jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yong Li | Kaixiong Hu | Erica T. Lilleodden | N. Huber
Source: Data in Brief (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Datasets for structural and mechanical properties of nanoporous networks from FIB reconstruction This dataset paper presents a comprehensive archive of 3D tomographic reconstruction image files, volume mesh files for finite element simulations, and tabulated structural and mechanical properties data of nanoporous gold structures. The base material is nanoporous gold, fabricated using a dealloying process, with a solid fraction of approximately 0.30. The NPG samples with ligament sizes ranging from 20 nm to 400 nm were prepared by dealloying and by controlling the thermal annealing process. The original data consist of tomographic TIFF files acquired through Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) 3D reconstruction, as detailed in Philosophical Magazine 2016 96 (32-34), 3322-3335. At each ligament size, six sets of 3D tomographic images were obtained from different regions of the same sample to ensure representative data. New simulations and analyses were conducted based on the 3D image data. The resulting structural and mechanical property data of nanoporous gold are reported for the first time in this dataset paper. Volume meshing of the 3D reconstructed data was performed using Simpleware software. Structural parameters, including surface area, solid volume, and solid volume fraction of the nanoporous network, were extracted from the meshed volumes. Structural connectivity was assessed from the 3D microstructures. The meshed volumes were then used as input for finite element simulations performed in Abaqus to evaluate mechanical responses under uniaxial compression along all three principal axes respectively. From the resulting stress-strain curves, the Young's modulus and yield strength of each structure were determined. Both elastic and plastic Poisson's ratios were analyzed from true strain increments. This dataset includes the 3D tomographic images, corresponding volume mesh files, mechanical behavior data and tables summarizing the structural and mechanical properties. The archived data serve as a database for nanoporous network materials and can be reused for numerical simulations, additive manufacturing, and machine learning applications within the materials science community. All files are openly accessible via the TORE repository at https://doi.org/10.15480/882.15230. Nanoporous ; Finite element method ; Volume fraction ; Volume (thermodynamics) ; Iterative reconstruction ; Elastic modulus ; Compression (physics) ; 3D reconstruction Nanoporous metals and alloys ; Anodic Oxide Films and Nanostructures ; Advanced Electron Microscopy Techniques and Applications
Authors: Weilong Chen | Franz Görlich | Paul Fuchs | Julija Zavadlav
Source: Journal of Chemical Theory and Computation (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer time scales that are otherwise infeasible with atomistic models. Machine learning potentials (MLPs), with their capacity to capture many-body interactions, can provide accurate approximations of the potential of mean force (PMF) in CG models. Current CG MLPs are typically trained in a bottom-up manner via force matching, which in practice relies on configurations sampled from the unbiased equilibrium Boltzmann distribution to ensure thermodynamic consistency. This convention poses two key limitations: first, sufficiently long atomistic trajectories are needed to reach convergence; and second, even once equilibrated, transition regions remain poorly sampled. To address these issues, we employ enhanced sampling to bias along CG degrees of freedom for data generation and then recompute the forces with respect to the unbiased potential. This strategy simultaneously shortens the simulation time required to produce equilibrated data and enriches sampling in transition regions, while preserving the correct PMF. We demonstrate its effectiveness on the Müller-Brown potential and capped alanine, achieving notable improvements. Our findings support the use of enhanced sampling for force matching as a promising direction to improve the accuracy and reliability of CG MLPs. Sampling (signal processing) ; Key (lock) ; Degrees of freedom (physics and chemistry) ; Reliability (semiconductor) ; Matching (statistics) ; Molecular dynamics ; Force field (fiction) ; Potential of mean force Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Mehdi Eslamifar | Hamed Tavakoli | Eiko Thiessen | Rainer Kock | Peter Lausen | Eberhard Hartung
Source: Sensors (article, 2025-11-04)
Field: Chemistry — Analytical Chemistry

