jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N.V. Ichanska | I.S. Usenko | D.V. Usenko | N.M. Pinchuk
Source: Modern construction and architecture (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

NUMERICAL MODELING OF THE IMPACT OF COMPLEX LOADS ON BUILDING MATERIALS The article is devoted to the urgent problem of predicting the durability of building materials and structures exposed to complex multifactorial loads, including mechanical, thermal, seismic, and corrosive effects. Current trends in construction, especially in areas with high risks of military damage and natural disasters, require scientifically grounded methodologies for assessing the performance of materials under real operating conditions. The study presents a comprehensive analysis of numerical modeling methods, with a particular focus on the finite element method (FEM). This approach enables detailed reproduction of the stress–strain state and makes it possible to account for nonlinear interactions between different types of loads, which is essential for accurate predictions of material service life. Special attention is paid to algorithms that integrate mechanical, seismic, and thermal effects into a unified model, as well as the application of combined methods, including the boundary element method, the Monte Carlo method, and finite differences. The proposed numerical schemes were validated against experimental data, confirming high accuracy with deviations not exceeding a few percent. An additional innovative aspect of the research lies in the integration of classical numerical methods with machine learning technologies, particularly deep neural networks, which allow the consideration of complex nonlinear degradation patterns of materials over time. The study also emphasizes the integration of numerical models with monitoring systems based on IoT sensors. Such an approach ensures real-time dynamic control of the technical state of building structures and enables the timely identification of critical deviations. It has been demonstrated that the application of these algorithms not only improves the accuracy of residual life predictions but also significantly reduces costs by implementing resource-saving restoration technologies. The conclusions outline future research directions, including the extension of numerical modeling methodologies for novel high-performance materials, the advancement of machine learning techniques, and the creation of fully automated systems for monitoring and predicting the technical state of building structures. Finite element method ; Nonlinear system ; Identification (biology) ; Artificial neural network ; Focus (optics) ; Monte Carlo method ; Numerical analysis ; Boundary value problem Structural mechanics and materials ; Structural Health Monitoring Techniques ; Environmental and Industrial Safety
Authors: Yafei Dong | Shutian Xuanyuan | Chuang Xie | Ying Sun | Xiaomeng Zhou | Yuanhang Wang
Source: Crystals (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Neural Network-Based Kinetic Model for Antisolvent Crystallization of Benzophenone: Construction, Validation, and Mechanistic Interpretation The emergence of artificial neural networks and the widespread application of process analytical technology (PAT) have founded a robust foundation for neural network applications in crystallization research. This study investigated benzophenone antisolvent crystallization kinetics through online monitoring of the crystallization process via PAT tools and kinetics fitting via neural networks. The antisolvent crystallization process was simulated by integrating network-based kinetics with population balance. The findings suggest that benzophenone exhibits size-independent growth in water–methanol systems. The neural network-based model demonstrates improved performance (a consistent 50 ± 5% enhancement in prediction accuracy (R2) over empirical kinetic models) in predicting crystallization kinetics. Furthermore, the network-based process model achieved remarkable agreement with the experimental crystal size distribution, showing smaller deviation (1.1%), less than that of traditional empirical models (5.29%). This work proposed a robust crystallization process model combining PAT tools and artificial neural networks, enabling rapid crystallization kinetics determination and accurate process simulations. Benzophenone ; Interpretation (philosophy) ; Crystallization ; Artificial neural network ; Biochemical engineering ; Biological system ; Computer science ; Materials science ; Artificial intelligence ; Chemical engineering ; Engineering ; Polymer chemistry ; Biology ; Programming language Crystallization and Solubility Studies ; Petroleum Processing and Analysis ; Thermal and Kinetic Analysis
Authors: Mads-Peter Verner Christiansen | Bjørk Hammer
Source: Machine Learning Science and Technology (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence Abstract Machine learning interatomic potentials have become an indispensable tool for materials science, enabling the study of larger systems and longer timescales. State-of-the-art models are generally graph neural networks that employ message passing to iteratively update atomic embeddings that are ultimately used for predicting properties. In this work we extend the message passing formalism with the inclusion of a continuous variable that accounts for fractional atomic existence. This allows us to calculate the gradient of the Gibbs free energy with respect to both the Cartesian coordinates of atoms and their existence. Using this we propose a gradient-based grand canonical optimization method and document its capabilities for a Cu(110) surface oxide. Formalism (music) ; Artificial neural network ; Graph ; Cartesian coordinate system ; Optimization problem ; Interatomic potential ; Canonical coordinates Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Graph Neural Networks
Authors: Marcin Mińkowski | Fahime Seyedheydari | Mikko Seppi | Bin Chen | Simo Särkkä
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning-based surrogate model for generation of transparent wood microstructures Distortion (music) ; Microstructure ; Parametric statistics ; Convolutional neural network ; Artificial neural network ; Bottleneck Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Wood Treatment and Properties
Authors: Pamela Costa Carvalho | Federico Zipoli | Alan C. Duriez | Marco Antonio Barroca | Rodrigo Neumann Barros Ferreira | Barbara Jones | Benjamin H. Wunsch | M. Steiner
Source: Scientific Data (article, 2025-11-12)
Field: Chemistry — Inorganic Chemistry

