated between visual characteristics, spectral properties, and chemical indicators of maturity, validating the effectiveness of computer vision-based approaches for automated strawberry maturity assessment. This integrated approach advances the development of reliable and efficient fruit quality assessment systems for commercial applications. Maturity (psychological) ; Infrared ; Materials science ; Environmental science ; Optics ; Physics ; Psychology Spectroscopy and Chemometric Analyses ; Berry genetics and cultivation research ; Leaf Properties and Growth Measurement
Authors: Weijie Liao | Xiangyi Xue | Jinshan Li | Jiangkun Fan | Lingyun Song | Xuequn Shang | Turab Lookman | Ruihao Yuan
Source: Acta Materialia (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Mapping microstructure to mechanical property by disentangling strengthening mechanism with deep learning Microstructure ; Representation (politics) ; Deep learning ; Property (philosophy) ; Key (lock) ; Yield (engineering) ; Characterization (materials science) Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Non-Destructive Testing Techniques
Authors: Gabriel Braun | Jakler Nichele | Júlio César Duarte | Leonardo Santos de Brito Alves | Itamar Borges
Source: ACS Engineering Au (article, 2025-08-16)
Field: Materials Science — Materials Chemistry

Interpretable Machine-Learning Models for Predicting Shear Viscosity and Thermal Conductivity of Binary Fluid Mixtures Accurate prediction of fluid transport properties is essential and challenging, especially for non-ideal mixtures and under extreme conditions. In this work, we systematically evaluate several machine learning (ML) regression methods─including linear, nonlinear, and decision-tree-based ensemble models─to predict shear viscosity and thermal conductivity for two representative fluid mixtures, N2/O2 and CO2/H2S, in the gas, liquid, and supercritical thermodynamic regions. These mixtures were selected to analyze the influence of composition and intermolecular interaction strength on the predictive accuracy because the two mixtures have different types of intermolecular interactions. Tree-based ensemble models consistently yielded superior predictive accuracy, with the Light Gradient Boosting Machine (LightGBM) performing particularly well for both properties, achieving low root-mean-square error (RMSE) values across all phases. SHAP (SHapley Additive exPlanations) analysis provided interpretability, showing that composition strongly influenced predictions in the gas phase, while pressure and temperature significantly impacted predictions in liquid and supercritical phases. Specifically, higher pressures and lower temperatures required larger corrections due to enhanced molecular interactions and structural complexities, especially for the CO2/H2S mixture. This study demonstrates the capability of interpretable ML models to accurately predict transport properties and offer physically meaningful insights into fluid mixture behaviors. Thermal conductivity ; Binary number ; Viscosity ; Shear (geology) ; Thermodynamics ; Thermal ; Materials science ; Artificial intelligence ; Computer science ; Mechanics ; Physics ; Mathematics ; Composite material Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Market Dynamics and Volatility
Authors: Pramod K. Kalambate | Devaraj Manoj
Source: ECS Sensors Plus (article, 2025-12-01)
Field: Chemistry — Electrochemistry

Historical Evolution of Electrodes and Their Impact on Electrochemical Sensing and Biosensing This review outlines the historical development of electrodes and their importance in electrochemical sensing and biosensing. Electrode design and material choice directly influence sensitivity, selectivity, and applicability. Early systems such as mercury-based dropping mercury electrodes (DMEs) provided reproducible surfaces and broad potential windows, although their toxicity and environmental concerns restricted widespread use. The shift to solid electrodes including glassy carbon, carbon paste, and noble metals brought higher stability, conductivity, and simpler modification, which expanded sensing applications. Subsequent advances such as screen-printed and pencil graphite electrodes introduced low-cost, disposable formats that made electrochemical sensing more portable and accessible. More recently, flexible substrates, 3D-printed devices, and nanostructured materials have created opportunities for wearable technologies, real-time monitoring, and ultra-sensitive detection. Alongside these material innovations, this review examines current gaps related to scalability, commercialization, and sustainability, where