Low-Cost NIR Spectroscopy Versus NMR Spectroscopy for Liquid Manure Characterization Accurate characterization of liquid manure properties, such as dry matter (DM), total nitrogen (TN), ammonium nitrogen (NH<sub>4</sub>-N), and total phosphorus (TP), is essential for effective nutrient management in agriculture. This study investigates the use of near-infrared spectroscopy (NIRS) within the 941-1671 nm range, combined with advanced pre-processing and machine learning techniques to accurately predict the liquid manure properties. The predictive accuracy of NIRS was assessed by comparison with nuclear magnetic resonance (NMR) spectroscopy as a benchmark method. A number of 51 liquid manure samples were analyzed in the laboratory for the reference manure properties and scanned with NIRS and NMR. The NIR data underwent spectral pre-processing, which included two- and three-band index transformations and feature selection. Partial least squares regression (PLSR) and LASSO regression were employed to develop calibration models. According to the results, using cohort-tuned models, NIRS showed fair predictive accuracy for DM (R<sup>2</sup> = 0.78, RPD = 2.15) compared to factory-calibrated NMR (R<sup>2</sup> = 0.68, RPD = 0.81). Factory-calibrated NMR outperformed for chemical properties, with R<sup>2</sup> (RPD) of 0.89 (1.74) for TN, 0.97 (5.70) for NH<sub>4</sub>-N, and 0.95 (2.64) for TP, versus NIRS's 0.66 (1.68), 0.84 (2.45), and 0.84 (2.51), respectively. In this study with 51 samples, two- and three-band indices significantly enhanced NIRS performance compared to raw data, with R<sup>2</sup> increases of 34%, 57%, 25%, and 33% for DM, TN, NH<sub>4</sub>-N, and TP, respectively. Feature selection efficiently reduced NIR spectral dimensionality without compromising the prediction accuracy. This study highlights NIRS's potential as a portable tool for on-site manure characterization, with NMR providing superior laboratory validation, offering complementary approaches for nutrient management. Partial least squares regression ; Near-infrared spectroscopy ; Manure ; Spectroscopy ; Chemometrics ; Calibration ; Characterization (materials science) ; Nuclear magnetic resonance spectroscopy ; Analytical Chemistry (journal) Spectroscopy and Chemometric Analyses ; Composting and Vermicomposting Techniques ; Soil Geostatistics and Mapping
Authors: Shuyao Lin | Zhuo Chen | Rebecca Janknecht | Zaoli Zhang | Lars Hultman | P.H. Mayrhofer | Nikola Koutná | Davide G. Sangiovanni
Source: Acta Materialia (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Machine-learning potentials predict orientation- and mode-dependent fracture in refractory diborides Fracture toughness ( K Ic ) and fracture strength ( σ f ) are key criteria in the selection and design of reliable ceramics. However, their experimental characterization remains challenging—especially for ceramic thin films, where size and interfacial effects hinder accurate and reproducible measurements. Here, machine-learning interatomic potentials (MLIPs) trained on ab initio datasets of single crystal models deformed up to fracture are used to characterize transgranular cleavage in pre-cracked ceramic diboride TMB 2 (TM = Ti, Zr, Hf) lattices through stress intensity factor ( K )-controlled loading. Mode-I simulations performed across distinct crack geometries show that fracture is primarily driven by straight crack extension along the original plane. The corresponding macroscale fracture-initiation properties ( K Ic ≈ 1 . 7 – 2 . 9 MPa ⋅ m , σ f ≈ 1 . 6 – 2 . 4 GPa ) are extrapolated using scaling laws previously established for monocrystal ceramics. Considering TiB 2 as a representative system, additional simulations explore loading conditions ranging from pure Mode-I (opening) to Mode-II (sliding). TiB 2 models containing prismatic cracks exhibit their lowest fracture resistance under mixed-mode conditions, where the crack deflects onto pyramidal planes—as confirmed by nanoindentation tests on TiB 2 (0001) thin films. This study establishes K -controlled, MLIP-based simulations as predictive tools for orientation- and mode-dependent fracture in ceramics. Fracture toughness ; Nanoindentation ; Transgranular fracture ; Ceramic ; Fracture (geology) ; Characterization (materials science) ; Stress intensity factor ; Fracture mechanics ; Refractory metals Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Machine Learning in Materials Science
Authors: Tom Reclik | Setareh Medghalchi | Peter Schumacher | Maximilian A. Wollenweber | Talal Al‐Samman | S. Korte-Kerzel | U. Kerzel
Source: Materials & Design (article, 2025-04-16)
Field: Materials Science — Surfaces, Coatings and Films

