jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Koorosh Delavari | Taejoon Park | Myoung‐Gyu Lee | Mostafa Elnaggar | Srikanth Pilla | Gang Li | Farhang Pourboghrat
Source: Polymer Composites (article, 2025-12-08)
Field: Materials Science — Polymers and Plastics

Finite Element Analysis of Mathematically Generated Model of Rib 1 × 1 Weft‐Knitted Fabric Under Uniaxial Tension ABSTRACT Different fiber‐reinforcement structures in composites cause varied mechanical responses by affecting how stress is distributed throughout the sample under various loading conditions. Dry weft‐knitted fabric, characterized by its high stretch rate, exhibits different behaviors when used as a standalone fabric compared to when it is used as reinforcement in composites. The high‐rate elongation property of weft‐knitted fabric arises from the yarns' interaction and the loops' geometry within the fabric, not from the material used. This study examines the tensile behavior of dry weft‐knitted fabrics, intended to serve as reinforcement structures in composite materials. To analyze the impact of this structure, a mathematical model representing the 3D geometry of a Rib 1 × 1 weft‐knitted fabric was developed. The fabric geometry was modeled using Python scripting of the mathematical equations within the ABAQUS FE software for analyzing the modeled geometries. An experimental tensile test was conducted on a commercial Rib 1 × 1 weft‐knitted fabric in both the longitudinal and transverse directions. The fabric exhibited average elongations at break of 262% and 71%, and average maximum tensile forces of 132.8 and 395.6 N in the transverse and longitudinal directions, respectively. Remarkably, the mechanical response and deformation patterns of the numerical and experimental samples closely matched in these tests. Using mathematical models to recreate the complex geometries of these reinforcements in the dry fabric facilitated the accurate prediction of their mechanical responses using numerical analysis. These models are ideally suited for efficiently generating data to train machine learning and artificial intelligence models in the future. Ultimate tensile strength ; Finite element method ; Reinforcement ; Transverse plane ; Python (programming language) ; Deformation (meteorology) ; Tensile testing ; Tension (geology) ; Mathematical model Textile materials and evaluations ; Mechanical Behavior of Composites ; Advanced Sensor and Energy Harvesting Materials
Authors: Yalan Li | Piaopiao Yang | Huixian Qing | Zhisheng Xie | Kexin Zhou | Lili Sun | Meiling Chen | Meng Wang | Xiaoliang Ren
Source: Biomedical Chromatography (article, 2025-08-19)
Field: Chemistry — Spectroscopy

A Multimodal Strategy for Quality Control and Botanical Origin Traceability of <i>Codonopsis</i> Radix based on HPLC Fingerprints With Chemometric Analysis Codonopsis Radix (CR) is widely used in the food and pharmaceutical industries. However, the existence of three botanical origins in CR makes it challenging to ensure the quality of CR in the market. In this study, a method was developed to identify and analyze CR from different botanical origins, and their signature components were quantitatively compared. HPLC fingerprints of 55 CR batches demonstrated similarities ranging from 0.707 to 0.994. Chemometrics was used to analyze the different botanical origins of CR and identify significant components affecting their classification. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) indicated that 55 CR batches could be divided into three groups according to their botanical origins. Partial least squares discriminant analysis (PLS-DA) was applied to confirm classification results and obtain the chemical markers. In addition, the reliability of the markers was revalidated by counter propagation artificial neural networks (CP-ANN). The content determination results showed significant differences in the contents of adenosine, eleutheroside B, tangshenoside I, and lobetyolin in the three kinds of CR. The methods and results of this study provide a reliable basis for the identification and rational utilization of CR. Codonopsis ; Chemistry ; Radix (gastropod) ; Chromatography ; Traceability ; High-performance liquid chromatography ; Mathematics ; Statistics ; Botany ; Traditional Chinese medicine Analytical Chemistry and Chromatography ; Essential Oils and Antimicrobial Activity ; Plant biochemistry and biosynthesis
Authors: Rivaldo Mersis Brilianto | Gunyoung Park | Chul Kim
Source: Journal of Mechanical Science and Technology (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Advanced plug design optimization using machine learning and hot-spinning for boss parts in high-pressure hydrogen vessels (99 MPa) Boss ; Spinning ; Mechanical engineering ; Spark plug ; Engineering ; Materials science ; Computer science Nuclear Materials and Properties ; Mechanical Failure Analysis and Simulation ; Graphite, nuclear technology, radiation studies
Authors: Soumajit Dutta | Cunzhi Zhang | Gustavo Perez Lemus | Juan Pablo | François Gygi | Giulia Galli | Andrew L. Ferguson
Source: ArXiv.org (preprint, 2025-12-03)
Field: Materials Science — Materials Chemistry

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials Silicon carbide (SiC) divacancies are attractive candidates for spin defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. The trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multi-defect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies. Silicon carbide ; Molecular dynamics ; Ab initio ; Stability (learning theory) ; Density functional theory ; Interatomic potential ; Annealing (glass) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Xiaojia Chen (1654465) | Min Nian (5467748) | Feng Zhao (90241) | Yu Ma (111665) | Jingzhi Yao (6498743) | Siyi Wang (1412581) | Xing Chen (140292) | Dan Li (106345) | Mingliang Fang (1343559)
Source: Figshare (article, 2025-04-04)
Field: Materials Science — Polymers and Plastics

Artificial Intelligencefor the Discovery of Safeand Effective Flame Retardants Organophosphorus flame retardants (OPFRs) are important chemical additives that are used in commercial products. However, owing to increasing health concerns, the discovery of new OPFRs has become imperative. Herein, we propose an explainable artificial intelligence-assisted product design (AIPD) methodological framework for screening novel, safe, and effective OPFRs. Using a deep neural network, we established a flame retardancy prediction model with an accuracy of 0.90. Employing the SHapley Additive exPlanations approach, we have identified the Morgan 507 (PN connected to a benzene ring) and 114 (quaternary carbon) substructures as promoting units in flame retardancy. Subsequently, approximately 600 compounds were selected as OPFR candidates from the ZINC database. Further refinement was achieved through a comprehensive scoring system that incorporated absorption, toxicity, and persistence, thereby yielding six prospective candidates. We experimentally validated these candidates and identified compound Z2 as a promising candidate, which was not toxic to zebrafish embryos. Our methodological framework leverages AIPD to effectively guide the discovery of novel flame retardants, significantly reducing both developmental time and costs. Artificial neural network ; Human health ; New product development ; Product (mathematics) Flame retardant materials and properties ; Pesticide Exposure and Toxicity ; Phosphorus compounds and reactions
Authors: Zhentao Lian (15212273) | Jianyong Wei (12035575) | Yuzhuo Liu (9532938) | Zuheng Liu (6574574) | Yumeng Liu (731817) | Maosong Xie (836190) | Yaping Dan (1823746) | Chang-Ching Tu (2808085) | Rui Yang (293531)
Source: Figshare (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Highly ResponsiveDual-Function Deep-Ultraviolet NeuromorphicPhototransistors Based on Silicon Carbide Nanoparticle/2D MoS<sub>2</sub> Heterostructures Two-dimensional (2D) semiconductors have been of great interest for phototransistors and neuromorphic devices in recent years because of their unique optical and electronic properties. However, the detectable spectral range and light absorption efficiency are limited for 2D-semiconductor-based phototransistors. Herein, we report a high-performance deep-ultraviolet (DUV) sensitive phototransistor by integrating molybdenum disulfide (MoS<sub>2</sub>) with silicon carbide nanoparticles (SiC NPs) to form a van der Waals heterostructure (vdWH), which shows ultrahigh responsivity and detectivity, especially in the DUV spectral range. The SiC NPs/few-layer MoS<sub>2</sub> vdWH phototransistor shows a 20-fold enhancement in responsivity (from 9.4 × 10<sup>2</sup> to 1.9 × 10<sup>4</sup> A/W) and 11-fold enhancement in detectivity (from 7.9 × 10<sup>12</sup> to 8.4 × 10<sup>13</sup> cm × Hz<sup>1/2</sup>/W) at 254 nm wavelength, compared to the phototransistor based on few-layer MoS<sub>2</sub> alone. Moreover, the SiC NPs/few-layer MoS<sub>2</sub> vdWH phototransistor also shows higher excitation postsynaptic current (EPSC) and longer retention time of postsynaptic current (PSC) compared to the phototransistor based on few-layer MoS<sub>2</sub> alone. This enables vdWH devices to successfully mimic various biological synaptic functions, including paired-pulse facilitation (PPF), spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-frequency-dependent plasticity, the transition from short-term plasticity (STP) to long-term plasticity (LTP), and long-term depression (LTD) capabilities. The simulation of a deep neural network (DNN) shows that the image inference accuracy based on these SiC NPs/few-layer MoS<sub>2</sub> vdWH neuromorphic phototransistors reaches up to 98.99% even after considering the photoresponsivity variations. The high-performance dual-function neuromorphic optoelectronics based on SiC NPs/MoS<sub>2</sub> vdWH hold great promise for ultrasensitive DUV photodetection, neuromorphic DUV visual sensing, and in-sensor computing applications in a single device. Neuromorphic engineering ; Responsivity ; Photodiode ; Heterojunction ; Silicon carbide ; Silicon 2D Materials and Applications ; Advanced Memory and Neural Computing ; Advanced Fiber Laser Technologies
Authors: Dickson E. Onyechi | Matthew N. Abonyi | Emmanuel Chinagorom Nwadike | Adeoluwa G. Adebusuyi
Source: Next research. (article, 2025-10-17)
Field: Materials Science — Polymers and Plastics

