jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Massimiliano Gioffrè | Vittorio Gusella | Mircea Grigoriu | Chiara Pepi
Source: International Journal of Mechanical Sciences (article, 2025-07-27)
Field: Materials Science — Materials Chemistry

Size effects on the mechanical behavior of single-layer graphene sheets with geometric defects: A probabilistic and machine learning assisted approach This study investigates the mechanical behavior of defective single-layer graphene sheets, accounting explicitly for variability induced by structural defects and size effects. A comprehensive probabilistic framework is developed to model how defect type (single vacancy, double vacancy, Stone–Wales), defect density (1%–3%), and sheet size (4.82–55.24 nm) influence the elastic modulus and Poisson’s ratio. The methodology combines a nonlinear stick-and-spring mechanical model, Monte Carlo simulations with random defect distributions, and machine learning-based sensitivity analysis (Gradient Boosting regression). Results reveal that defect type strongly governs stiffness degradation—double vacancies causing the largest reductions—while Stone–Wales defects enhance lateral deformation. The elastic moduli decrease linearly with defect density, and smaller sheets exhibit greater anisotropy and variability, whereas sheets larger than 35 nm approach isotropic behavior. Statistical modeling identifies lognormal and Weibull distributions as appropriate for quantifying property variability. The study makes four key contributions: (i) a novel probabilistic treatment of both stiffness and Poisson’s ratio, providing new insights into variability and uncertainty; (ii) systematic quantification of size effects and their interaction with defect-induced variability; (iii) interpretable machine learning-based sensitivity analysis identifying dominant factors influencing mechanical behavior; and (iv) a modular, extensible modeling framework applicable to other 2D materials. The results advance the understanding of defect-driven variability in graphene mechanics and support the development of defect-aware design strategies for graphene-based materials. • Multiscale framework quantifies the impact of defects on graphene elasticity. • SV, DV, and SW defects are studied across multiple sizes and densities. • Monte Carlo sampling captures variability due to random defect distributions. • Gradient boosting identifies dominant factors affecting elastic moduli and Poisson’s ratio. • Lognormal and Weibull models provide statistical descriptions of mechanical variability. Probabilistic logic ; Graphene ; Materials science ; Layer (electronics) ; Composite material ; Structural engineering ; Mechanical engineering ; Engineering drawing ; Computer science ; Artificial intelligence ; Nanotechnology ; Engineering Graphene research and applications ; Thermal properties of materials ; Composite Material Mechanics
Authors: Austin
C. Houston (21461348) | Sumner B. Harris (20634071) | Hao Wang (39217) | Yu-Chuan Lin (459680) | David B. Geohegan (1373514) | Kai Xiao (421646) | Gerd Duscher (1501987)
Source: Figshare (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Atom Identificationin Bilayer Moire Materialswith Gomb-Net Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS<sub>2</sub>-WS<sub>2(1–<i>x</i>)</sub>Se<sub>2<i>x</i></sub> heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern’s local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics. Bilayer ; Atom (system on chip) ; van der Waals force ; Dopant ; Janus ; Heterojunction 2D Materials and Applications ; Topological Materials and Phenomena ; Advanced Sensor and Energy Harvesting Materials
Authors: Wen Cao | Guangyao Huang | Yue Fang | Jingyuan Shao | Shijie Hao | Ying Wang | Tai Lon Tan | Zhenxing Li | M. G. Zhao | Lingwei Hou | Junping Wang | Hongzhi Wang
Source: Biosensors and Bioelectronics (article, 2025-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

PlasmoBridge: Stable hotspot engineering and targeted Surface-enhanced Raman spectroscopy (SERS) monitoring of methotrexate Methotrexate ; Mass spectrometry ; Raman spectroscopy ; Biosensor ; Analyte ; Plasmon ; Surface-enhanced Raman spectroscopy ; Nanoscopic scale Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Yanbang Tang
Source: Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