TBHubbard: tight-binding and extended Hubbard model dataset for metal-organic frameworks Metal-organic frameworks (MOFs) are porous materials composed of metal ions and organic linkers. Due to their chemical diversity, MOFs can support a broad range of applications in chemical separations. However, the vast amount of structural compositions encoded in crystallographic information files complicates application-oriented computational screening and design. The existing crystallographic data, therefore, requires augmentation by simulated data so that suitable descriptors for machine-learning tasks become available. Here, we provide extensive simulation data augmentation for MOFs within the QMOF dataset. We have applied a tight-binding, lattice Hamiltonian and density functional theory to MOFs for performing electronic structure calculations. Specifically, we provide a tight-binding representation of 10,000 MOFs, and an Extended Hubbard model representation for a sub-set of 240 MOFs containing transition metals, where intra-site U and inter-site V parameters are computed self-consistently. In addition to computational workflows for identifying structure-property correlations, the data supports quantum computing tasks that rely on tight-binding Hamiltonian and self-consistent computed Hubbard parameters. For validation and reuse, we have made the data publicly available. Workflow ; Hubbard model ; Hamiltonian (control theory) ; Representation (politics) ; Density functional theory ; Lattice (music) ; Ising model ; Electronic structure Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Bin Ouyang
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Extend Materials Genome to High Entropy Battery Materials The Materials Genome has been emerging and evolving for over a decade, exemplified by frameworks such as the Materials Project, which encompasses most known material structures, properties and compositional combinations across the periodic table. This talk will present recent advancements in addressing the dimensionality challenges of the Materials Genome and explore its applications—often referred to as high-entropy materials—in energy storage and conversion. A brief overview will be provided on the incubation of high-entropy battery materials, as well as their recent expansion into the broader energy storage and conversion field. Furthermore, to align with state-of-the-art foundational AI efforts, a demonstration of a specialized database for high-entropy energy materials (HEEM) and tailored machine learning models will be presented. Curse of dimensionality ; Genome ; Battery (electricity) ; Energy storage ; Electrochemical energy storage ; Entropy (arrow of time) ; Energy (signal processing) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Thermal Expansion and Ionic Conductivity
Authors: Yi-Fan Hou | Cheng Wang | Pavlo O. Dral
Source: ChemRxiv (preprint, 2025-04-11)
Field: Chemistry — Analytical Chemistry

Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models Infrared (IR) spectroscopy is a potent tool for identifying molecular structures and studying the chemical properties of compounds, and hence, various theoretical approaches have been developed to simulate and predict the IR spectra. However, the theoretical approaches based on quantum chemical calculations suffer from high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (e.g., semi-empirical methods orders of magnitude faster than DFT). Here, we introduce a new approach, based on the universal machine learning (ML) models of the AIQM series targeting CCSD(T)/CBS level, that can deliver molecular IR spectra with accuracy close to DFT (compared to the experiment) and the speed close to a semi-empirical GFN2-xTB method. This approach is based on the harmonic oscillator approximation with the frequency scaling factors fitted to experimental data. While the benchmarks reported here are focused on harmonic IR spectra, our implementation supports anharmonic spectra simulations via molecular dynamics and VPT2. These implementations are available in MLatom as described in https://github.com/dralgroup/mlatom and can be performed online via a web browser. Infrared ; Infrared spectroscopy ; Spectral line ; Environmental science ; Statistical physics ; Physics ; Optics ; Astronomy ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Taline Kerackian | Matthieu Daniel | Clément Wespiser | Éric Pasquinet | Eugénie Romero
Source: ChemRxiv (preprint, 2025-01-30)
Field: Materials Science — Materials Chemistry

Investigation of Arenes and Heteroarenes Nitration supported by High-Throughput Experimentation and Machine Learning Access to the nitro functional group is a very common and longstanding transformation of interest in many fields of chemistry. However, the robustness and specificity of this transformation can remain challenging, particularly in the case of heteroarene nitration. From this observation, a large investigation was initiated to screen nitration conditions on various arenes and heteroarenes. The systematical and diverse study of both nitrating agents and activating reagents was conducted using high-throughput experimentation, to afford high quantity and high quality data generation. General trends have been identified and correlated to the electronic property of the heteroarene, notably the difficult nitration of electron-poor heteroarenes was highlighted. Original combinations of reagents were found to perform well in nitration reactions. The obtained data were also used to design a predictive tool relying on machine learning in order to provide the best nitration reaction conditions depending on the targeted substrate. The limited predictive efficiency obtained pointed out the importance of the diversification and the chemically relevant encoding of the data set. Nitration ; Reagent ; Combinatorial chemistry ; Computer science ; Robustness (evolution) ; Biochemical engineering ; High-throughput screening ; Chemistry ; Artificial intelligence ; Machine learning ; Organic chemistry ; Engineering ; Biochemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Asymmetric Hydrogenation and Catalysis
Authors: Andrew Martin | Kien Trung Nguyen | Sebastian Zaatini | Michael Lastovich | Bharat Gwalani | Paul Bogdan | Martin Thuo
Source: Advanced Science (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Graph‐Theory Approach to Element Miscibility and Alloy Design With 118 currently known elements, hence millions of possible quinary combinations, understanding and discovering potentially beneficial mixtures is challenging. Possible stable mixtures, however, are vital for alloy design or realization of new materials. Thermodynamically favorable interactions can also lead to enhanced materials properties. We apply graph theory to map relation in key thermodynamic parameters between elements, therefore revealing possible favorable (miscible) pairs and their congeners. Using closeness centrality and Lipschitz-Hölder exponent, we define miscibility across the whole period table, albeit at ambient conditions. Whereas most metals have fair solubility, hyper- and hypo- centrality (high and low solubility respectively) clusters are identified. We confirm our graph-based approach in understanding miscibility by comparing results to CALPHAD and Miedema's model. Unlike the heuristic models, the graph-based approach is amenable to machine learning enabling prediction at extreme environments by extrapolating known scaling laws. Quinary ; Miscibility ; Centrality ; CALPHAD ; Scaling ; Realization (probability) ; Closeness ; Alloy ; Phase diagram Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Selection and Properties
Authors: Thamiris Gire Zine Neves | Roberto G. Beraldo | Cinara Ghedini
Source:  (article, 2025-09-30)
Field: Materials Science — Polymers and Plastics

Multiclass Classification of Denim Fabric Defects Using Convolutional Neural Networks In denim fabric manufacturing, quality assessment traditionally relies on visual inspection by weavers, as guided by ABNT NBR 13484. This manual, post-production process can delay defect detection, allowing issues to persist and recur, which disrupts the production chain. To address this, we developed a deep neural network for multiclass classification of common fabric defects, including double yarns, coarse ends, harness misdraws, slack ends, broken wefts, and soft wefts. Our model integrates convolutional layers for feature extraction and linear layers for classification. It was trained on 2,985 images captured using a low-cost camera mounted above a textile manufacturing loom in a denim manufacturing laboratory, replicating realworld inspection conditions. Our model achieved a classification accuracy of 89%, outperforming other state-of-the-art machine learning algorithms. These results demonstrate the potential for real-time defect detection during production, reducing material waste and improving overall fabric quality. Denim ; Convolutional neural network ; Process (computing) ; Feature extraction ; Artificial neural network ; Multiclass classification ; Pattern recognition (psychology) ; Textile ; Feature (linguistics) Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Advanced Sensor and Energy Harvesting Materials
Authors: Jing Wang | Shuai Long | Z. Yu | Peng Peng
Source: International Journal of Hydrogen Energy (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Accelerated discovery of low-cost and high-activity metal oxide catalysts for magnesium hydrolysis via active learning and experimental validation Catalysis ; Oxide ; Magnesium ; Hydrogen ; Hydrogen production ; Ionization ; Transition metal ; Metal Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; Electrocatalysts for Energy Conversion
Authors: Sungin Kim (7534526) | Valentin Briega-Martos (20749946) | Shikai Liu (245897) | Kwanghwi Je (14289535) | Chuqiao Shi (8721630) | Katherine Marusak Stephens (21414527) | Steven E. Zeltmann (8350224) | Zhijing Zhang (297460) | Rafael Guzman-Soriano (21414530) | Wenqi Li (137889) | Jiahong Jiang (781283) | Juhyung Choi (11809596) | Yafet J. Negash (21414533) | Franklin S. Walden (21414536) | Nelson L. Marthe (21414539) | Patrick S. Wellborn (21414542) | Yaofeng Guo (21414545) | John Damiano (1420231) | Yimo Han (1661224) | Erik H. Thiede (9029096) | Yao Yang (543059)
Source: Figshare (article, 2025-05-23)
Field: Chemistry — Electrochemistry