Resolution enhancement of scanning electron micrographs using artificial intelligence Microscopic imaging of materials often requires the examination of large sample areas at high magnification to identify and analyse rare structural features. High-resolution imaging in scanning electron microscopy is particularly time-intensive, as images are acquired through sequential scanning of an electron beam across the sample surface. This presents a critical challenge, as researchers must balance imaging speed and resolution while ensuring statistically meaningful observations of sparsely distributed features. To address these challenges, we present a novel resolution enhancement method for electron microscopy based on artificial intelligence. Suitable reference images are selected using vector embeddings and processed by a texture-transformer network. Using a tailored dataset of dual-phase steel micrographs, we demonstrate that our trained network outperforms traditional interpolation methods in both quantitative similarity metrics and crucial material-specific features, such as phase boundaries and microstructural voids. The method's transferability is validated using micrographs from a 16MnCrS5 case-hardening steel sample. We achieve a 16-fold acceleration through resolution trade-offs between target and recording and additionally propose a scan-enhance-rescan workflow where resolution-enhanced micrographs guide the identification of regions of interest for targeted high-resolution rescanning. We provide quantitative estimates of expected time savings, offering a practical framework for efficient high-resolution microscopy across large areas. • AI-based resolution enhancement method enables 16-fold faster SEM imaging. • Workflow of deep learning super resolution and vector embeddings. • Trained network preserves small scale microstructural details. • Application to two industrially relevant steels with different microstructures. • Proposed application in a scan-enhance-rescan workflow. Materials science ; Micrograph ; Electron micrographs ; Scanning electron microscope ; Resolution (logic) ; Nanotechnology ; Artificial intelligence ; Optics ; Electron microscope ; Composite material ; Computer science Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Industrial Vision Systems and Defect Detection
Authors: Annabel S. J. Eardley-Brunt | Thomas Mills | Rafail Kontronias | Giovanni Luigi De Maria | Keith M. Channon | The Oxford Acute Myocardial Infarction OxAMI Study | Claire Vallance
Source: The Analyst (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Prediction of clinical outcomes of ST-elevated myocardial infarction patients using atmospheric solids analysis probe mass spectrometry and machine learning <i>Introduction</i>: Analysis of small molecule metabolites found in blood plasma of patients undergoing treatment for STEMI has the potential to be used as a clinical diagnostic and prognostic tool, capable of predicting disease progression, risk of negative outcomes, and response to treatment. <i>Methods</i>: Rapid mass spectrometry has been used to measure the metabolite profiles of coronary aspirate blood plasma from 288 STEMI patients enrolled in the Oxford Acute Myocardial Infarction (OxAMI) study. Supervised machine learning applied to the mass spectra was used to stratify patients based on clinically relevant variables related to health and treatment response. <i>Results</i>: In this small proof-of-concept study, patient mortality and microvascular obstruction (MVO) were predicted with over 80% accuracy; heart failure diagnosis, ischemic time, peak troponin, and thrombus score were predicted with over 76% accuracy, and creatinine and index of microcirculatory resistance (IMR) were predicted with over 70% accuracy. Using feature-reduction methods, we were able to identify key mass-to-charge (<i>m</i>/<i>z</i>) peaks in the mass spectra that correlated with the assignment to particular patent groups. These may potentially be used in the future as mass spectrometric biomarkers in the development of a diagnostic and prognostic test for STEMI risk. Myocardial infarction ; Thrombus ; Creatinine ; Body mass index ; Heart failure ; Coronary heart disease Mass Spectrometry Techniques and Applications ; Biosimilars and Bioanalytical Methods ; Analytical chemistry methods development
Authors: Tianyu Song | Junkai Deng | Rui Tang | Hongxing Xiao | Xiangdong Ding | Jun Sun