A systematic framework for predicting irradiation-induced swelling in austenitic stainless steels using automated machine learning Feature (linguistics) ; Ranking (information retrieval) ; Key (lock) ; Mean squared error ; Austenite ; Swelling Machine Learning in Materials Science ; Fusion materials and technologies ; Nuclear Materials and Properties
Authors: Q. Zheng | Boyang Li | Sizhan Liu | Chuntian Cao | Jessica Rimsza | Qiubo Zhang | Jianming Bai | Chun Chang | Jiawei Wang | Chengyao Liang | Haiyan Mao | Matthew R. Carbone | Deyu Lu | Tatiana Pyatina | Haimei Zheng
Source: Science Advances (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Strain release through hydrogen bond–mediated layer twisting Strain engineering, enabling the precise control over structure and functional properties, is a key strategy for the design of advanced materials. However, the mechanisms governing strain evolution and release at the nanoscale remain largely unexplored. In this study, we leverage in situ heating transmission electron microscopy and synchrotron x-ray spectroscopy to investigate the strain relaxation pathways of boehmite (γ-AlOOH) at 575 kelvin by revealing real-time structural dynamics. Through tracking the moiré pattern evolution, we identify distinct strain release mechanisms, including layer twisting, defect formation, and domain restructuring. Our neural network potential calculations reveal that energy fluctuations at small twist angles are dominated by an interference-like interaction modulation of hydrogen bonds between boehmite interlayers, with metastable twisted structures corresponding to local minima of the potential energy landscape. This work establishes a previously unidentified paradigm of two-dimensional layer twisting mediated by hydrogen bonding, offering insights into strain-driven transformation mechanisms, and thus may have broad implications for strain in material and earth sciences. Boehmite ; Strain (injury) ; Transmission electron microscopy ; Hydrogen ; Strain energy ; Metastability ; Overlayer ; Molecular dynamics ; Relaxation (psychology) Microstructure and mechanical properties ; Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis
Authors: Yizhen Luo | Jiashuo Wang | Shuaiwei Fan | Zaiqing Nie
Source: ArXiv.org (preprint, 2025-03-12)
Field: Materials Science — Materials Chemistry

PharMolixFM: All-Atom Foundation Models for Molecular Modeling and Generation Structural biology relies on accurate three-dimensional biomolecular structures to advance our understanding of biological functions, disease mechanisms, and therapeutics. While recent advances in deep learning have enabled the development of all-atom foundation models for molecular modeling and generation, existing approaches face challenges in generalization due to the multi-modal nature of atomic data and the lack of comprehensive analysis of training and sampling strategies. To address these limitations, we propose PharMolixFM, a unified framework for constructing all-atom foundation models based on multi-modal generative techniques. Our framework includes three variants using state-of-the-art multi-modal generative models. By formulating molecular tasks as a generalized denoising process with task-specific priors, PharMolixFM achieves robust performance across various structural biology applications. Experimental results demonstrate that PharMolixFM-Diff achieves competitive prediction accuracy in protein-small-molecule docking (83.9% vs. 90.2% RMSD &lt; 2Å, given pocket) with significantly improved inference speed. Moreover, we explore the empirical inference scaling law by introducing more sampling repeats or steps. Our code and model are available at https://github.com/PharMolix/OpenBioMed. Inference ; Generative grammar ; Generalization ; Foundation (evidence) ; Process (computing) ; Generative model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Wei Guo | Yuanhao Wang | Xiaobin Zhao | Wei Li | Kui Yan | Meihong Deng | G.J. Tang | Xiaoling Yan | Ruixuan Liu | Sung Pil Chang
Source: Journal of Applied Polymer Science (article, 2025-08-11)
Field: Materials Science — Polymers and Plastics

Optimizing Parameters for Microcellular Foaming and In‐Mold Decoration of Talc/ <scp>PP</scp> Composites Using <scp>PSOBP</scp> Neural Network ABSTRACT The growing demand for lightweight and high‐performance materials in the automotive industry has driven the development of microcellular foaming technology, which enables weight reduction and improved material properties. However, injection molding processes often result in warpage, limiting large‐scale applications. This study investigates the effects of varying talc contents (0%, 2%, 4%, 6%, and 8%) in polypropylene (PP) composites on foaming behavior, mechanical properties, and warpage. Results reveal that 4% talc provides the optimal balance between mechanical performance and dimensional stability. Based on these findings, a particle swarm optimization with backpropagation (PSOBP) neural network was used to optimize key parameters, including mold temperature, filling pressure, and cooling time. By analyzing 200 parameter combinations via Moldex3D simulations, the optimal process achieved a 2.1% weight reduction and a 7.2% warpage reduction. Experimental trials validated the simulation results, confirming the method's reliability and industrial feasibility. This study provides a practical framework for optimizing manufacturing efficiency, dimensional accuracy, and lightweighting of automotive components. Materials science ; Molding (decorative) ; Composite material ; Polypropylene ; Mold ; Automotive industry ; Talc ; Die (integrated circuit) ; Artificial neural network ; Computer science ; Engineering ; Nanotechnology Polymer Foaming and Composites ; Injection Molding Process and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Bin Tang | Ye Yuan | Hua Yang | Wenjuan Zhang | Zhiguo Xia | Mi Zhou | Tao Song
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Light fuel classification based on Raman spectroscopy and region-adaptive convolutional neural networks In modern warfare, the demands of individual combat and rapid deployment place increasingly stringent requirements on energy security. The safe and reliable supply of light fuels directly impacts combat effectiveness and strategic success. Therefore, there is an urgent need for a rapid, accurate, and field-adaptable detection method to ensure the security of fuel supply. Among the available techniques, portable Raman spectroscopy, owing to its non-destructive nature, rapid analysis capability, and high sensitivity to molecular structures, has been widely applied in the detection of light fuels. However, in Raman spectroscopy, light fuel samples often exhibit highly similar spectral profiles. Moreover, traditional feature extraction methods suffer from limited capability, and under conditions of low signal-to-noise ratios or strong background interference, they show insufficient sensitivity to weak characteristic peaks, which substantially constrains classification performance. To address this challenge, we collected a total of 16 different grades and sources of light fuels, including gasoline, diesel, and jet fuel, and proposed a novel one-dimensional convolutional neural network (1D-CNN) for fuel classification. The proposed model segments each Raman spectrum into four regions, performs region-specific feature extraction, and assigns initial weights based on the relative peak density of each region. During training, these weights are further optimized through a constraint mechanism that adaptively adjusts regional contributions, thereby guiding the network to focus on the relative importance of peaks within each region and enhancing the model' fine-grained discriminative ability. Experimental results demonstrate that the proposed model achieves an average accuracy of 96.16%, a macro-averaged precision of 96.23%, a macro-averaged recall of 96.08%, and a macro-averaged <i>F</i>1 score of 96.12%, all consistently outperforming those of the other models, while maintaining a relatively low parameter complexity of 0.7 M and a short training time of 226.32 s. Further sensitivity analysis validates that the parameters we selected are indeed optimal. It provides a novel and practical solution for rapid battlefield identification of light fuels and offers valuable insights into the design and application of region-aware 1D deep learning models. Convolutional neural network ; Sensitivity (control systems) ; Discriminative model ; Raman spectroscopy ; Pattern recognition (psychology) ; Feature (linguistics) ; Artificial neural network ; False alarm Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jian Sun | Junmeng Luo | Ming Gao | Fang Wang | Wenjing Nie | Moran Chen | Suming Chen
Source: Angewandte Chemie (article, 2025-07-29)
Field: Chemistry — Spectroscopy

Quantum Chemistry Calculation‐Assisted Large‐Scale Collision Cross Section Prediction Empowers Derivatization‐Enhanced Multidimensional Metabolomics Abstract Derivatization‐enhanced multidimensional metabolomics combined with ion mobility mass spectrometry will greatly improve the accuracy and coverage of metabolic analysis. However, accurate prediction of the large‐scale collision cross section (CCS) of derivatized metabolites without relying on standards and the establishment of multidimensional analytical methods faces great challenges. Here, we propose quantum chemistry calculation‐assisted machine learning strategies applicable to the accurate prediction of the CCS of derivatized sterols, develop C═C bond‐targeted N‐Me derivatization methods for unsaturated sterols, and create a large‐scale, 4D information database of derivatized sterol lipids ( n = 4891) by combining retention time and fragment ion prediction. Furthermore, a high‐coverage unsaturated sterolomics at the isomer level was established on this basis, which quantitatively revealed the tissue‐specific distribution patterns of over 100 sterol lipids. This study provides a key foundation for derivatization‐enhanced metabolomics and provides important techniques and information for metabolic and functional studies of sterols. Chemistry ; Metabolomics ; Section (typography) ; Quantum chemistry ; Derivatization ; Computational chemistry ; Scale (ratio) ; Mass spectrometry ; Nanotechnology ; Chromatography ; Computer science ; Reaction mechanism ; Organic chemistry ; Physics ; Materials science ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Lukas Roming | Felix Kronenwett | Paul Bäcker | Jerardh Josekutty | Georg Maier | Thomas Längle
Source: Waste Management (article, 2025-10-09)
Field: Chemistry — Analytical Chemistry

