jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hui Zhang | Jiaqi Zhu | Tingting Shen | Hongxu Zhang | Linlin Zhang | Yunyun Luo | Jizhong Yan | Jieqiang Zhu | Cuifen Fang
Source: Microchemical Journal (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

Authenticity identification of Pinellia ternata based on hyperspectral imaging combined with spectral-texture multimodal data fusion strategy Pinellia ternata has high commercial and ecological value. As a traditional Chinese medicine (TCM), the authenticity identification of Pinellia ternata is critical to maintaining the quality and safety of the market. Traditional chemical detection methods are highly destructive and operationally cumbersome, struggling to meet industrial-scale rapid screening demands. This study developed a hyperspectral imaging-based spectral-texture multimodal fusion strategy and combined with machine learning algorithms to achieve efficient non-destructive discrimination of TCM, taking Pinellia ternata (PT), Arisaema heterophyllum (AH), Typhonium flagelliforme (TF), and Pinellia pedatisecta (PP) as examples. Hyperspectral images from 1140 samples were acquired in the 898–1751 nm spectral range. Critic weighting method objectively identified key characteristic wavelengths, while 11 textural features were extracted using gray-level run-length matrix analysis. A variety of feature selection strategies were employed to refine texture dimensions. By constructing dual-stage feature screening, important wavelengths and texture information were evaluated and captured. Based on the above, a spectral-textural multimodal dataset was constructed. Models such as Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) algorithm enabled authenticity verification, while the Linear Discriminant Analysis (LDA) were employed for TCM sorting. Results showed that DD-SIMCA model implementing spectral-image data fusion strategy demonstrated superior efficiency (99.87 %) over single-modality analytical approaches (92.47 %). The classification accuracy rate of LDA for PT and its counterfeits reached 99.17 %. Therefore, the hyperspectral imaging-based spectral-texture multimodal fusion strategy and intelligent identification framework overcome the limitations of single-modality analysis, and offered technical foundations for intelligent classification of raw material in manufacturing of TCM. • Authenticity identification of Pinellia ternata using hyperspectral imaging. • A multimodal data fusion strategy based on spectrum-texture information is raised. • Building Critic-NAD feature screening strategy to achieve cooperative optimization. • The DD-SIMCA model can effectively identify the authenticity of Pinellia ternata . • The strategy increased identification efficiency to 99.87 %, an increase of 7.40 %. Pinellia ternata ; Hyperspectral imaging ; Artificial intelligence ; Texture (cosmology) ; Pattern recognition (psychology) ; Identification (biology) ; Fusion ; Computer science ; Botany ; Biology ; Image (mathematics) ; Philosophy ; Linguistics ; Medicine Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Traditional Chinese Medicine Analysis
Authors: M. V. A. Saraiva | Orlando David Marbello Ospina | Lucas Konaka Nolasco | Renan Cunha | Cléber Renato Mendonça
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting Fs-Laser-Induced Nv Centers with Pca and Neural Networks Artificial neural network ; Laser ; Computer science ; Artificial intelligence ; Physics ; Optics Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Laser-Matter Interactions and Applications
Authors: Cheng Zong | De-Ping Guo | Renhong Wang | Weihan Zhang | Jiaqi Dai | Zhongqin Zhang | Cong Wang | Xianghua Kong | Wei Ji | Cheng, Zhihai | Lu, Zhong-Yi | Ji, Wei
Source: ArXiv.org (preprint, 2025-07-14)
Field: Chemistry — Inorganic Chemistry

Kinetically accessible 1D magnetic chains of transition-metal chalcogenides and halides on van der Waals surfaces One-dimensional (1D) chains offer unique opportunities for nanoelectronics and spintronics, yet their experimental realization remains challenging because 1D motifs are often thermodynamically disfavored relative to higher-dimensional phases. Here we present a high-throughput first-principles exploration of 1D single-atomic transition-metal chalcogenide and halide chains, screening 6,832 candidates constructed from binary combinations of 28 metals and 8 non-metals. To assess kinetic accessibility, we compare the formation energetics of 1D chains with competing two-dimensional polymorphs at the nucleation stage across relevant chemical-potential windows, using nucleation-stage thermodynamic selectivity as a proxy. This workflow identifies 183 kinetically accessible 1D chains. Interpretable machine-learning analysis reveals two simple stability descriptors as key drivers of 1D stabilization. The accessible chains exhibit diverse magnetic configurations with different magnetic characters. We further uncover their pronounced magnetoelastic couplings, exemplified by CrTe with giant magnetostriction reaching 5.93%. Finally, we show that selected metallic ferromagnetic chains retain robust edge magnetism on superconducting substrates, laying the groundwork for proximity-induced topological superconductivity and Majorana zero modes. Magnetism ; Ferromagnetism ; van der Waals force ; Superconductivity ; MAJORANA ; Chalcogenide ; Graphene ; Monatomic ion ; Nanoelectronics Inorganic Chemistry and Materials ; Magnetic and transport properties of perovskites and related materials ; Iron-based superconductors research
Authors: Wallace, Austin M. | Sherrill, C. David | Krishnan, Giri P.
Source: arXiv (Cornell University) (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