Fabrication, characterization and optimization of bio-based fibreboards from palm fruit fibre and rice husk fibre using RSM and ANN Husk ; Absorption of water ; Response surface methodology ; Thermogravimetric analysis ; Scanning electron microscope ; Compressive strength ; Thermal stability ; Characterization (materials science) Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Additive Manufacturing and 3D Printing Technologies
Authors: Jianan Gao | Ruixuan Chen | Wei Sun | Xiaonan Wang
Source: Inorganics (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Predicting the Coordination Number of Transition Metal Elements from XANES Spectra Using Deep Learning X-ray absorption near-edge structure (XANES) spectra are employed to characterise the coordination numbers of metallic elements within materials. However, conventional XANES analysis methods frequently rely on preconceived assumptions regarding the analysed samples, which may not fully satisfy the requirements of scientific research and industrial applications. To mitigate such reliance, a novel approach based on the Gated Adaptive Network for Deep Automated Learning of Features (GANDALF) is proposed. To effectively extract multi-scale information from the XANES spectrum, the spectrum was segmented into multiple scales. Each segment was fitted using a pseudo-Voigt function, with the absorption edge position. The GANDALF algorithm, a table-based deep learning approach, was employed to model the coordination environment of absorbing elements. The proposed method was validated using a previously published open-access dataset. For vanadium-containing samples, the model achieved R2 values of 0.7837 on test sets with non-integer coordination numbers, whereas the random forest model only achieved 0.6328. Furthermore, our results highlight the significant importance of the post-edge peak when predicting coordination numbers using the full spectrum. XANES ; Coordination number ; Deep learning ; Spectral line ; Absorption (acoustics) ; Absorption spectroscopy ; Key (lock) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Mithony Keng (12343676) | Kenneth M. Merz (1311057)
Source: Figshare (article, 2025-07-08)
Field: Chemistry — Spectroscopy