Black plastic identification by hyperspectral imaging in mid-wave infrared Sensor-based sorters operating in the Near-Infrared (NIR) range are commonly used to sort post-consumer plastics. However, this method fails, when the NIR light is fully absorbed by carbon black pigments, which are present in black plastics. Mid-Wave Infrared (MWIR) is less absorbed by carbon black and therefore provides a promising alternative wavelength range for analyzing black polymers. This study compares MWIR to NIR hyperspectral imaging for classifying black and colored plastic waste. We collected spectral data from five common polymers found in post-consumer packaging, namely High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), Polyethylene Terephthalate (PET), Polypropylene (PP), and Polystyrene (PS). We classified the spectra using several chemometric methods, including a convolutional neural network (CNN). The results quantitatively verify the superior performance of MWIR over NIR for classifying black plastics. MWIR achieved a balanced accuracy of 83.4% compared to 47.5% for NIR, when using a CNN, which outperformed other chemometric methods for all sensors and sample sets. On the other hand, NIR surpasses MWIR by 7 percentage points for colored plastics in balanced accuracy. These findings suggest that MWIR hyperspectral imaging is an effective alternative to NIR hyperspectral imaging for sorting post-consumer packaging waste, especially when the share of black plastics is high. Hyperspectral imaging ; Carbon black ; Polypropylene ; Polyethylene terephthalate ; Polyethylene ; Near-infrared spectroscopy ; Spectral signature ; Polystyrene Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Industrial Vision Systems and Defect Detection
Authors: K. H. M. Ali | Luttfi A. Al-Haddad
Source: Discover Materials (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Data-driven prediction of thermal and thermoelectric performance in GeTe-Sb2Te3 systems: extreme learning of deep neural networks Abstract It is essential to accurately predict thermoelectric properties for the rational design of high-efficiency energy conversion materials. In this study, a data-driven model based on extremely learned Deep Neural Networks (DeepELM-DNNs) was developed to forecast the power factor which is a key thermoelectric performance indicator for two polycrystalline systems: (GeTe)₁₀Sb₂Te₃ and (GeTe)₂₄Sb₂Te₃. Three DeepELM-DNN models were implemented and tested using experimentally collected features, namely seebeck coefficient, electrical resistivity, and temperature datasets. The models were assessed by four metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R²), and Mean Absolute Percentage Error (MAPE). The best model, DeepELM-DNN-3, achieved good performance for (GeTe)₁₀Sb₂Te₃ with MAE = 0.0932, RMSE = 0.1112, R² = 0.9837, and MAPE = 4.3261%, and for (GeTe)₂₄Sb₂Te₃ with MAE = 0.1032, RMSE = 0.1387, R² = 0.9834, and MAPE = 4.9748%. These findings illustrate the robustness of the model to describe zT magnitudes with a manner corresponding to their nonlinearity in temperature and composition for complex telluride materials. The results also reveal that the DeepELM-DNN is highly accurate and efficient in modeling thermoelectric materials, and has the potential to significantly accelerate AI-driven discovery of high-performance heat-to-electricity conversion materials. Mean absolute percentage error ; Mean squared error ; Artificial neural network ; Robustness (evolution) ; Seebeck coefficient ; Approximation error ; Thermoelectric generator ; Thermoelectric effect Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Aleksandr I. Proshunin | Sergei Borzunov | N. I. Selvesyuk
Source: Mathematics in Engineering (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Identification of the parameters of the stochastic Preisach operator In this paper, we propose a method for identifying the measure of the stochastic Preisach operator using machine learning techniques. The classical Preisach operator model is widely used to describe hysteresis phenomena in various fields such as physics, chemistry, economics, and biology. However, it does not account for uncontrolled fluctuations in the parameters of elementary hysteresis carriers — hysterons, which limits its applicability in real systems where these parameters can be stochastic variables. The proposed method is based on sequential reconstruction of the operator's measure on the plane of hysteron threshold parameters $ (\alpha, \beta) $. The identification is carried out through specially constructed input signals that trigger switching of only specific hysterons in given local regions of this plane. Multiple repetitions of such input actions and application of the law of large numbers allow for estimating the mathematical expectations of hysteron state changes. These estimates are used in the algorithm to minimize a loss functional, leading to the reconstruction of the operator's measure from experimental data. The results open new possibilities for modeling and analyzing complex systems with hysteretic properties, taking into account the stochastic nature of parameters. Measure (data warehouse) ; Operator (biology) ; Identification (biology) ; Hysteresis ; Stochastic process ; State (computer science) ; Control theory (sociology) Magnetic Properties and Applications ; Advanced Mathematical Modeling in Engineering ; Shape Memory Alloy Transformations
Authors: James Eastwood
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

What's up with OpenFF? This article provides a six-month update on the scientific progress, infrastructure development, and community activity of the Open Force Field (OpenFF) Initiative, an OMSF-hosted project focused on developing open, high-quality molecular mechanics force fields. The post highlights recent advances in the Sage force field line, the development of neural-network-based partial charge models, new workflows for parameterizing covalently modified proteins, and ongoing collaborations with academic research groups. It also summarizes major infrastructure improvements, including updates to OpenFF’s interoperability tools, benchmarking pipelines, and system-preparation capabilities. In addition to technical progress, the article documents staff transitions, internal process improvements, and in-person collaboration efforts, providing a snapshot of how OpenFF operates as a distributed scientific software project within the OMSF ecosystem. Interoperability ; Workflow ; Benchmarking ; Software ; Field (mathematics) ; Snapshot (computer storage) Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Protein Structure and Dynamics