$Δ$-ML Ensembles for Selecting Quantum Chemistry Methods to Compute Intermolecular Interactions Ab initio quantum chemical methods for accurately computing interactions between molecules have a wide range of applications but are often computationally expensive. Hence, selecting an appropriate method based on accuracy and computational cost remains a significant challenge due to varying performance of methods. In this work, we propose a framework based on an ensemble of $Δ$-ML models trained on features extracted from a pre-trained atom-pairwise neural network to predict the error of each method relative to all other methods including the ``gold standard'' coupled cluster with single, double, and perturbative triple excitations at the estimated complete basis set limit [CCSD(T)/CBS]. Our proposed approach provides error estimates across various levels of theories and identifies the computationally efficient approach for a given error range utilizing only a subset of the dataset. Further, this approach allows comparison between various theories. We demonstrate the effectiveness of our approach using an extended BioFragment dataset, which includes the interaction energies for common biomolecular fragments and small organic dimers. Our results show that the proposed framework achieves very small mean-absolute-errors below 0.1 kcal/mol regardless of the given method. Furthermore, by analyzing all-to-all $Δ$-ML models for present levels of theory, we identify method groupings that align with theoretical hypotheses, providing evidence that $Δ$-ML models can easily learn corrections from any level of theory to any other level of theory. Set (abstract data type) ; Limit (mathematics) ; Range (aeronautics) ; Ab initio ; Coupled cluster ; Quantum ; Artificial neural network ; Intermolecular force ; Quantum chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Wallace, Austin M. | Sherrill, C. David | Krishnan, Giri P.
Source: ArXiv.org (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

$Δ$-ML Ensembles for Selecting Quantum Chemistry Methods to Compute Intermolecular Interactions Ab initio quantum chemical methods for accurately computing interactions between molecules have a wide range of applications but are often computationally expensive. Hence, selecting an appropriate method based on accuracy and computational cost remains a significant challenge due to varying performance of methods. In this work, we propose a framework based on an ensemble of $Δ$-ML models trained on features extracted from a pre-trained atom-pairwise neural network to predict the error of each method relative to all other methods including the ``gold standard'' coupled cluster with single, double, and perturbative triple excitations at the estimated complete basis set limit [CCSD(T)/CBS]. Our proposed approach provides error estimates across various levels of theories and identifies the computationally efficient approach for a given error range utilizing only a subset of the dataset. Further, this approach allows comparison between various theories. We demonstrate the effectiveness of our approach using an extended BioFragment dataset, which includes the interaction energies for common biomolecular fragments and small organic dimers. Our results show that the proposed framework achieves very small mean-absolute-errors below 0.1 kcal/mol regardless of the given method. Furthermore, by analyzing all-to-all $Δ$-ML models for present levels of theory, we identify method groupings that align with theoretical hypotheses, providing evidence that $Δ$-ML models can easily learn corrections from any level of theory to any other level of theory. Set (abstract data type) ; Limit (mathematics) ; Range (aeronautics) ; Ab initio ; Coupled cluster ; Quantum ; Artificial neural network ; Intermolecular force ; Quantum chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Ling Yuan | Sheng Li | Yuxuan Ma | Han Zhang | Chen Chen | Weiming Zhang | Yanyang Zhang | Ming Hua | Lu Lv | Bingcai Pan
Source: Chemical Engineering Journal (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Machine learning-aided design of adapted water treatment adsorbents: Advances, challenges, and opportunities Process (computing) ; Embedding ; Set (abstract data type) ; Field (mathematics) ; Scarcity ; Adaptation (eye) ; Key (lock) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Hydrological Forecasting Using AI ; Adsorption and biosorption for pollutant removal
Authors: Boyu Du | Yanchun Yang | Lida Huang | Kelvin Y. Xie | Xing Wang | Jinghui Zhou | Xingxiang Ji | Lupeng Shao
Source: Advanced Science (article, 2025-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From Industrial Waste to Multistage Applications: Ultralight Lignin‐Based Aerogel with Situ Vertically Oriented Structure for Photothermal‐Assisted Pb <sup>2+</sup> Adsorption in Wastewater and Reuse as Efficient Output and Stability Triboelectric Materials In this work, the ultralight lignin-based aerogel (GSPCAA) with situ vertically oriented structure is developed through a simple and green synthesis strategy for efficient photothermally assisted adsorption of Pb<sup>2+</sup>, and then reused as a lignin-based triboelectric nanogenerator (TENG). Sulfonation is first performed on lignin's structure to enhance its water solubility and introduce the number of active adsorption sites. Then, GSPCAA is prepared using directed freezing and ionic crosslinking methods. The synergistic interactions between sulfonated lignin (SL) and graphene (GO) components endow GSPCAA with excellent photothermal properties, thereby generating the localized thermal environment on GSPCAA's surface to further improve the adsorption capacity of Pb<sup>2+</sup>. Finally, the spent adsorbent (GSPCAA-Pb<sup>2+</sup>) is applied as lignin-based TENG for the first time to achieve multi-stage application of lignin-based adsorbent. It displays excellent electric signal output performances. Meanwhile, a 3 × 3 self-powered array is designed to accurately identify and visualize the pressure distributions of different load objects. Besides, combined with deep learning model algorithms, an overall recognition accuracy of 98.5% is achieved in the classification and identification of 11 different objects, fully indicating its application potential in the field of smart homes. Aerogel ; Adsorption ; Triboelectric effect ; Reuse ; Thermal stability ; Wastewater ; Industrial wastewater treatment ; Graphene ; Chemical stability Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Boxu Zhou | He Wang | Siyuan Wang | Z. Ren | Qinglun Zhang | Yan Shi
Source: Microchemical Journal (article, 2025-11-24)
Field: Chemistry — Organic Chemistry