State EnsembleEnergy Recognition (SEER): A HybridGas-Phase Molecular Charge State Predictor Accurately resolving a three-dimensional structure that corresponds to an experimental mass spectrometry (MS) result is valuable for outcomes such as improved analyte identification, determination of physiochemical properties relating to conformation, analyte impurity testing, and drug chemical integrity analysis. Computational approaches utilizing charge state modeling, conformational sampling, quantum mechanical optimizations, relative energy scoring, and computed ion-neutral collision cross sections (CCS) have historically achieved success at assigning equilibrium structures to ion-mobility MS-derived CCS values. Despite this positive status, there remains a lack of new computational software to achieve higher throughput when modeling large systems. A major adverse impact on computational cost is the general increase in titratable sites with molecular size, which then warrants additional protonation/deprotonation models in order to ensure that the correct charge state is captured. Here, we introduce a user-friendly machine learning program called SEER (<b>S</b>tate <b>E</b>nsemble <b>E</b>nergy <b>R</b>ecognition) to accurately and efficiently predict the equilibrium charge states of MS-relevant ions. We report that for all systems within the test set, SEER successfully captured the lowest relative energy minimum charge states within its top two predicted candidates from an overall average number of ∼ seven titratable sites. Furthermore, the density functional theory optimized geometries for SEER assigned charge states produced CCS experimental errors that are within the acceptable threshold (i.e., ≤3% error) set for this work. The benchmark study compared SEER to two well-established charge state prediction software packages CREST and Epik classic and found that SEER is either on par or better at consistently locating the correct charge states for the test set with competitive efficiency. SEER requires no additional user programming and is readily accessible through the Google Colab platform at https://github.com/mitkeng/SEER. Charge (physics) ; Benchmark (surveying) ; State of charge ; Software ; Ground state ; State (computer science) ; Set (abstract data type) ; Basis set ; Test set Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Weiyu Guo | Long Wang | Chuantao Hou | Yonglong Du | Chao Chen | Daxu Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Generating High-Fidelity Models of Woven Ceramic Matrix Composites Considering Real Void Morphology: Image-Driven Geometry and Damage-Induced Behaviour Void (composites) ; Materials science ; Composite material ; Ceramic ; Ceramic matrix composite ; Morphology (biology) ; Geometry ; Matrix (chemical analysis) ; Fidelity ; Mathematics ; Engineering ; Geology Textile materials and evaluations
Authors: Anastasia Yu. Sholokhova | Т. М. Baygildiev | Ekaterina S. Chichkanova | Svetlana A. Borovikova | Yuri A. Ikhalaynen | И. А. Родин | Dmitriy D. Matyushin
Source: ChemRxiv (preprint, 2025-08-25)
Field: Chemistry — Spectroscopy

HREI-MSDB: Data-science ready database of high resolution electron ionization mass spectra Despite the increasing use of gas chromatography coupled with electron ionization high resolution mass spectrometry (GC-EI-HRMS), only a limited amount of GC-EI-HRMS reference data is currently available. Such data are beneficial not only in combination with a GC-EI-HRMS instrument, but also for computational purposes - for example, for training machine learning models that predict fragmentation under electron ionization conditions. Such models are currently trained and tested on integer m/z data, although they predict the exact elemental composition of ions. High-quality GC-EI-HRMS data will be able to boost the development of such models even if the end-user does not have such an instrument. We present HREI-MSDB: a GC-EI-HRMS mass spectral library with an m/z range of 40, an m/z accuracy of 0.6 mDa, and a resolution (full width at half maximum) of 30,000. The data set includes 329 mass spectra of molecules of various classes (nitrogen-containing heterocycles, aromatic and polyaromatic compounds, alcohols, phenols, and essential oil components). All mass spectra are manually verified, do not contain impurity peaks, and are of high quality. In the future, mass spectra may be posted on MassBank. The data is available online: https://doi.org/10.6084/m9.figshare.29713460. Spectral line ; Ionization ; High resolution ; Electron ; Physics ; Atomic physics ; Database ; Ion ; Computer science ; Geology ; Remote sensing ; Nuclear physics ; Astronomy Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Yongcheng Jiang | Zijing Wei | Gensheng Hu
Source: Signal Image and Video Processing (article, 2025-09-05)
Field: Chemistry — Analytical Chemistry