translation from laboratory research to practical devices remains limited. The growing role of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) in optimizing electrode design, enabling large-scale data analysis, and supporting remote monitoring is also discussed. By combining historical insights with present challenges, this review outlines future directions toward reliable, safe, and widely accessible electrochemical sensing technologies. Electrode ; Wearable computer ; Chemically modified electrode ; Biosensor ; Electrochemistry ; Wearable technology Electrochemical Analysis and Applications ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Aike Xi | Qinyuan Jiang | Fei Wang | Siming Zhao | Kangkang Wang | Yonglu Zang | Yanlong Zhao | Ya Huang | Khaixien Leu | Rufan Zhang
Source: Nano Letters (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Identification and Analysis of Two Critical Structural Parameters Governing the Strength of Carbon Nanotube Fibers Despite significant advancements in theoretical and experimental research, the strength of state-of-the-art carbon nanotube fibers (CNTFs) still falls short of their theoretical limits. To bridge this gap, a detailed understanding of the structure-strength relationships of CNTFs is urgently needed to guide enhancement of the fiber strength. In this work, a mesoscale quantitative model employing coarse-grained molecular dynamics simulations was proposed to investigate the relationship between the conformation of CNTFs and fiber strength. Two structural features were identified to affect the overall fiber strength by machine learning: the length-pore ratio (α) and porosity (β). An in-depth analysis of various postprocessing techniques reveals that the objective of process optimization is to maximize α while simultaneously minimizing β. When these two factors are fine-tuned, it is possible to significantly enhance the mechanical performance of CNTFs, bringing their strength closer to the theoretical predictions. Carbon nanotube ; Identification (biology) ; Materials science ; Nanotechnology ; Nanotube ; Composite material ; Biology Carbon Nanotubes in Composites ; Graphene research and applications ; Smart Materials for Construction
Authors: Shuyi Lu | Qian Wu | Shanshan Wang | Dongru Sun | Yan Liu | Shaohua Huang | Biling Huang
Source: Analytical Chemistry (article, 2025-10-07)
Field: Chemistry — Spectroscopy

<sup>19</sup>F NMR-Guided Chiral Analysis and Machine Learning for Absolute Configuration Prediction of Carboxylic Acids <sup>19</sup>F-labeled molecular probes offer significant advantages in chiral analysis, with their stereostructures playing a critical role in enantiodiscrimination. Rapid and accurate assignment of absolute configurations is particularly important for the pharmaceutical field and asymmetric synthesis. This work presents a comparative evaluation of two <sup>19</sup>F-labeled probes as CDAs: aromatic <b>P</b><sub><b>1</b></sub> ((<i>S</i>)-1-(2-fluorophenyl)ethylamine) and aliphatic <b>P</b><sub><b>2</b></sub> (<i>trans</i>-2-fluorocyclohexanamine), focusing on their enantiomeric recognition of chiral carboxylic acids. The results demonstrate that <b>P</b><sub><b>1</b></sub> outperformed <b>P</b><sub><b>2</b></sub>, particularly in analyzing carboxylic acids with chiral centers positioned several carbons away from the carboxyl group or possessing multiple chiral centers. Density functional theory calculations elucidated the underlying mechanisms, revealing that <b>P</b><sub><b>1</b></sub>'s great recognition efficiency arises from a larger energy difference between diastereomers and the formation of hydrogen bonds between its fluorine atom and the analytes' protons. These findings emphasize the importance of probe-analyte interactions in CDA-based enantiorecognition systems for achieving high-resolution discrimination. Moreover, <b>P</b><sub><b>1</b></sub> exhibited practical applications in determining ee values and analyzing various brands of ibuprofen capsules and tablets, highlighting its potential for pharmaceutical quality control. Furthermore, we introduced a novel online platform combining a <b>P</b><sub><b>1</b></sub>-guided <sup>19</sup>F NMR approach with machine learning for automated prediction of absolute configurations. This work sheds light on the differences between aromatic and aliphatic probes in enantiorecognition, while introducing an online predictive tool, in favor of the design of <sup>19</sup>F molecular probes and the rapid assignment of absolute configurations. Diastereomer ; Enantiomer ; Absolute configuration ; Hydrogen bond ; Carboxylic acid ; Absolute (philosophy) ; Chiral auxiliary ; Molecular recognition ; Fluorine ; Molecular descriptor Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Shiqiang Wang | Huiyun Jiang | Jin Yan | Changkun Qiu | H R Wang | Yifan Song | Liang Zhu
