jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md Inzamam Ul Haque | Ramakrishnan Kannan | Jacob Hinkle | Sylwia A. Stopka | Anton V. Ievlev | Nathalie Y.R. Agar | Olga S. Ovchinnikova | Debangshu Mukherjee
Source: Advanced Intelligent Discovery (article, 2025-06-18)
Field: Chemistry — Spectroscopy

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion Recent advancements in molecular Mass Spectrometry Imaging have sparked interest in integrating high spatial resolution methods with molecular mass‐spectrometry‐based chemical imaging. Fusion‐based algorithms have proven effective in generating high spatial‐resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry imaging techniques. By leveraging FT‐ICR's unparalleled spectral resolution and ToF‐SIMS's exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies. Mass spectrometry imaging ; Mass spectrometry ; Fourier transform ion cyclotron resonance ; Image resolution ; Computer science ; Spectral imaging ; Chemical imaging ; Artificial intelligence ; Sensor fusion ; Resolution (logic) ; Image fusion ; Hyperspectral imaging ; Chemistry ; Physics ; Optics ; Image (mathematics) Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Ion-surface interactions and analysis
Authors: Yang Yu | Yongkun Qiao | Chenlong Fan | Man Dong | Ke Cao
Source: Frontiers in Nutrition (article, 2025-12-17)
Field: Chemistry — Analytical Chemistry

Machine learning and near-infrared fusion-driven quantitative characterization and detection of protein content in maize kernels This study aims to develop a rapid and non-destructive method for determining protein content in maize using near-infrared spectroscopy (NIRS). To mitigate the effects of surface irregularities and uneven protein distribution in whole kernels on spectral measurements, maize powder was used as the test material to enhance the uniformity and stability of spectral signals. A total of 90 maize powder samples were collected from major production regions across China, and a custom NIRS acquisition system was constructed. To optimize the spectral data, eight preprocessing methods-including Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), First Derivative (1D), Savitzky-Golay smoothing (S-G), and their combinations-were systematically evaluated. Subsequently, traditional machine learning models (Partial Least Squares Regression, PLSR; Support Vector Machine, SVM) and deep learning models (ResNet-18, Transformer) were developed to predict protein content, and their performances were compared. Results indicated that the combined preprocessing strategy of First Derivative and Multiplicative Scatter Correction (1D + MSC) was the most effective. Among the models, PLSR demonstrated the best predictive performance, and traditional chemometric methods showed greater practical utility compared to deep learning models. To further enhance model efficiency, four feature wavelength selection methods-Partial Least Squares Regression Coefficients (PLSRC), Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and Uninformative Variable Elimination (UVE)-were applied. It was found that the PLSR model combined with the Successive Projections Algorithm (SPA) yielded the optimal performance, achieving a validation set correlation coefficient (<i>R</i> <sub>p</sub>) of 0.927, a root mean square error of prediction (RMSE<sub>P</sub>) of 0.301, and a residual predictive deviation (RPD) of 2.502, along with the fastest computational speed. This study provides a reliable technical solution and theoretical foundation for the rapid and non-destructive detection of protein content in maize, while also validating the advantage of using powdered samples in improving the accuracy of NIRS detection. Smoothing ; Partial least squares regression ; Residual ; Feature selection ; Multiplicative function ; Pattern recognition (psychology) ; Support vector machine ; Cross-validation ; Mean squared error ; Preprocessor Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Imaging and Spectroscopy Techniques
Authors: Zhengtao Xu | Christof Wöll | Stefan Braese
Source: Advanced Functional Materials (article, 2025-10-04)
Field: Chemistry — Inorganic Chemistry