Source: Journal of Materials Research and Technology (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Bi-objective optimization of compressive strength and thermal conductivity for UN-U3Si2 composite fuel based on AI techniques The UN-U 3 Si 2 composite fuel has been developed as a promising accident-tolerant fuel (ATF) due to its superior thermal conductivity and higher uranium density compared to conventional UO 2 . To further reduce accident risks, it is highly desirable to optimize the physical properties of ATF fuel, such as compressive strength (CS) and thermal conductivity (TC). Tailoring the microstructure offers an effective approach to achieving multi-objective optimization of the composite fuel, ensuring a balanced trade-off between key properties. In this study, a relationship between the microstructure of UN-U 3 Si 2 composite fuel and the associated CS and TC was established via a convolutional neural network (CNN). To address the challenge of data insufficiency, a dataset of 15,000 microstructure-property pairs was generated through the high-throughput finite element method (FEM). Using reconstructed metallographic images as input, the CNN models achieved a prediction of the CS or TC within a relative error of 3 %. Moreover, critical features strongly correlated with the CS and TC of composites were identified through the saliency map method analysis and Pearson correlation coefficient (PCC) evaluation. Finally, a bi-objective optimization strategy was employed to design microstructures for UN-U 3 Si 2 composite fuel pellets that effectively balance CS and TC properties. This work not only provides practical guidelines for designing advanced ATF fuels with improved performance but also introduces a robust workflow for the multi-objective optimization of composite materials with superior physical properties. Materials science ; Composite number ; Thermal conductivity ; Composite material ; Compressive strength Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Samy Yousef | Javad Hashemibeni | Justas Eimontas | Nerijus Striūgas | Giedrius Janušas | Asta Bronušienė | Mohammed Ali Abdelnaby
Source: Journal of Analytical and Applied Pyrolysis (article, 2025-10-23)
Field: Chemistry — Inorganic Chemistry

Pyrolysis characteristics of metal-organic frameworks: TG-FTIR, TG-GC-MS, kinetic, thermodynamic and artificial neural network modelling Pyrolysis ; Thermogravimetric analysis ; Graphene ; Molecule ; Carbon fibers ; Elemental analysis ; Oxide ; Infrared spectroscopy ; Nanomaterials Metal-Organic Frameworks: Synthesis and Applications ; Thermal and Kinetic Analysis ; Free Radicals and Antioxidants
Authors: Kaixuan Sun | Wenqi Zhang | Ping Hao | Fangcheng Lü | Sidi Fan
Source: Journal of Physics D Applied Physics (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Data-driven prediction of insulation properties in dielectric composites by machine learning Abstract Materials exhibiting superior thermal and electrical insulation characteristics are highly sought after for the development of electrical power systems and electronic equipment. Nevertheless, the composition and architecture complicatedly influence the insulation performance due to the vast array of design variables. Conventional design methods approaches typically involve extensive trial-and-error experiments, necessitating substantial investment and effort to achieve optimal results. The simulation of electrical trees using the finite element method (FEM) serves as a crucial metric for assessing insulation characteristics. A prolonged evolution period of the electrical tree is indicative of superior insulation performance. This approach provides accurate simulations, albeit at a significant computational cost. In this study, we introduce a machine learning (ML) model that enables the precise and efficient prediction of the development time of electrical trees in dielectric composites. The linear and nonlinear ML models are thoroughly trained, including both ElasticNet and a multi-layer perceptron (MLP), leveraging a dataset constructed from FEM simulations. The ElasticNet model demonstrates minimal prediction errors, offering a viable substitute for the FEM model and significantly improving the accuracy of predictions. In addition, an analysis is conducted to determine the influence of each input feature on the predictive outcomes, providing critical insights that facilitate and improve the design process of dielectric composites. To substantiate its effectiveness, the ElasticNet model has been employed to forecast existing experimental outcomes, thereby confirming its practicality and dependability.&amp;#xD; Composite material ; Materials science ; Dielectric High voltage insulation and dielectric phenomena ; Smart Materials for Construction ; Dielectric materials and actuators
Authors: D. Li | Jiyuan Yang | Xiangkai Chen | Li‐Rong Yu | Shi Liu
Source: arXiv (Cornell University) (preprint, 2025-03-11)
Field: Materials Science — Materials Chemistry

Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics? Machine learning force fields have emerged as promising tools for molecular dynamics (MD) simulations, potentially offering quantum-mechanical accuracy with the efficiency of classical MD. Inspired by foundational large language models, recent years have seen considerable progress in developing foundational atomistic models, sometimes referred to as universal force fields, designed to cover most elements in the periodic table. This Perspective adopts a practitioner's viewpoint to ask a critical question: Are these foundational atomistic models reliable for one of their most compelling applications, in particular simulating finite-temperature dynamics? Instead of a broad benchmark, we use the canonical ferroelectric-paraelectric phase transition in PbTiO$_3$ as a focused case study to evaluate prominent foundational atomistic models. Our findings suggest a potential disconnect between static accuracy and dynamic reliability. While 0 K properties are often well-reproduced, we observed that the models can struggle to consistently capture the correct phase transition, sometimes exhibiting simulation instabilities. We believe these challenges may stem from inherent biases in training data and a limited description of anharmonicity. These observed shortcomings, though demonstrated on a single system, appear to point to broader, systemic challenges that can be addressed with targeted fine-tuning. This Perspective serves not to rank models, but to initiate a crucial discussion on the practical readiness of foundational atomistic models and to explore future directions for their improvement. Perspective (graphical) ; Point (geometry) ; Cover (algebra) ; Phase (matter) ; Field (mathematics) ; Molecular dynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: Morteza Mehrabian | Javad Mahmoudimehr
Source: Energy Conversion and Management (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Optimizing steam-to-carbon and air-to-carbon ratios in internal autothermal reforming of a biogas-fed solid oxide fuel cell using the combination of computational fluid dynamics and artificial intelligence Steam reforming ; Exothermic reaction ; Hydrogen production ; Methane reformer ; Solid oxide fuel cell ; Combustion ; Hydrogen ; Endothermic process Advancements in Solid Oxide Fuel Cells ; Catalysts for Methane Reforming ; Chemical Looping and Thermochemical Processes
Authors: Cheng Shen | Yuecheng Qi | Lijun Yun | Xu Zhang | Zaiqing Chen
Source: Frontiers in Plant Science (article, 2025-03-05)
Field: Chemistry — Analytical Chemistry

DBY-Tobacco: a dual-branch model for non-tobacco related materials detection based on hyperspectral feature fusion The removal of non-tobacco related materials (NTRMs) is crucial for improving tobacco product quality and consumer safety. Traditional NTRM detection methods are labor-intensive and inefficient. This study proposes a novel approach for real-time NTRM detection using hyperspectral imaging (HSI) and an enhanced YOLOv8 model, named Dual-branch-YOLO-Tobacco (DBY-Tobacco). We created a dataset of 1,000 images containing 4,203 NTRMs by using a hyperspectral camera, SpectraEye (SEL-24), with a spectral range of 400-900 nm. To improve processing efficiency of HSIs data, three characteristic wavelengths (580nm, 680nm, and 850nm) were extracted by analyzing the weighted coefficients of the principal components. Then the pseudo color image fusion and decorrelation contrast stretch methods were applied for image enhancement. The DBY-Tobacco model features a dual-branch backbone network and a BiFPN-Efficient-Lighting-Feature-Pyramid-Network (BELFPN) module for effective feature fusion. Experimental results demonstrate that the DBY-Tobacco model achieves high performance metrics, including an F1 score of 89.7%, mAP@50 of 92.8%, mAP@50-95 of 73.7%, and a processing speed of 151 FPS, making it suitable for real-time applications in dynamic production environments. The study highlights the potential of combining HSI with advanced deep learning techniques for improving tobacco product quality and safety. Future work will focus on addressing limitations such as stripe noise in HSI and expanding the detection to other types of NTRMs. The dataset and code are available at: https://github.com/Ikaros-sc/DBY-Tobacco. Hyperspectral imaging ; Dual (grammatical number) ; Fusion ; Feature (linguistics) ; Tobacco use ; Biology ; Botany ; Artificial intelligence ; Computational biology ; Computer science ; Medicine ; Environmental health Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies