jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luigi Vitagliano | Nunzianna Doti | Nicole Balasco
Source: International Journal of Molecular Sciences (review, 2025-10-18)
Field: Materials Science — Biomaterials

Determinants of Chain Selection and Staggering in Heterotrimeric Collagens: A Comprehensive Review of the Structural Data Collagen is a family of large, fibrous biomacromolecules common in animals, distinguished by unique molecular, structural, and functional properties. Despite the relatively low complexity of their sequences and the repetitive conformation of the triple helix, which is the defining feature of this family, unraveling sequence-stability and structure-function relationships in this group of proteins remains a challenging task. Considering the importance of the structural aspects in collagen chain recognition and selection, we reviewed our current knowledge of the heterotrimeric structures of non-collagenous (NC) regions that lack the triple helix sequence motif, Gly-X-Y, and are crucial for the correct folding of the functional states of these proteins. This study was conducted by simultaneously surveying the current literature, mining the structural database, and making predictions of the three-dimensional structure of these domains using highly reliable approaches based on machine learning techniques, such as AlphaFold. The combination of experimental structural data and predictive analyses offers some interesting clues about the structural features of heterotrimers formed by collagen NC regions. Structural studies carried out in the last decade show that for fibrillar collagens (types I, V, XI, and mixed V/XI), key factors include the formation of specific disulfide bridges and electrostatic interaction patterns. In the subgroup of collagens whose heterotrimers create supramolecular networks (types IV and VIII), available structural information provides a solid ground for the definition of the basis of the molecular and supramolecular organization. Very recent AlphaFold predictions and structural analyses of type VI collagen offer strong evidence of the specific domains in the NC region of the protein that are involved in chain selection and their staggering. Insightful crystallographic studies have also revealed some fundamental elements of the chain selection process in type IX collagen. Collectively, the data reported here indicate that, although some aspects (particularly the quantification of the relative contribution of the NC and triple helix regions to correct collagen folding) are yet to be fully understood, the available structural information provides a solid foundation for future studies aimed at precisely defining sequence-structure-function relationships in collagens. Heterotrimeric G protein ; Folding (DSP implementation) ; Disulfide bond ; Structural biology ; Structural bioinformatics ; Supramolecular chemistry ; Structural protein ; Protein structure ; Sequence (biology) Collagen: Extraction and Characterization ; Calcium Carbonate Crystallization and Inhibition ; biodegradable polymer synthesis and properties
Authors: Amena Darwish | Akash Meena | Stefan Ericson | Kent Salomonsson
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning and reduced-order modeling for nonlinear dynamical systems: Application to melt-pool temperature prediction Dynamic mode decomposition ; Nonlinear system ; Deep learning ; Perceptron ; Field (mathematics) ; Mean squared error ; Artificial neural network ; Representation (politics) Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Model Reduction and Neural Networks
Authors: Muhammad AKRAM KHAN
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Metals and Alloys

AI Algorithm for Hydrogen Embrittlement Prediction in Gas Pipelines - (AI-HYPERN) Pipeline transport ; Hydrogen embrittlement ; Pipeline (software) ; Natural gas ; Artificial neural network ; Benchmark (surveying) ; Hydrogen ; Energy (signal processing) Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis ; Material Properties and Failure Mechanisms
Authors: Furat, Orkun | Gogineni, Vinay Chakravarthi | Bindslev, Henrik | Nadimi, Esmaeil S.
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-informed Neural Operators for Predicting 3D Electromagnetic Fields Transformed by Metasurfaces Metasurfaces, typically realized as arrays of nanopillars, transform electromagnetic (EM) fields depending on their geometry and spatial arrangement. For solving the inverse problem of designing new metasurfaces that transform EM fields in a desirable manner, it is often necessary to explore large design spaces through full-wave simulations that can be computationally demanding. In this work, we demonstrate that neural operators, which are artificial neural network architectures designed to learn operators between function spaces, can effectively approximate the differential operators underlying Maxwell's equations, enabling their use as fast and accurate 3D surrogate models that can predict 3D EM fields transformed by metasurfaces. To calibrate neural operators, we generate synthetic training data consisting of 3D metasurface geometries together with their associated 3D EM fields obtained by numerically solving Maxwell's equations. Using the generated synthetic data, we train physics-informed neural operators to minimize physical inconsistencies of predicted EM fields by incorporating residuals that capture deviations from Maxwell's equations. We observe that a training dataset consisting of fewer than 5000 examples already suffices to achieve reasonable results. In particular, our experiments show that the resulting 3D surrogate model achieves high predictive performance across a wide range of metasurface geometries, including types of structures not encountered during training. Notably, it predicts diffraction efficiencies with relative errors of 3.9 % and provides a 67-fold speedup compared to conventional 3D simulations. Overall, once trained, our 3D surrogate model can rapidly predict EM fields for previously unseen metasurface geometries, which can facilitate efficient gradient-based design of nanostructured materials for EM wave control. Artificial neural network ; Range (aeronautics) ; Field (mathematics) ; Inverse problem ; Surrogate model ; Speedup ; Inverse ; Electromagnetic field Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Antenna and Metasurface Technologies
Authors: Dewei Tang | Yutao Liu | Song Han | Cheng Deng | Mengyuan Liu | Tinghong Gao | Yongchao Liang | Qingquan Xiao | Yunjun Ruan
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Neuroevolution potential-driven accurate and efficient discovery of Graphene/GaN heterojunctions: From ballistic-diffusive transition to thermal conductivity enhancement Graphene ; Heterojunction ; Thermal conductivity ; Materials science ; Nanotechnology ; Optoelectronics ; Conductivity ; Condensed matter physics ; Physics ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Longguo Wu | Yao Zhang | Ling Ma | Yanzhe Zhu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Detection of the Fv/Fm in Melon Leaves by Fluorescence Hyperspectral Imaging Technique and Deep Learning Hyperspectral imaging ; Artificial intelligence ; Fluorescence ; Remote sensing ; Melon ; Pattern recognition (psychology) ; Computer science ; Biology ; Geography ; Physics ; Optics ; Horticulture Spectroscopy and Chemometric Analyses
Authors: Katherine M. Hinnant (12359081) | Paige E. Sudol (16353582) | Jeffrey A. Cramer (1266009) | David W. Moore (12482140) | Loren C. Brown (3395768) | Caleb. M. Bunton (22215893) | Matthew C. Davis (1595725) | Arthur W. Snow (2028133) | Ramagopal Ananth (18004093)
Source: Figshare (article, 2025-09-09)
Field: Chemistry — Organic Chemistry

Database of SurfactantStructure and Properties toAid in the Development of Fluorine-Free, Fire-Suppressing Surfactantsand Foams A database is developed to facilitate correlations between surfactant structure, properties, and foam fire suppression and to develop machine learning models. Models and structure–property relationships are crucial for developing insights into surfactant design and development for a specific application such as firefighting. The database contains foam fire suppression metrics for 71 individual surfactants and 116 mixture formulations with well-characterized chemical structures. Thirty-six of the 71 are synthesized, and the rest are commercial. In addition, the database contains 13 commercial individual surfactants and 48 commercial mixture formulations without well-defined structures. Properties include surfactant critical micelle concentration, solution surface tension, and interfacial tension with fuels, fuel-induced foam degradation, heptane flux through a foam layer, and foam fire extinction performance on 19 cm heptane and gasoline pool fires. Limited acute aquatic toxicity measurements for 7 surfactants and 2 mixtures for 48 h exposure to Ceriodaphnia dubia are reported. These values were compared to the calculated acute aquatic toxicity for 48 h exposure to Daphnia magna using two methods (one being Ecological Structure Activity Relationships, ECOSAR). These programs were unable to address issues of surfactant polydispersity and showed inconsistencies in classifying surfactant toxicity. Both surfactant mixtures exhibited synergism in acute aquatic toxicity: Cap:G215 was more toxic and G225:502W was less toxic than their individual components. These calculated methods should not be used to assess the potential toxicity of surfactants within this database, and continued testing of surfactant mixture acute aquatic toxicity is needed. This database along with continued toxicity testing can contribute to model development for rapidly extinguishing, environmentally friendly firefighting foams. Pulmonary surfactant ; Daphnia magna ; Aquatic toxicology ; Acute toxicity ; Surface tension ; Dispersity ; Critical micelle concentration Surfactants and Colloidal Systems ; Oil Spill Detection and Mitigation ; Environmental Chemistry and Analysis
Authors: Jieqing Tan | Fazida Hanim Hashim | Jahariah Sampe | Aqilah Baseri Huddin | Ghassan Maan Salim | Sawal Hamid Md Ali
Source: PeerJ (article, 2025-11-18)
Field: Chemistry — Analytical Chemistry

Machine learning classification of mango maturity based on carotene content from Raman spectra Determining mango ripeness is essential for ensuring its delicious taste, enticing aroma, and rich nutritional value. For farmers, harvesting mangoes too early can result in stunted fruit and lower yields compared to those harvested at a ripe stage. This study aims to develop a potentially non-invasive and efficient method for detecting mango ripeness using Raman spectroscopy. Traditional methods, which rely on human assessment and color evaluation with image processing, are inconsistent, inaccurate, and time-consuming due to variations in mango color and individual differences in vision and perception. To address these limitations, this study pursued three main objectives: extracting data characteristics of organic compounds in mangoes based on raw Raman spectrum data, identifying the correlation between carotene characteristics and mango ripeness levels, and evaluating the performance of machine learning models in classifying mango ripeness levels. A total of 29 mango fruit spectra were analyzed, with 13 samples selected to represent three ripeness categories: underripe, ripe, and overripe. Raman spectra peak signal analysis revealed that mango peel contains lycopene, β-carotene, lutein, and neoxanthin, all of which are derived from carotenoid molecules in the range of 1,480 cm<sup>-1</sup> to 1,550 cm<sup>-1</sup>. Statistical analysis confirmed the significance (<i>p</i> < 0.05) of extracted Raman Peak Intensity features in distinguishing ripeness levels, supported by high correlation coefficients between carotenoid peak intensity and mango maturity. This study achieved 100% accuracy in classifying mango ripeness levels using three classifier models: the Medium Gaussian Support Vector Machine, the Cubic Support Vector Machine, and the Weighted K-Nearest Neighbors. Raman spectroscopy has proven to be a reliable and robust method, immune to external factors such as light, humidity, and noise, which makes it a promising approach for assessing mango ripeness. Ripeness ; Raman spectroscopy ; Support vector machine ; Pattern recognition (psychology) ; Hue ; Ripening ; Carotenoid ; Carotene Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Plant Physiology and Cultivation Studies
Authors: Yashun Wang | Huirong Chen | Jian-Ting Gong | Yang Cui | Huiqin Zou | Yonghong Yan
Source: Journal of Traditional Chinese Medical Sciences (article, 2025-03-11)
Field: Chemistry — Analytical Chemistry

Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne To explore a rapid and accurate method for evaluating the quality of Prunus mandshurica (Maxim.) Koehne ( P. mandshurica , Ku Xing Ren) during rancidity using machine vision and learning. Sensory evaluation and chemometrics were used to classify P. mandshurica quality grades after rancidity. Chemical indicators of the P. mandshurica quality change were determined to verify the obtained grades and support the subsequent modeling. The International Commission on Illumination color space was used to extract the color features of the P. mandshurica . Discrimination and prediction models based on color features combined with multiple machine learning algorithms were established using 10-fold cross-validation and external test set validation. The P. mandshurica rancidity samples were allocated to three quality grades. The Bayes net model based on powder color successfully identified the P. mandshurica at different grades with an accuracy of 88.89% and 100% using two validations, and the naive Bayes model based on section color achieved the same accuracy with an receiver operating characteristic area of 0.979. The instance-based k-nearest neighbors model based on powder color performed best in predicting the amygdalin content [R 2 = 0.9801, mean absolute error (MAE) = 0.2071, root mean squared error (RMSE) = 0.4170], followed by the random committee model in predicting the acid value (R 2 = 0.9580, MAE = 1.5121, RMSE = 1.9099) and the random forest model in predicting the peroxide value (R 2 = 0.8857, MAE = 0.0027, RMSE = 0.0035). This study demonstrates that color digitization analysis is a potential method for rapidly evaluating the quality of P. mandshurica across the rancidity process, providing a new reference for the quality assessment of traditional Chinese medicines. Maxim ; Prunus ; Biology ; Botany ; Philosophy ; Epistemology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Ke Qin | Xiaoting Dai | Linhai Wu
Source: Journal of Food Protection (article, 2025-10-24)
Field: Chemistry — Analytical Chemistry

Hybrid Sampling and Ensemble Learning for Food Safety Sampling Inspection Classification Food safety sampling inspection is critical for risk prevention in complex supply chains. However, extreme class imbalance, where unqualified samples are significantly outnumbered by qualified ones, biases machine learning (ML) models to prioritize majority classes, compromising unqualified sample detection. Conventional oversampling methods fail to handle food inspection data's nonlinear features, complex distributions, and multiclass scenarios, often generating low-quality synthetic samples and noisy decision boundaries. To address these challenges, we proposed LOF-KNN-CSENN (Local Outlier Factor-K-Nearest Neighbors-Combined Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors), a hybrid sampling algorithm of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) integrating Local Outlier Factor (LOF) for noise filtering and K-Nearest Neighbors (KNN) for boundary sample preservation. LOF-KNN-CSENN synergizes minority oversampling and majority undersampling to optimize data distribution. A stacking ensemble learning framework is further introduced, combining six tree-based models with Logistic Regression (LR) as a meta model to enhance classification robustness. Experiments on a real-world food safety sampling inspection dataset demonstrated that LOF-KNN-CSENN suppresses noisy sample synthesis and balances data distribution. When integrated with stacking, the model achieves 0.4-5.6% higher precision and 0.8-30.7% higher F1-score compared to single models. Shapley Additive Explanations (SHAP) analysis identified production address, sampling stage, and location as key risk factors, supporting targeted supervision. This study provides a novel framework for intelligent food safety regulation, leveraging hybrid sampling and ensemble learning to mitigate class imbalance and enhance unqualified sample detection in multicategory food inspection. Oversampling ; Undersampling ; Sample (material) ; Outlier ; Sampling (signal processing) ; Ensemble learning ; Anomaly detection ; Decision boundary Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Industrial Vision Systems and Defect Detection
Authors: Ran An | Zhihan Jiang | Q.P. Cao | Shuang Zhang | Edith C.‐H. Ngai | Tianshuo Zhao
Source: Advanced Functional Materials (article, 2025-10-06)
Field: Chemistry — Spectroscopy

Chemically Programmable Fano Resonances via Colloidal Nanocrystal‐Ligand Chemistry for Ultra‐Sensitive Ion Detection Abstract Thin‐film‐based Fano resonators (TFFRs) offer a promising route to scalable, high‐sensitivity refractive index (RI) sensing without complex nanostructure fabrication. However, their vacuum‐deposited, porous, and lossy dielectrics are limited compositionally to perform ion detection in biochemical applications. This study introduces an ion‐responsive TFFR platform by integrating two resonators, namely a colloidal metal nanocrystal (NC) film and a metal–insulator–metal (MIM) cavity. Upon precise ligand ion treatment, the solution‐processed NC film can provide widely tunable RI for coupling with the MIM cavity, enabling continuous spectral tuning of the TFFR between Fano and Lorentzian line shapes in calculations. It showcases this design using Au and Ag NCs and SCN − and Cl − ligand ions, respectively. Chemical, structural, and optical analyses track the RI evolution within the NC film as the ligand ion concentration changes stepwise from 1 × 10 −6 to 10 mg mL −1 , revealing reproducibility and sample‐to‐sample variation &lt;3.5%. The measured distinctive spectral signatures corroborate simulations to enable efficient machine learning algorithms that predict SCN − concentrations below 1 × 10 −3 mg mL −1 with 97.4% accuracy. The NC/MIM TFFR achieves electrode‐free detection of SCN − and Cl − ions with exceptional detection limits of 245.6 and 3.83 nmol L −1 , respectively, in a dynamic range exceeding 10⁷. Ion ; Colloid ; Nanostructure ; Nanocrystal ; Ligand (biochemistry) ; Refractive index ; Resonator ; Dielectric Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques
Authors: А. О. Еркимбаев | В. Ю. Зицерман | Г. А. Кобзев
Source: Научно-техническая информация Серия 2 Информационные процессы и системы (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Традиция интенсивного использования данных на примере дисциплины «Теплофизика». Методы и алгоритмы На примере теплофизики прослеживается эволюция подходов к работе с научными данными по свойствам веществ и материалов. Показано, что на всех этапах теплофизика может быть отнесена к категории «Дисциплин с интенсивным использованием данных» (Data-Intensive Science, DIS), для которых характерна превалирующая ориентация на работу с данными, включая их хранение, систематизацию и извлечение значимой информации. Показано совершенствование методов обработки, связанное с привлечением новых информационных технологий, включая методы машинного обучения. Проведен анализ его реальных возможностей в области термодинамики при сопоставлении с традиционными методами статистики. В этой связи рассмотрена общая проблема соотношения статистики и науки о данных (Data Science), породившая обширную дискуссию в литературе и сети Интернет. Все выводы авторов основаны на анализе конкретных проблем прогнозирования свойств веществ, построения уравнения состояния и термодинамики многокомпонентных систем. Using the example of thermophysics, the evolution of approaches to working with scientific data on the substances and materials properties is traced. It shows that throughout all stages, thermophysics can be classified as a "Data-Intensive Science" (DIS), characterized by a focus on working with data, including its storage, organization, and extraction of meaningful information. Improvements in processing methods are shown, associated with the use of new information technologies, including machine learning techniques. The analysis of their potential in the field of thermodynamics is compared with traditional statistical methods. In this context, the general issue of the relationship between statistics and Data Science is discussed, which has generated extensive debate in literature and online.. All the authors' conclusions are based on an analysis of specific issues related to predicting the properties of substances and constructing the equation of state and thermodynamic models for multicomponent systems. Focus (optics) ; Field (mathematics) ; Data processing ; Statistical analysis ; Experimental data Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Yuanhao Li | Weijin Zhang | Fuyin Xie | Zhongru Zhou | Zequn Yang | Lijian Leng | Hailong Li
Source: Separation and Purification Technology (article, 2025-12-31)
Field: Chemistry — Inorganic Chemistry

Automated machine learning predicting SO₂ and CO₂ adsorption and separation in metal organic frameworks Flue gas ; Metal-organic framework ; Adsorption ; Boosting (machine learning) ; Bridging (networking) ; Selectivity ; Deep learning Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Carbon Dioxide Capture Technologies