Advancing Electronic Application of Coordination Solids: Enhancing Electron Transport and Device Integration via Surface‐Mounted MOFs (SURMOFs) Abstract The layer‐by‐layer (LbL) assembly of coordination solids, enabled by the surface‐mounted metal–organic framework (SURMOF) platform, yields thin films with well‐defined orientation, tunable thickness, low density of defects, and editable crystalline heteroepitaxy. Such high‐quality thin films are suited for integrating metal‐organic framework (MOF) materials into devices used in electronics and optoelectronics technologies. However, the potential of the SURMOF platform has not been fully realized due to its instability, poor electronic interaction/transport, and limited intercalation/heteroepitaxy functions. Leveraging the longstanding efforts in processing and functionalizing coordination networks, four directions are highlighted for fully unleashing the technological potential of the SURMOF platform: 1) cascade cyclization to form polycyclic aromatic, nanographene‐like scaffolds with strong electron polarizability and electroactivity; 2) crosslinking by fused‐aromatic and metal–thiolate bridges for improved charge transport and structural stability; 3) covalent‐ionic heteroepitaxy of conductive metal–thiolate layers alternating with metal‐aqua‐hydroxide layers to emulate the transport layer and the charge storage layer in high‐temperature superconductors of cuprates and iron pnictides; and 4) machine learning (ML)‐based methods to optimize synthesis conditions. Electronics ; Polarizability ; Layer (electronics) ; Electrical conductor ; Electron transport chain ; Flexible electronics ; Charge (physics) ; Thin film Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques
Authors: Koyuru Nakayama | Akio Kumagai | Keita Sakakibara
Source: Carbohydrate Polymer Technologies and Applications (article, 2025-02-05)
Field: Materials Science — Biomaterials

Machine learning-assisted sedimentation analysis of cellulose nanofibers to predict the specific surface area This study introduced a novel machine learning (ML) approach for predicting the specific surface area (SSA) of cellulose nanofibers (CNFs) at various fibrillation stages by leveraging sedimentation profiles from their aqueous slurries. Both sedimentation speed and sedimentation heatmap images, derived from the sedimentation profile data, formed the basis of the ML-assisted prediction model, achieving a coefficient of determination (R²) of up to 0.94 for SSA prediction. The high R 2 values can be obtained through the appropriate ML algorithms used for the prediction model, including extreme gradient-boosting (XGBoost) regression and convolutional neural networks (CNN) for sedimentation speed and sedimentation heatmap images, respectively, which are effective to deal with these sedimentation data, enabling accurate predictions. Furthermore, the predicted SSA values were used for the construction of the prediction model for impact strength of polypropylene/ wood-derived CNF composite materials by integrating with the infrared spectrum data of the CNFs, achieving the improved R² of 0.88, as compared to the conventional models based on experimentally obtained SSA with R 2 = 0.79. This sedimentation analysis method therefore enables the acquisition of information related to the morphology of CNFs, which can be widely applied in the quality control of CNFs as well as in the material applications. Nanofiber ; Cellulose ; Sedimentation ; Materials science ; Surface (topology) ; Polymer science ; Nanotechnology ; Chemical engineering ; Geology ; Mathematics ; Engineering ; Geomorphology ; Geometry Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: Machine Learning Science and Technology (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

CrysMTM: a multiphase, temperature-resolved, multimodal dataset for crystalline materials Abstract We present CrysMTM, a large-scale, multimodal dataset designed to benchmark temperature- and phase-sensitive machine learning models for crystalline materials. The dataset comprises approximately 30 000 atomistic samples covering the three primary polymorphs of titanium dioxide–anatase, brookite, and rutile–each evaluated across a temperature spectrum ranging from cryogenic to ambient and elevated conditions. Each data entry integrates three complementary modalities: (1) three-dimensional atomic coordinates, (2) RGBA molecular visualizations, and (3) structured textual metadata encompassing geometric descriptors, local bonding environments, and phase transformation parameters. This multimodal structure enables both supervised and self-supervised learning across graph-based, image-based, and language-based architectures. CrysMTM supports rigorous evaluation of model robustness under thermal perturbations and crystallographic phase transitions. Baseline benchmarking across 18 models–including graph neural networks (GNNs), convolutional neural networks, and foundation models–reveals significant property-specific challenges. For example, bandgap predictions exhibit errors exceeding 25%, while volumetric expansion and atomic displacement estimations frequently deviate by more than 100%. Even state-of-the-art GNNs, which achieve an average in-distribution (ID) mean absolute percentage error of approximately 20%, show a threefold increase under out-of-distribution (OOD) thermal conditions. In contrast, a few-shot multimodal large language model reduces global prediction error from 96% to 23% and narrows the performance gap between ID and OOD cases to just four percentage points. These results highlight both the selective difficulty posed by temperature-sensitive geometric targets and the considerable room for innovation in model design. All dataset files, model implementations, and pretrained checkpoints are available at https://github.com/KurbanIntelligenceLab/CrysMTM . Materials science Machine Learning in Materials Science
Authors: Honghong Gao | Zhichao Li | Tao Tao Qiang | Yan Liang | Baoji Ma
Source: Materials Today Communications (article, 2025-10-19)
Field: Materials Science — Biomaterials

Deformation prediction and process parameters optimization of magnesium alloys processed by ultrasonic surface rolling Artificial neural network ; Deformation (meteorology) ; Machining ; Process (computing) ; Reduction (mathematics) ; Response surface methodology ; Approximation error ; Ultrasonic sensor ; Nonlinear system Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Ruofan Shen | Ya-Chao Liu | Wanli Jia | Jia Shi | Yoshiyuki Kawazoe | Vei Wang
Source: The Journal of Physical Chemistry C (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Machine Learning Insights into Band Alignments of van der Waals Heterostructures The integration of two-dimensional (2D) materials into van der Waals heterostructures (vdWHs) enables the stacking of atomically thin layers through weak vdW interactions, offering tunable properties for next-generation optoelectronic and catalytic applications. In this study, we systematically predicted the band alignment types of over 32,000 vdWHs, constructed by pairing any two of the 256 semiconductor monolayers from the 2D Semiconductor Computational Database (2DSdb) [J. Phys. Chem. Lett. 2022 13, 11581], based on Anderson’s rule. Nearly 100 features were extracted from the physical properties of these vdWHs to establish a descriptor database for the triclassification of vdWHs using machine learning models, including extreme gradient boosting, random forest, and gradient boosting classifier, achieving accuracy rates between 85 and 87%. SHapley Additive exPlanations (SHAP) analysis identified electronegativity, oxygen fraction, valence electrons in the p-orbital, and lattice constants as the most influential features. The resulting database is expected to provide valuable guidance for experimentalists in designing nanodevices and photocatalysts. van der Waals force ; Heterojunction ; Materials science ; Physics ; Computer science ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Psychology ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Yutaka Majima | Yosuke Oyama | Eiji Ohta | Long Yang | Atsushi Ishikawa | Junichiro Otomo | Yasufumi Sakai
Source: Procedia Computer Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Self-Supervised Learning by Element Shuffling for Material Property Prediction Materials Informatics (MI) using machine learning has been widely adopted for materials discovery. Material structures are naturally represented as graph structures, which makes Graph Neural Networks (GNNs) a prevalent method for predictions. However, Artificial Intelligence (AI) models require large volumes of high-quality labeled data, which is costly to obtain through simulations or experiments. To address this, Self-Supervised Learning (SSL) methods have been developed to utilize unlabeled structure data, allowing models to learn essential data representations using automatically generated pseudo-labels. An SSL method for materials, which involves replacing atoms with others, has successfully improved predictions for inorganic material energies. Despite its success, this method suffers from easily detectable atom replacements because elements not originally present in the structure are used for replacement, making effective SSL challenging. Building on previous work, this study introduces a novel SSL method for predicting material properties. This method is based on shuffling atoms, ensuring that the processed structure contains only elements present in the original structure. Furthermore, it is designed to prevent hint information from interfering with the learning process. The proposed method was evaluated using pre-training with SSL and semi-supervised learning. In the pre-training validation, the accuracy increase during fine-tuning was confirmed to be up to 0.366 eV compared to a state-of-the-art method. Furthermore, the effectiveness of the method for semi-supervised learning was observed, achieving approximately a 12% improvement in energy prediction accuracy compared to using only supervised training. Shuffling ; Computer science ; Property (philosophy) ; Element (criminal law) ; Artificial intelligence ; Machine learning ; Programming language ; Law Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced machining processes and optimization
Authors: Saibal Ghosh | Sonali Banerjee | Koyel Basak | Jyoti Prajapati | Santanu Majumder | Pradip Bhattacharyya
Source: Communications in Soil Science and Plant Analysis (article, 2025-01-13)
Field: Materials Science — Biomaterials

Identifying an Appropriate Extractant for Assessing the Presence of Available Potassium in Soils with High Mica Content: A Comprehensive Statistical Analysis The choice of extractant for estimating plant-available potassium (K) in mica-rich soil has not been universally established. In order to evaluate and monitor the complex chemical performance of K in K-rich soil, and subsequently, its availability in crops, a suitable extractant protocol that takes into consideration soil properties in relation to rice plant parts (root, shoot, and grain) uptake becomes necessary. Nine laboratory soil K extraction methods to estimate the K content using 63 soil samples collected from nearly 21 mica mines, encompassing a wide range of properties across different agricultural lands. While extraction using ammonium acetate is the widely used for soil K testing, other methods have also been proposed for estimating crop-available K. Among the various extractants tested, 0.5 M sodium bicarbonate at pH 8.5 (mean 132.24 mg/kg) gave the best results for estimating the K content in mica-rich soil from agricultural areas near mica mines. Additionally, the soil water-soluble K extractant showed promise for rice plant parts such as root, shoot, and grain K uptake, as indicated by a correlation coefficient of 0.94, 0.94, and 0.93 respectively. Based on the artificial neural network (ANN) and Sobol (variance-based method for determining how different input variables contribute to the output variance of a model) methods, water-soluble K and the Mehlich 3 extraction procedure showed the best results for suitable extractable forms of K and a positive impact on K uptake by rice grain. Taylor (a graphical tool used to compare various models) diagram used for comparing multiple machine learning algorithms, multivariate adaptive regression spline and extreme gradient boosting (XGBoost) emerged as the best-fit models for determining the contribution of each extractant to rice K uptake. Considering soils derived from weathered parent materials often have negative K balances, which could lead to negative of plant-available K. Soil water ; Potassium ; Mica ; Chemistry ; Environmental chemistry ; Statistical analysis ; Mineralogy ; Environmental science ; Soil science ; Mathematics ; Materials science ; Statistics ; Metallurgy Clay minerals and soil interactions ; Soil Geostatistics and Mapping
Authors: Walter O. C. Flores | Felipe Hornung | Katia Christina Zuffellato-Ribas | Márcia Müller | José Luí­s Fabris | André Eugênio Lazzaretti
Source: IEEE Sensors Journal (article, 2025-07-02)
Field: Chemistry — Analytical Chemistry

Machine Learning Models for Chlorophyll Content Estimation in Wheat Leaves From Multiangular Reflection Spectra Chlorophyll is a crucial pigment in plants that converts sunlight into chemical energy during photosynthesis, whereas nitrogen is an essential nutrient associated with the growth and development of crops. As nitrogen and chlorophyll contents in the plants are closely related, monitoring the chlorophyll content allows for ascertaining the photosynthetic rate and dry matter production, which contributes to the optimization of crop production. In this sense, there is great interest in developing non-destructive and reliable methods for chlorophyll quantification in the field. This work proposes a non-destructive method based on multi-angular reflectance spectroscopy assisted by artificial intelligence for quantifying chlorophyll in wheat leaves. Deep neural networks, MiniRocket, XGBRegressor, Random Forest Regression, and Decision Tree Regressor models were trained and tested with reflection spectra. The spectral range from 450 to 750 nm was preprocessed by Savitzky-Golay filter and Principal Component Analysis. Chlorophyll content was determined using a fresh mass extraction methodology and absorption spectroscopy. Results indicate the effectiveness of machine learning models in predicting total chlorophyll content in wheat leaves from reflection spectra taken at multiple arbitrarily chosen angles. The deep neural network model determined total chlorophyll content with a mean absolute error of 0.022 mg/g, and root mean squared error of 0.032 mg/g. Additionally, we provide a SHapley Additive exPlanations (SHAP) analysis to determine the most relevant spectral ranges for the model prediction and their relation with chlorophyll a and b. The results together form a relevant and original contribution to quantifying and interpreting chlorophyll in wheat leaves using non-destructive approaches. Reflection (computer programming) ; Spectral line ; Reflectivity ; Content (measure theory) ; Chlorophyll ; Biological system ; Remote sensing ; Computer science ; Materials science ; Environmental science ; Optics ; Physics ; Mathematics ; Botany ; Geology ; Astronomy ; Biology ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Abid | Zhao, Luneng | Huang, Ju | Zheng, Yongjia | Sato, Yuta | Wang, Tianyu | Levshov, Dmitry | Wang, Lingfeng | Lin, Qingyun | Han, Zhen | Yang, Chunxia | Nduwarugira, Bill Herve | Ma, Yicheng | Zheng, Yige | Wang, Hang | Ullah, Salman | Khan, Afzal | Zhang, Qi | Li, Wenbin | Gao, Junfeng | Ju, Bingfeng | Ding, Feng | Li, Yan | Herrebout, Wouter | Suenaga, Kazu | Maruyama, Shigeo | Yang, Huayong | Xiang, Rong
Source: ArXiv.org (preprint, 2025-06-19)
Field: Materials Science — Materials Chemistry

Preferred Synthesis of Armchair Transition Metal Dichalcogenide Nanotubes In this work, we present the synthesis of transition-metal dichalcogenide (TMDC) nanotubes with a preferred chiral angle. SnS2, MoS2, and WS2 are formed with high yield and structural purity inside the channels of boron nitride nanotubes. Atomic-resolution imaging, nano-area electron diffraction, and Circular Dichroism spectroscopy reveal that these synthesized TMDC nanotubes prefer to have an armchair configuration, with a probability up to 84%. Density functional theory reveals a negligible difference in the formation energy between armchair and zigzag nanotubes, suggesting that the chirality preference does not originate from the differences in structural stability. However, a detailed TEM investigation revealed that these TMDC nanotubes formed via a transition state of nanoribbons, and these nanoribbons are energetically more stable in a zigzag configuration. Subsequent machine learning potential molecular dynamics simulations verify that zigzag nanoribbons do roll up to form an armchair SnS2 nanotubes. Finally, this "zigzag nanoribbon to armchair nanotube" transition process is directly observed in real time by in-situ transmission electron microscopy. This work demonstrates the first, but likely general, experimental strategy for synthesizing chirality-preferred TMDC nanotubes. Zigzag ; Density functional theory ; Boron nitride ; Nanotube ; Graphene nanoribbons ; Yield (engineering) ; Structural stability ; Carbon nanotube 2D Materials and Applications ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials
Authors: Jon de Vecchy | J.H. Tortai | M. Besacier | Delphine Le Cunff | B. Pelissier
Source: Microelectronic Engineering (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Hybrid metrology investigation combining Raman and ellipsometry spectroscopy applied to in line GeSbTe crystallization measurements and deep learning approaches for accurate prediction Raman spectroscopy ; Ellipsometry ; Materials science ; Metrology ; Crystallization ; Line (geometry) ; Nanotechnology ; Optoelectronics ; Optics ; Physics ; Thin film ; Chemical engineering ; Engineering ; Mathematics Phase-change materials and chalcogenides ; Nonlinear Optical Materials Studies ; Chalcogenide Semiconductor Thin Films