A nondestructive detection method for tracing the origin of Angelica dahurica based on comprehensive extraction of spectral features using AllNet combined with hyperspectral system Hyperspectral imaging ; Tracing ; Pattern recognition (psychology) ; Feature extraction ; Feature (linguistics) ; Block (permutation group theory) ; Convolutional neural network Wood and Agarwood Research ; Traditional Chinese Medicine Analysis ; Biological and pharmacological studies of plants
Authors: Jiahao Zhang | Wenli Gao | Yang Wang | Yicheng Shan | Shaojun Dong | Leitao Cao | Zijian Li | Hongti Zhang | Jing Ren | Shengjie Ling
Source: Small (article, 2025-05-06)
Field: Materials Science — Biomaterials

Spider‐Web‐Like Artificial Network for Smart Capture The construction of multifunctional spiderwebs by spiders, achieved through the synergistic integration of flagelliform silk, aggregate silk, and major ampullate silk, enables tasks such as prey capture, wind sensing, and water collection from the air. However, replicating spiderweb-like functionality using simple artificial systems remains challenging. Herein, the principles of structural and functional bioinspiration are employed to functionalize carbon nanotube fibers into capture fibers and signaling fibers, mimicking the function of flagelliform silk/aggregate silk, and major ampullate silk, respectively. The prepared capture fiber exhibits viscosity, humidity sensitivity, and actuation capabilities like the combination of flagelliform silk and aggregate silk, while the signal fiber resembles major ampullate silk, capable of sensing object touch through triboelectric signals and identifying the materials of touched objects. These functional fiber components are then assembled into an artificial spiderweb, which is further integrated with machine learning and Internet of Things technology to enable smart object capture. Similar to natural spiderwebs, this functionality is achieved by regulating web tension and relaxation through the switchover between wetting and drying states. The artificial network underscores the importance of learning from nature and expands the boundaries of soft robotics by integrating mutually complementary functions within a single system of simple construction. SILK ; Spider silk ; Materials science ; Computer science ; Fiber ; Nanotechnology ; Composite material Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Zhenhao Chen (8957471) | Zhuangwei Shi (10337897) | Jianchen Zi (20997047) | Chenhui Wang (3196683) | Hai Bi (2194462) | Yunlong Zhu (208562)
Source: Figshare (article, 2025-04-04)
Field: Chemistry — Analytical Chemistry

Ethanol ConcentrationDetermination in Baijiu by Graph-RegularizedPCA and Random Forest-Based Raman Spectroscopy Baijiu is a type of traditional Chinese alcoholic beverage with significant economic and cultural value. Ethanol concentration determination through machine learning-based Raman spectroscopy offers the advantages of being contact-free and rapid, and the technique holds considerable potential for baijiu quality control in the industrial manufacturing process. However, current applications of Raman spectroscopy for the quantitative analysis of biochemical materials are restricted by measurement accuracy, as well as the flexibility and robustness of chemometric tools. To address these issues, we propose a method that combines graph-regularized principal component analysis (graph-regularized PCA) and an ensemble learning framework, random forest, to capture effective low-dimensional representations from high-dimensional Raman spectra data while reducing spectra data instability. Furthermore, we propose a protocol that adopts ethanol solutions with various concentrations as the training set for fitting a single regression model to determine the ethanol concentrations of different types of baijiu. In ethanol concentration detection across all three types of baijiu, our proposed method achieves a mean average percentage error (MAPE) of 0.415% on ethanol concentration determination of all three types of baijiu, outperforming all other methods. The results validate the accuracy and robustness of our proposed method. Principal component analysis ; Raman spectroscopy ; Robustness (evolution) ; Analytical Chemistry (journal) ; Ethanol ; Chemometrics ; Spectroscopy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Edible Oils Quality and Analysis
Authors: Ahmad Ali
Source: ArXiv.org (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

From Regression to Classification: Exploring the Benefits of Categorical Representations of Energy in MLIPs Density Functional Theory (DFT) is a widely used computational method for estimating the energy and behavior of molecules. Machine Learning Interatomic Potentials (MLIPs) are models trained to approximate DFT-level energies and forces at dramatically lower computational cost. Many modern MLIPs rely on a scalar regression formulation; given information about a molecule, they predict a single energy value and corresponding forces while minimizing absolute error with DFT's calculations. In this work, we explore a multi-class classification formulation that predicts a categorical distribution over energy/force values, providing richer supervision through multiple targets. Most importantly, this approach offers a principled way to quantify model uncertainty. In particular, our method predicts a histogram of the energy/force distribution, converts scalar targets into histograms, and trains the model using cross-entropy loss. Our results demonstrate that this categorical formulation can achieve absolute error performance comparable to regression baselines. Furthermore, this representation enables the quantification of epistemic uncertainty through the entropy of the predicted distribution, offering a measure of model confidence absent in scalar regression approaches. Categorical variable ; Regression ; Scalar (mathematics) ; Entropy (arrow of time) ; Histogram ; Regression analysis ; Representation (politics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sijia Liu | Chao Yang | Bo Peng | Guanhua Sun | Hao Tian | Qingzhou Tao | Tianhao Li | Mingwei Li | Ye Wei
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accurate mechanical property prediction by combining unsupervised learning and multimodal Deep Neural Network Unsupervised learning ; Artificial neural network ; Property (philosophy) ; Ultimate tensile strength ; Pattern recognition (psychology) ; Construct (python library) ; Elongation ; Deep learning ; Microstructure Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metal Forming Simulation Techniques
Authors: Yoshinori Hashimoto | Mark Kirk
Source: International Journal of Pressure Vessels and Piping (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Charpy V-notch energy prediction for irradiated reactor pressure vessel steel using the extreme gradient boost method Charpy impact test ; Pressure vessel ; Reactor pressure vessel ; Materials science ; Nuclear engineering ; Pressure gradient ; Irradiation ; Structural engineering ; Composite material ; Mechanics ; Engineering ; Toughness ; Physics ; Nuclear physics Nuclear Materials and Properties ; Fusion materials and technologies ; Metallurgy and Material Forming
Authors: Marcel Hensel | Jochen Vestner | Jörg Fahrer | Dominik Durner
Source: American Journal of Enology and Viticulture (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Evaluation of Machine Learning Algorithms to Classify Blanc de noir Wines with Spectrophotometric Data <h3>Abstract</h3> <h3>Background and goals</h3> Machine learning (ML) and statistical modeling have emerged as important innovations in science. In wine research, these methods are often employed to predict abstract parameters, such as wine quality, based on complex instrumental chemical analysis. This study compares the use of three ML algorithms—logistic regression, support vector machine (SVM), and eXtreme Gradient Boost (XGBoost)—to distinguish blanc de noir wines from white and rosé wines on the basis of spectrophotometric data. <h3>Methods and key findings</h3> The present study of 176 commercial wines uses both spectrophotometric data and CIE L*a*b* coordinates created from dimensional reduction of the transmission data. The transmission spectra were used to train the extreme gradient-boosted trees and SVM models. CIE L*a*b* coordinates were used to train SVM and logistic regression models. After hyperparameter tuning, the use of the SVM model on the CIE L*a*b* data provided optimal classification, with a cross-validated accuracy of 0.88 and an F1 score of 0.93. <h3>Conclusions and significance</h3> The dimensional reduction of spectrophotometric data for use as input to train ML models can both provide accurate ML results and simplify the use of ML algorithms for potential users. The final classification model is deployed in a browser-based user-friendly dashboard for winemakers and other users, such as wine laboratories. Computer science ; Artificial intelligence ; Machine learning ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies