jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rainer Niekamp | Johanna Niemann | Maximilian Reichel | Hongbin Zhang | Jörg Schröder
Source: Computational Mechanics (article, 2025-03-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An efficient Poisson solver and a data-driven surrogate model for magnetic stray field calculations Abstract In this contribution we propose a data-driven surrogate model for the prediction of magnetic stray fields in two-dimensional random micro-heterogeneous materials. Since data driven models require thousands of training data samples, finite element simulations appear to be too time consuming.To bypass this computational bottleneck, an efficient approach based on Brownian motion and the evaluation of stochastic transition matrices is used to generate a large number of training data in short time. The method presented here is a generalization of an approach that has already been described for the simulation of porous materials with impermeable inclusions. The novel approach is an extension to heterogeneous materials with different magnitudes of permeability. For the encoding of the microstructure and the optimization of the surrogate model, two architectures are compared, i.e., the so-called U-shaped Residual Network (UResNet) model and the Fourier Convolutional Neural Network (FCNN). To demonstrate the workability of the proposed methods numerical examples are employed. Solver ; Magnetic field ; Surrogate model ; Computational Science and Engineering ; Field (mathematics) ; Poisson distribution ; Physics ; Computer science ; Computational science ; Mathematics ; Mathematical optimization ; Statistics Magnetic Properties and Applications ; Magnetic Properties of Alloys ; Magnetic properties of thin films
Authors: Swati Mayur Keny | Malarkodi Velraj | Natarajan Kiruthiga | Anand Krishnan | Mangirish Deshpande | Stalin Arulsamy | Panneerselvam Theivendren
Source: South African Journal of Botany (article, 2025-12-10)
Field: Chemistry — Organic Chemistry

Exploring natural bioactive compounds for breast cancer treatment: Integrating Rubia Cordifolia L. and Strychnine SLNs with traditional treatments through machine learning and nanotechnology Docking (animal) ; Oleanolic acid ; Breast cancer ; Solid lipid nanoparticle ; Random forest ; Cytotoxicity ; Drug ; Drug delivery Synthesis and Characterization of Pyrroles ; Synthesis of Organic Compounds ; Retinoids in leukemia and cellular processes
Authors: Md Omar Faruque | Dil K. Limbu | Nathan London | Mohammad R. Momeni
Source: arXiv (Cornell University) (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

Predictive quantum vibrational spectra through active learning 4G-NNPs Predictive simulation of vibrational spectra of complex condensed-phase and interface systems with thousands of degrees of freedom has long been a challenging task of modern condensed matter theory. In this work, fourth-generation high-dimensional committee neural network potentials (4G-HDCNNPs) are developed using active learning and query-by-committee, and introduced to the essential nuclear quantum effects (NQEs) as well as conformational entropy and anharmonicities from path integral (PI) molecular dynamics simulations. Using representative bulk water and air-water interface test cases, we demonstrate the accuracy of the developed framework in infrared spectral simulations. Specifically, by seamlessly integrating non-local charge transfer effects from 4G-HDCNNPs with the NQEs from PI methods, our introduced methodology yields accurate infrared spectra using predicted charges from the 4G-HDCNNP architecture without explicit training of dipole moments. The framework introduced in this work is simple and general, offering a practical paradigm for predictive spectral simulations of complex condensed phases and interfaces, free from empirical parameterizations and ad hoc fitting. Dipole ; Quantum ; Spectral line ; Interface (matter) ; Artificial neural network ; Infrared ; Molecular dynamics ; Charge (physics) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Murilo Neco Saraiva | Cléber Renato Mendonça
Source:  (article, 2025-09-21)
Field: Materials Science — Materials Chemistry

Random Forest for Chalcogenide Glasses: A Data-Driven Method for Predicting the Nonlinear Refractive Index Chalcogenide glasses exhibit unique properties that make them promising candidates for advanced photonic applications, including nonlinear optics, optical storage, fibers, and optoelectronic devices. However, producing glasses with tailored properties through a “trial and error” approach is time-consuming and costly. In this study, a Random Forest model accurately predicted the nonlinear refractive index of chalcogenide glasses based on their matrix composition, achieving a mean absolute percentage error below 10% on unseen samples. These results highlight the potential of machine learning to accelerate materials development by drastically reducing the dependence on experimental measurements. Chalcogenide ; Refractive index ; Nonlinear system ; Chalcogenide glass ; Random forest ; Photonics ; Nonlinear optics Phase-change materials and chalcogenides ; Glass properties and applications ; Nonlinear Optical Materials Studies
Authors: Guangchen Liu | Songge Yang | Yu Zhong
Source: Materials Today Physics (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

A computational framework for interface design using lattice matching, machine learning potentials, and active learning: A case study on LaCoO3/La2NiO4 Interfaces play a crucial role in determining the stability and functional properties of materials, yet their complex nature poses significant challenges for computational modeling. While Density Functional Theory (DFT) offers accurate insights into interfacial behavior, its high computational cost limits its applicability to large-scale studies. To address this, a scalable computational framework integrating lattice matching algorithms, machine learning potentials, and active learning is developed to enable efficient and accurate interface design. The LaCoO 3 /La 2 NiO 4 interface is employed as a case study to demonstrate the framework's capabilities in systematically exploring interface stability, geometry, and composition. Customized interface structures are generated via lattice matching, and atomic simulations are accelerated using machine learning potentials. Key geometric factors—including interface area, shape, and thickness—are systematically evaluated, while active learning is used to optimize the exploration of interfacial orientations and compositions, enabling efficient identification of low-energy configurations. A predictive model is established, linking interfacial characteristics to stability with DFT-level fidelity at reduced computational cost. This framework offers a versatile, data-driven approach for complex interface modeling and design. Interface (matter) ; Scalability ; Lattice (music) ; High fidelity ; Stability (learning theory) ; Computational model ; Fidelity ; Computational learning theory Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Juhee Ko | Jungchul Lee
Source:  (article, 2025-01-19)
Field: Chemistry — Analytical Chemistry

Liquid Mixture Analysis by Simultaneous Properties Measurements under Temperature Modulation and Deep Learning The precise analysis of liquid mixtures is important for industrial applications, such as product quality control and safety management. Conventional methods, including liquid chromatography-mass spectrometry (LC-MS) and spectroscopic techniques, are limited by the need for destructive sample preparation, reliance on ionization efficiency, or the requirement for distinct calibrations across liquid species. We propose a novel, separation-free approach using a heater-integrated microchannel resonator (HMR) combined with deep learning. The HMR simultaneously measures density (ρ), viscosity (μ), thermal conductivity (k), and specific heat capacity (c<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf>) under temperature modulation. A deep learning model, trained on these mechanical and thermophysical properties, predicts the concentrations of all components in ternary mixtures with an error of less than 2%. We also show that prediction accuracy increases when the model is developed with mechanical and thermophysical properties at various temperatures. This study highlights the feasibility of non- invasive, real-time analysis of liquid mixtures based on material properties, setting a new metrology for liquid composition analysis. Modulation (music) ; Temperature measurement ; Materials science ; Computer science ; Optoelectronics ; Physics ; Acoustics ; Thermodynamics Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies
Authors: Weifeng Wan | Xiuqi Xu | Yu Cheng | Haoyun Zhang | Xiaojun Yan
Source: International Journal of Fatigue (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Revealing and Predicting Fatigue Crack Nucleation in Textured Zircaloy-4 via Full-Field Experiments and Physics-Informed Machine Learning Texture (cosmology) ; Pattern recognition (psychology) ; Deformation (meteorology) ; Nucleation Nuclear Materials and Properties ; Machine Learning in Materials Science ; Advanced ceramic materials synthesis
Authors: Haitao Wu (2411155) | Hao Wang (39217) | Changcheng Wang (1457353) | Zhaoyang Yuan (7217516) | Hu Xu (2160514) | Jing Zheng (65946) | Mengjin Jiang (1396993) | Jinrong Wu (2036902)
Source: Figshare (article, 2025-05-02)
Field: Materials Science — Polymers and Plastics

Machine-Learning-AssistedDesign of Mechanically RobustRoom-Temperature Self-Healing Epoxy Resins Epoxy resins are the most widely used thermosets, yet they typically lack the capability to self-heal at room temperature due to their molecular chains and networks being immobilized in a glassy state. Herein, machine learning identifies fractional free volume as a crucial factor for enabling self-healing in the glassy state. Guided by this insight, we designed an epoxy network incorporating dangling chains together with numerous hydrogen bonds and aromatic disulfide bonds. The dangling chains introduce large free volume, facilitating the reorganization of hydrogen bonds and the radical-mediated exchange of aromatic disulfide bonds, thereby imparting prominent self-healing capability at room temperature. Notably, the damaged epoxy not only can recover 81.2% of its tensile strength without intervention but also can autonomously and completely eliminate electrical tree damage and scratches at room temperature. Under mild compression, 100% healing occurs within tens of minutes in the glassy state. Additionally, the optimized epoxy exhibits high physicomechanical properties with a tensile strength of 42.1 MPa, a modulus of 2.9 GPa, and a glass transition temperature of 53.2 °C. Its ability to self-heal both electrical tree and mechanical damage at room temperature positions this epoxy as a promising material for advanced insulating and sealing applications. Epoxy ; Ultimate tensile strength ; Composite epoxy material ; Glass transition ; Modulus ; Hydrogen bond ; Dynamic mechanical analysis Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Flame retardant materials and properties
Authors: Lelin Qian | Zhangli Yu | Yuan Zhang | Jingyi Huang | Daixin Ye | Lili Niu | Juan Zhang
Source: Microchimica Acta (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Identification of cariogenic bacteria by click chemistry mediated polyethylene glycolized graphyne nanozymes Click chemistry ; Identification (biology) ; Chemistry ; Bacteria ; Graphyne ; Nanotechnology ; Computational biology ; Biology ; Materials science ; Combinatorial chemistry ; Botany ; Computational chemistry ; Genetics ; Density functional theory Advanced Nanomaterials in Catalysis ; Oral microbiology and periodontitis research ; Advanced biosensing and bioanalysis techniques
Authors: Dong Zhang | Liqiang Zhang | Jinchao Ji | Wang Li | Jingrui Zhang | Yongqian Shen
Source: physica status solidi (a) (article, 2025-09-30)
Field: Materials Science — Surfaces, Coatings and Films