Source: Journal of Hazardous Materials (article, 2025-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Trace-level discrimination and detection of polycyclic aromatic hydrocarbons via hybrid photonic-plasmonic SERS sensors integrated with machine learning algorithms Detection limit ; Support vector machine ; Field (mathematics) ; Analyte ; Ranging ; Local binary patterns ; Photonics ; Principal component analysis Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Ke Ma | Ting Liu | Chunguang Zhou | Yan‐Li Li | Xiaomeng Yu | Sheng‐Lin Qiao | Kezheng Chen
Source: Coordination Chemistry Reviews (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Catalytic Nanozyme: Unlocking new horizons in articular cartilage degeneration treatment Chemistry ; Degeneration (medical) ; Articular cartilage ; Cartilage ; Anatomy ; Osteoarthritis ; Pathology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanoplatforms for cancer theranostics
Authors: Ocean Monjur | Md. Toukir Ahmed | Md Wadud Ahmed | Mohammed Kamruzzaman
Source:  (article, 2025-06-11)
Field: Chemistry — Analytical Chemistry

Agro-Net: a Convolution-Attention Fusion Based Hyperspectral Model for Agro-Food Quality Assessment Hyperspectral imaging (HSI) has emerged as a groundbreaking technology for non-invasive and non-destructive food quality assessment, eliminating the limitations of traditional measurement systems. Simultaneously capturing spectral and spatial data, HSI provides detailed chemical and structural insights, enabling more precise quality evaluation. However, most existing research in the food domain primarily relies on average spectral data, failing to harness the full potential of the rich spatial and spectral information contained in hyperspectral images. To address this gap, this study introduces a novel Convolution-Attention Fusion model named “Agro-Net,” designed for hyperspectral image feature extraction to enhance the accuracy of food quality prediction. The superior performance of Agro-Net, compared to state-of-the-art machine learning models in classification and regression tasks, underscores the importance of utilizing both spatial and spectral data for more effective food quality assessment. Hyperspectral imaging ; Sensor fusion ; Pattern recognition (psychology) ; Spatial analysis ; Quality (philosophy) ; Quality assessment ; Fusion ; Feature extraction Spectroscopy and Chemometric Analyses
Authors: Jie Pan | Xin Qiao | Chunlei Zhang | Bin Li | Lun Li | Guomeng Li | Shaohua Qin
Source: Electronics (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane Plasma catalysis is capable of significantly enhancing the energy conversion efficiency of the CO2 reforming of methane. Simulation is an effective method for studying internal principles and operational mechanisms of the plasma-catalyzed CO2 reforming of methane. However, simulation has some potential problems such as poor convergence and high computational complexity. To address these challenges, a stacking ensemble learning-assisted simulation of the plasma-catalyzed CO2 reforming of methane was proposed. The stacking ensemble model, trained on limited converged simulation data, interpolates non-convergent points by leveraging the combined predictive power of multiple base models (KNN, DT, XGBoost). This approach ensures that predictions remain within the training data’s parameter space, minimizing extrapolation risks. We utilize Bayesian optimization and stacking ensemble methods aimed at improving the accuracy and generalization capability of this model. Experimental results show that this model can provide accurate CO density values under different E/N and CO2 gas-feeding ratio conditions. The comparative analysis results also demonstrate that Bayesian optimization and ensemble techniques can effectively improve model accuracy. This model combines advanced machine learning techniques with traditional simulation techniques. The time for predicting particle density under new experimental conditions has been reduced from 24 min in numerical simulation to a few seconds, which is 99.8% less than traditional 0D simulations, while maintaining high prediction accuracy (R2 = 0.9795). Stacking ; Methane ; Catalysis ; Plasma ; Materials science ; Chemical engineering ; Environmental science ; Computer science ; Chemistry ; Engineering ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Data Storage Technologies ; CO2 Reduction Techniques and Catalysts