<i>Operando</i> Heating and Cooling Electrochemical4D-STEM Probing Nanoscale Dynamics at Solid–Liquid Interfaces <i>Operando</i>/<i>in situ</i> methods have revolutionized our fundamental understanding of molecular and structural changes at solid–liquid interfaces and enabled the vision of “watching chemistry in action”. <i>Operando</i> transmission electron microscopy (TEM) emerges as a powerful tool to interrogate time-resolved nanoscale dynamics, which involve local electrical fields and charge transfer kinetics distinctly different from those of their bulk counterparts. Despite early reports on electrochemical or heating liquid-cell TEM, developing <i>operando</i> TEM with simultaneous electrochemical and thermal control remains a formidable challenge. Here, we developed <i>operando</i> heating and cooling electrochemical liquid-cell scanning TEM (EC-STEM). By integrating a three-electrode electrochemical circuit and an additional two-electrode thermal circuit, we can investigate heterogeneous electrochemical kinetics across a wide temperature range of −50 to 300 °C. We used Cu electrodeposition/stripping processes as a model system to demonstrate quantitative electrochemistry from −40 to 95 °C in both transient and steady states in aqueous and organic solutions, which paves the way for investigating energy materials operating in extreme climates. Machine learning-assisted quantitative 4D-STEM structural analysis in cold liquids (−40 °C) reveals a distinct two-stage growth of nanometer-scale mossy Cu nanoislands with random orientations followed by μm-scale Cu dendrites with preferential orientations. This work benchmarked electrochemistry in the three-electrode EC-STEM and systematically investigated the temperature and pH dependence of the Pt pseudoreference electrode (RE). At room temperature, the Pt pseudo-RE shows a reliable potential of 0.8 ± 0.1 V vs the standard hydrogen electrode and remains pH-independent on the reversible hydrogen electrode scale. We anticipate that <i>operando</i> heating/cooling EC-STEM will become invaluable for understanding fundamental temperature-controlled nanoscale electrochemistry and advancing renewable energy technologies (e.g., catalysts and batteries) in realistic climates. Electrochemistry ; Electrode ; Nanoscopic scale ; Electrochemical kinetics ; Thermal ; Hydrogen ; Kinetics ; Work (physics) Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Hajar Ech-chajiy | Haibo Ouyang | Cuiyan Li | Xin Wang | Mohammed Rouinkou
Source: Journal of Materials Science (article, 2025-11-25)
Field: Chemistry — Spectroscopy

Review: Electrospun oxide ceramic nanofibers for high-temperature thermal insulation applications Ceramic ; Electrospinning ; Nanofiber ; Spinning ; Thermal insulation ; Oxide ; Calcination Aerogels and thermal insulation ; Advanced ceramic materials synthesis ; Thermal properties of materials
Authors: Guolin Cao | Shiyu He | Yirong Zhang | Sha Yang | Shuang Li | Yusheng Li | Ji-Chang Ren
Source: Materials Today Electronics (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

A data-driven framework for efficient and physically interpretable prediction of RPA-level adsorption energies Accurately predicting the adsorption energies of reaction intermediates is crucial for the rational design of heterogeneous catalysts. However, high-accuracy methods such as the random phase approximation (RPA) are computationally expensive, hindering rapid catalytic materials discovery. Although machine learning models have been proposed to address this, they are often trained on less accurate data or lack physical interpretability. In this work, we introduce a comprehensive framework that integrates three key components: (1) feature importance analysis to identify critical descriptors, (2) a multi-fidelity approach that correlates expensive RPA data with cheap low-fidelity calculations, and (3) symbolic regression to derive transparent, analytic models. Notably, the model not only achieves high predictive accuracy (with an R 2 value of 0.96) using just 9% of the RPA data, but also offers fundamental insight by explicitly linking surface energy to an electronic descriptor. Our work thus establishes a rapid and interpretable pathway for catalytic materials discovery. Work (physics) ; Feature (linguistics) ; Energy (signal processing) ; Adsorption ; Phase (matter) ; Surface (topology) ; Regression ; Experimental data ; Key (lock) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Yang Lv | Jianfu Li | Zhaobin Zhang | Yong Liu | Jianan Yuan | Jiachuan Lin | Xiaoli Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Mixed Ionic-Electronic Conduit of Ca2+ Confined by Two-Dimensional Multicomponent Boron-Carbon Layers Boron ; Electrical conduit ; Carbon fibers ; Ionic bonding ; Materials science ; Ion ; Nanotechnology ; Chemical physics ; Chemistry ; Composite material ; Mechanical engineering ; Engineering ; Composite number Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research ; Ionic liquids properties and applications