Micro‐ and Nanostructured Surfaces are Structured by a Femtosecond Laser toward Superhydrophobicity: A Review With rapid development of Industry 4.0 and AI 2.0, autonomous optimization and dynamic control of complex process parameters have been enabled by data‐driven paradigms such as deep learning and reinforcement learning. Superhydrophobic surfaces have found applications in aerospace, transportation, microfluidics, and new energy. These surfaces are fabricated by sol–gel processing, chemical vapor deposition, template‐based techniques, and femtosecond laser processing. Femtosecond laser processing is valued for wide material applicability, high processing efficiency, and flexibility and is regarded as promising. The principles are described. The microscopic mechanisms of interaction between femtosecond lasers and matter are analyzed. Clarification is provided by this analysis regarding how precise micro‐nanoprocessing is achieved, thereby enabling superhydrophobic properties on these surfaces. A systematic analysis of application cases is conducted based on the wetting theory. Three representative fields—SiC, metals, and photovoltaic modules—are considered, with implementations demonstrated, where surface structuring enables controllable wettability. The advantages of femtosecond laser processing in superhydrophobic fabrication are summarized. The influence of laser‐induced morphology on wettability is elucidated. Progress is reviewed in four applications: oil–water separation, self‐cleaning, de‐icing, and photovoltaic‐panel cleaning. Current technical challenges are analyzed, and future trends are predicted via performance comparisons. Femtosecond ; Laser ; Flexibility (engineering) ; Fabrication ; Wetting ; Structuring ; Materials processing Surface Modification and Superhydrophobicity ; Laser Material Processing Techniques ; Fluid Dynamics and Heat Transfer
Authors: Andreas Niederquell | Barbora Vraníková | Martin Kuentz
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Ranking of Apparent Drug Affinity to Mesoporous Silica Utilizing a Chromatographic Screening Method and a Tree-Based Prediction Model Ranking (information retrieval) ; Chromatography ; Drug ; Decision tree ; Tree (set theory) ; Mesoporous silica ; Chemistry ; Computer science ; Mathematics ; Mesoporous material ; Data mining ; Machine learning ; Pharmacology ; Organic chemistry ; Medicine ; Combinatorics Analytical Chemistry and Chromatography ; Protein purification and stability ; Drug Solubulity and Delivery Systems
Authors: H. J. Ehrich | Marvin Carl May | Stefan J. Eder
Source: ArXiv.org (preprint, 2025-12-05)
Field: Materials Science — Materials Chemistry

Machine-learning-enabled interpretation of tribological deformation patterns in large-scale MD data Molecular dynamics (MD) simulations have become indispensable for exploring tribological deformation patterns at the atomic scale. However, transforming the resulting high-dimensional data into interpretable deformation pattern maps remains a resource-intensive and largely manual process. In this work, we introduce a data-driven workflow that automates this interpretation step using unsupervised and supervised learning. Grain-orientation-colored computational tomograph pictures obtained from CuNi alloy simulations were first compressed through an autoencoder to a 32-dimensional global feature vector. Despite this strong compression, the reconstructed images retained the essential microstructural motifs: grain boundaries, stacking faults, twins, and partial lattice rotations, while omitting only the finest defects. The learned representations were then combined with simulation metadata (composition, load, time, temperature, and spatial position) to train a CNN-MLP model to predict the dominant deformation pattern. The resulting model achieves a prediction accuracy of approximately 96% on validation data. A refined evaluation strategy, in which an entire spatial region containing distinct grains was excluded from training, provides a more robust measure of generalization. The approach demonstrates that essential tribological deformation signatures can be automatically identified and classified from structural images using Machine Learning. This proof of concept constitutes a first step towards fully automated, data-driven construction of tribological mechanism maps and, ultimately, toward predictive modeling frameworks that may reduce the need for large-scale MD simulation campaigns. Workflow ; Tribology ; Deformation (meteorology) ; Autoencoder ; Interpretation (philosophy) ; Feature (linguistics) ; Stacking Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Ghefira Shofa Imriane | Laily Ade Oktaviana | Fathur Rahman | Maulid Purnawan | Dita Puspitasari
Source:  (article, 2025-08-27)
Field: Materials Science — Polymers and Plastics

Deep Learning-Based Segmentation for Porosity Analysis of PMMA from SEM Images This study analyses pore size, porosity, and SEM image enhancement of Poly(methyl methacrylate) (PMMA) using contrast adjustment. It evaluates the performance of three segmentation methods: Thresholding, U-Net, and ResUNet. The SEM image contrast was adjusted beforehand to enhance pore visibility, thereby facilitating the segmentation process and porosity calculation. ImageJ was used as the manual benchmark, yielding a porosity value of 25.785%. Deep Learning image segmentation methods yielded varying results: Thresholding achieved 23.75%, ResUNet achieved 24.49%, and U-Net achieved 17.61%. Although the result from Thresholding is numerically close to the benchmark, ResUNet outperforms the other methods by providing more accurate and consistent pore structure segmentation. ResUNet also demonstrated the closest match in terms of average and minimum pore width, thereby avoiding the oversegmentation observed in Thresholding and the undersegmentation found in U-Net. These results highlight the potential of ResUNet as a reliable and scalable tool for automated porosity analysis in PMMA, especially for detecting fine structural features. The study emphasises the importance of adopting deep learning-based segmentation to enhance material characterisation and suggests further refinement of model generalisation for broader applicability. Thresholding ; Segmentation ; Porosity ; Image segmentation ; Pattern recognition (psychology) ; Image (mathematics) Polymer Foaming and Composites ; Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques
Authors: Surbhi
K. A. Kumar (22404618) | Dorina F. Sava Gallis (7296647) | David S. Sholl (1270002)
Source: Figshare (article, 2025-10-09)
Field: Chemistry — Inorganic Chemistry

Computational Analysisof the Energetic Stabilityof High-Entropy Structures of a Prototypical Lanthanide-Based Metal–OrganicFramework High-entropy materials are characterized by their complex compositions, typically comprising five or more elements in near-equiatomic proportions. Applying this concept to metal ions in metal–organic frameworks (MOFs) has paved the way for exploring a new class of high-entropy MOFs. While the compositional strategy of high-entropy materials leverages configurational entropy to aid thermodynamic stability, it also poses significant analytical challenges due to the vast compositional landscape and diverse phases that these materials can adopt. We present a computational study of several complexities associated with selecting potential high-entropy versions of a prototype lanthanide-based MOF. We compute the energetics of metal mixing of these heterometallic MOFs using density functional theory (DFT) and machine learning interatomic potential (MLIP) methods. The use of MLIP methods allows a systematic exploration of the convex hull of thermodynamically stable MOF structures containing up to 5 distinct metals. Stability (learning theory) ; Entropy (arrow of time) ; Convex hull ; Density functional theory ; Interatomic potential ; Mixing (physics) ; Regular polygon Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Alaa M. Elsayad | Khaled A. Elsayad
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Comprehensive Qsar and Explainable Ai for Biodegradability: A Study with 2d and 3d Molecular Descriptors Quantitative structure–activity relationship ; Artificial intelligence ; Computational biology ; Chemistry ; Computer science ; Stereochemistry ; Biology Spectroscopy and Chemometric Analyses
Authors: Serine Amina Benhacine | Said Labed | Akram Kout
Source:  (article, 2025-12-03)
Field: Chemistry — Analytical Chemistry

Early potato disease detection using spectral imaging and machine learning techniques: a review Potato is a globally important staple crop, requiring rapid and accurate disease detection to protect yield and quality. Phytopathological threats such as Root-Knot Nematodes, Early Blight, and Late Blight can spread quickly, often reaching damaging levels before visible symptoms appear. Conventional visual inspection methods are labor-intensive, prone to subjectivity, and ineffective for large-scale monitoring. to three broad spectral bands and cannot detect pre-symptomatic changes. Multispectral and hyperspectral imaging overcome these limitations by capturing dozens to hundreds of rich spectral signatures across the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) regions, enabling the early detection of biochemical and structural changes in plant tissues. However, the high-dimensional and non-linear nature of spectral data demands advanced analytics. Computer vision and machine learning are effective at capturing complex patterns, handling large feature spaces, and adapting to variable conditions, enabling automated and accurate early-stage disease detection. This review provides a comprehensive synthesis of recent studies on potato disease detection based on spectral imaging and machine learning models, emphasizing emerging trends, ongoing challenges, and future opportunities for robust early-warning systems in precision agriculture. Hyperspectral imaging ; Multispectral image ; Feature (linguistics) ; Plant disease ; Spectral signature ; Spectral imaging ; Pattern recognition (psychology) ; Machine vision Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Potato Plant Research