TGDNet: an efficient detection model for multi-scale tea geometrids in complex scenes Scale (ratio) ; Computer science ; Artificial intelligence ; Computer vision ; Geography ; Cartography Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Smart Agriculture and AI
Authors: Anjaneyulu Nelluru | Srinivas Gaddam | V T Ram Pavan Kumar M | Sriharsha Vikruthi | Naresh Kumar Bhagavatham | Vellaturi Gopikrishna
Source:  (article, 2025-09-17)
Field: Chemistry — Analytical Chemistry

Transforming Dairy Standards: Machine Learning in Milk Quality Prediction This research presents an effective and practical method for predicting essential milk quality parameters—fat, protein, lactose, and somatic cell count—by utilizing mid-infrared (MIR) spectroscopy along with machine learning techniques. Among the models tested, Random Forest Regression (RFR) consistently delivered the most accurate predictions, outperforming both Support Vector Regression (SVR) and Gradient Boosting Machines (GBM), with R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values surpassing 0.90 in most cases. A key strength of this approach lies in its ability to identify the most relevant MIR spectral regions, offering insights into the chemical makeup of milk and enhancing the transparency of the predictive model. Unlike high-cost sensor technologies, this framework relies on routinely collected MIR data, making it a more accessible and cost-effective option for real-time, on-farm milk quality assessment. By combining domain-specific knowledge with statistical learning, the study contributes a reliable and interpretable tool for improving dairy herd management and ensuring consistent milk quality. Boosting (machine learning) ; Support vector machine ; Random forest ; Quality (philosophy) ; Somatic cell count ; Gradient boosting ; Regression ; Predictive modelling ; Regression analysis Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rick Oerder (20298628) | Christian Hölzer (17812554) | Jan Hamaekers (5805014)
Source: Figshare (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

ConfRank+: ExtendingConformer Ranking to ChargedMolecules We present a machine learning model for high-throughput energetic ranking of charged molecular conformers. Based on the ConfRank (Hölzer et al. <i>J. Chem. Inf. Model.</i> <b>2024</b> <i>64</i>, 8909–8925) approach, the model is trained in a pairwise fashion to predict energy differences for pairs of conformers. By conditioning the model on data set embedding vectors, we are able to train our model on two different reference levels simultaneously, allowing for a larger training data set and to emulate multiple reference methods. In particular, we train our model on a large subset of the SPICE 2.0.1 data set with ωB97M-D3(BJ)/def2-TZVPPD range-separated hybrid meta-GGA DFT reference computations and a self-developed conformer data set based on the GEOM data set including r2SCAN-3c references. The result is a single multifidelity model that can reproduce both reference levels up to ML-typical model errors for small- and medium-sized molecules including the following elements: H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I. By including partial atomic charges obtained from the electronegativity equilibration charge model, our model incorporates information about the charge distribution in a molecule, allowing the treatment of charged closed-shell species and explicit treatment of electrostatic interactions. We test the ranking capability of the model on various data sets, paying special attention to molecular charges of −1, 0, 1. Throughout all tests, we find our model to be as accurate as current AIMNet2 and MACE-OFF23(L) models, while requiring an order of magnitude fewer parameters and matching the robustness of the state-of-the-art semiempirical quantum method GFN2-xTB. Robustness (evolution) ; Embedding ; Reference data ; Pairwise comparison ; Test set ; Computation ; Set (abstract data type) ; Charge (physics) ; Data set Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods