jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Himanshu Prasad Mamgain | Maria Vittoria Diamanti | Pravat Ranjan Pati | M. E. Mohamed | Jitendra Kumar Pandey | Nitin Bhardwaj | Ankit Vasudeva | Mohammad Kanan
Source: Scientific Reports (article, 2025-10-03)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning-driven stability analysis of eco-friendly superhydrophobic graphene-based coatings on copper substrate This study inspects the integration of machine learning (ML) techniques with materials science to develop durable, eco-friendly superhydrophobic (SHP) graphene-based coatings for copper. We employed various ML and regression models, including XGBoost, polynomial regression models, Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Regression (SVR), to predict the stability of the contact angle (CA) under different stress conditions, such as NaCl immersion, abrasion cycles, tape peeling tests, sand impact, and open-air exposure. Our findings demonstrate that ensemble learning models, particularly XGBoost and Random Forest, outperform traditional regression techniques by effectively capturing nonlinear dependencies between stress parameters and CA retention. Higher-order polynomial regression models also exhibit strong predictive accuracy, making them well-suited for conditions where CA follows a well-defined trend. In contrast, SVR and KNN show limited generalization due to their sensitivity to hyperparameter selection and local interpolation effects, leading to weaker performance in datasets with high variability. ML-based algorithms predict CA values for tested coatings at longer term with respect to experimental tests, and underlined the beneficial effect of graphene incorporation in the coatings to extend the service life and preserve superhydrophobicity, overall reflecting the material's resilience under mechanical stress. The study highlights the importance of advanced predictive models, such as higher-degree polynomial regression and XGBoost, in capturing the complex relationships between variables influencing coating stability. Additionally, the integration of these models significantly accelerates the design and analysis process by reducing the reliance on time-consuming experimental testing. Support vector machine ; Stability (learning theory) ; Polynomial regression ; Random forest ; Hyperparameter ; Regression ; Polynomial ; Regression analysis ; Generalization ; Coating Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; High voltage insulation and dielectric phenomena
Authors: Yuzhou Hao | Junwei Che | Xiaoying Wang | Xuejie Li | Turab Lookman | Jun Sun | Xiangdong Ding | Zhibin Gao
Source: Physical review. B./Physical review. B (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Copper delocalization leads to ultralow thermal conductivity in chalcohalide <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">CuBiSeCl</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> Mixed anion halide-chalcogenide materials have attracted considerable attention due to their exceptional optoelectronic properties, making them promising candidates for various applications. Among these, ${\mathrm{CuBiSeCl}}_{2}$ has recently been experimentally identified with remarkably low lattice thermal conductivity (${\ensuremath{\kappa}}_{L}$). In this study, we employ Wigner transport theory combined with neuroevolution machine learning potential-assisted self-consistent phonon calculations to unravel the microscopic origins of this low ${\ensuremath{\kappa}}_{L}$. Our findings reveal that the delocalization and weak bonding of copper atoms are key contributors to the strong phonon anharmonicity and wavelike tunneling (random walk diffusons). These insights deepen our understanding of the relationship between bonding characteristics, anharmonicity, delocalization, and vibrational dynamics, paving the way for the design and optimization of ${\mathrm{CuBiSeCl}}_{2}$ and analogous materials for advanced phonon engineering applications. Thermal conductivity ; Materials science ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Solid-state spectroscopy and crystallography ; Crystal Structures and Properties
Authors: Da Wan | Shulin Bai | Suhua Fan | Bixuan Li | Xiaoya Huang | Hongyuan Zhao | Ziqi Wang | Zhen Li | Yu Liu | Peng Kang | Lei Zheng | Li‐Dong Zhao | Huibin Xu
Source: Science Bulletin (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Machine learning-guided design of high-performance Mg-based thermoelectrics: insights into thermal expansion effects Mg-based thermoelectric materials are becoming ideal candidates for thermoelectric applications, owing to their eco-friendliness and abundant availability. To overcome the limitations of conventional experimental methods and accelerate the development of high-performance thermoelectric materials, this study leverages high-throughput computing and machine learning to perform a comprehensive and systematic evaluation of a vast array of Mg-based thermoelectric materials. Our findings highlight the pivotal role of thermal expansion in modulating the thermoelectric figure of merit (ZT) in Mg-based systems. Specifically, thermal expansion alters the interatomic interaction potential, enhancing material anharmonicity and significantly reducing lattice thermal conductivity. Furthermore, thermal expansion reduces energy band dispersion, leading to a more concentrated density of states near the Fermi level. This effect increases effective mass, thereby potentially boosting the Seebeck coefficient. These insights not only deepen the understanding of the physical mechanisms by which thermal expansion influences thermoelectric performance but also establish a universal theoretical framework for optimizing high-performance thermoelectric materials. To accelerate the discovery and application of Mg-based thermoelectric materials, we have developed an XGBoost model with high predictive accuracy and robust generalization performance. This model enables the precise prediction of thermoelectric properties, providing a tool for rapid screening and optimization of Mg-based thermoelectric materials. Thermal expansion ; Thermoelectric materials ; Computer science ; Materials science ; Engineering physics ; Engineering ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Thermal properties of materials
Authors: Kailong Gu | Xingying Zhang | Zhiqiang Dong | Hongyun Chen | M. Xu | Zhi Sun | Shenjie Han | Jieyu Zhang | Youming Yu | Junfeng Hou
Source: International Journal of Biological Macromolecules (article, 2025-03-03)
Field: Materials Science — Polymers and Plastics

Deep learning-aided preparation and mechanism revaluation of waste wood lignocellulose-based flame-retardant composites Fire retardant ; Materials science ; Composite number ; Combustion ; Smoke ; Composite material ; Adhesive ; Pulp and paper industry ; Waste management ; Chemistry ; Organic chemistry Flame retardant materials and properties ; Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry
Authors: Hiroyasu Ochiai | Hiromasa Kaneko
Source: ACS Omega (article, 2025-01-06)
Field: Chemistry — Analytical Chemistry

Construction of Machine Learning Models to Predict the Maximum Absorption Wavelength Considering the Solute and Solvent and Inverse Analysis of the Models Fluorescent organic molecules are used in a wide range of fields, such as organic light-emitting diodes, paints, and fluorescent imaging, and they are indispensable in our daily lives. However, the development of fluorescent organic molecules is difficult because fluorescence is only exhibited by specific molecules. In addition, the optical properties of the same fluorescent organic molecule can change by changing the solvent. Therefore, to design new fluorescent organic molecules, it is necessary to predict their optical properties by considering the interaction between the solute and solvent. In this study, mathematical models of the maximum absorption wavelength obtained from the interaction between the solute and solvent were constructed by machine learning, and the solute-solvent combination with a specific maximum absorption wavelength can be predicted. In addition to the solute-solvent combinations in the data set used to construct the model, we also predicted the maximum absorption wavelengths of new solute-solvent combinations and new solute data sets. Fluorescence ; Solvent ; Absorption (acoustics) ; Wavelength ; Molecule ; Absorption spectroscopy ; Chemistry ; Materials science ; Biological system ; Chemical physics ; Optics ; Organic chemistry ; Optoelectronics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Mujian Xu | Lingxing Zhang | Ling Yuan | Chenghan Ji | Yueqing Zhang | Deyang Kong | Yanyang Zhang | Lu Lv | Ming Hua | Weiming Zhang
Source: Separation and Purification Technology (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Machine learning-assisted adsorption capacity prediction of ion exchange or chelate resin for heavy metals in aqueous solutions: External validation via multi-factor experiments Adsorption ; Aqueous solution ; Ion-exchange resin ; Chelation ; Ion exchange ; Chemistry ; Ion ; Inorganic chemistry ; Chemical engineering ; Materials science ; Engineering ; Organic chemistry Machine Learning in Materials Science
Authors: Wangfei Luo | Jihong Deng | Chenxi Li | Hui Jiang
Source: Foods (article, 2025-02-01)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of Peanut Skin Adulterants by Fourier Transform Near-Infrared Spectroscopy Combined with Chemometrics Peanut skin is a potential medicinal material. The adulteration of peanut skin samples with starchy substances severely affects their medicinal value. This study aimed to quantitatively analyze the adulterants present in peanut skin using Fourier transform near-infrared (FT-NIR) spectroscopy. Two adulterants, sweet potato starch and corn starch, were included in this study. First, spectral information of the adulterated samples was collected for characterization. Then, the applicability of different preprocessing methods and techniques to the obtained spectral data was compared. Subsequently, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to extract effective variables from the preprocessed spectral data, and Partial Least Squares Regression (PLSR), a Support Vector Machine (SVM), and a Black Kite Algorithm-Support Vector Machine (BKA-SVM) were employed to predict the adulterant content in the samples, as well as the overall adulteration level. The results showed that the BKA-SVM model performed excellently in predicting the content of sweet potato starch, corn starch, and overall adulterants, with determination coefficients (RP2) of 0.9833, 0.9893, and 0.9987, respectively. The experimental results indicate that FT-NIR spectroscopy combined with advanced machine learning techniques can effectively and accurately detect adulterants in peanut skin, providing a reliable technological support for food safety detection. Adulterant ; Chemometrics ; Partial least squares regression ; Starch ; Support vector machine ; Mathematics ; Food science ; Chemistry ; Pattern recognition (psychology) ; Fourier transform infrared spectroscopy ; Biological system ; Artificial intelligence ; Chromatography ; Computer science ; Statistics ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Aslak Fellman | Jesper Byggmästar | Fredric Granberg | K. Nordlund | Flyura Djurabekova
Source: Physical Review Materials (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al, and Ni Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than classical interatomic potentials and are usually trained with near equilibrium simulations in mind. Here, the authors have created computationally efficient Gaussian Approximation Potentials (GAP) for large-scale simulations in Cu, Al, and Ni. The models use a selection of low-dimensional descriptors and tabulation (tabGAP), achieving orders-of-magnitude speed up compared to standard GAP. Furthermore, the models include external repulsive pair interactions, and the training databases have been designed with extra attention to far-from equilibrium simulations. Materials science ; Interatomic potential ; Scale (ratio) ; Atomic units ; Molecular dynamics ; Chemical physics ; Metallurgy ; Computational chemistry ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Diego Ontiveros | Sergi Vela | Francesc Viñes | Carmen Sousa
Source: ACS Catalysis (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

MXgap: A MXene Learning Tool for Bandgap Prediction The increasing demand for clean and renewable energy has intensified the exploration of advanced materials for efficient photocatalysis, particularly for water splitting applications. Among these materials, MXenes, a family of two-dimensional (2D) transition metal carbides and nitrides, have shown great promise. This study leverages machine learning (ML) to address the resource-intensive process of predicting the bandgap of MXenes, which is critical for their photocatalytic performance. Using an extensive data set of 4356 MXene structures, we trained multiple ML models and developed a robust classifier-regressor pipeline that achieves a classification accuracy of 92% and a mean absolute error (MAE) of 0.17 eV for bandgap prediction. This framework, implemented in an open-source Python package, MXgap, has been applied to screen 396 La-based MXenes, identifying six promising candidates with suitable band alignments for water splitting whose optical properties were further explored via optical absorption and solar-to-hydrogen (STH) efficiency. These findings demonstrate the potential of ML to accelerate MXene discovery and optimization for energy applications. Band gap ; Materials science ; Catalysis ; Computer science ; Artificial intelligence ; Nanotechnology ; Chemistry ; Optoelectronics MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Nada K. Abbas | Mouna Imene Ousaadi | Mohammed Berkani | Moisés Canle | Kasrani Oumnia | Lalitha Gnanasekaran | ‪Damià Barceló | Yasser Vasseghian | Hecini Abdeldjalil El Mouatez Billah
Source: Inorganic Chemistry Communications (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Green synthesis and analysis of iron oxide nanoparticles for methylene blue degradation by Fenton-like process: Antimicrobial properties and integrated AI-GA modeling Methylene blue ; Degradation (telecommunications) ; Nanoparticle ; Antimicrobial ; Chemistry ; Chemical engineering ; Process (computing) ; Oxide ; Methylene ; Iron oxide ; Nuclear chemistry ; Materials science ; Nanotechnology ; Photocatalysis ; Organic chemistry ; Catalysis ; Computer science ; Engineering Nanoparticles: synthesis and applications ; Water Quality and Pollution Assessment
Authors: Madhavi Latha Chinta | Pradeep Kumar Gandam | Sreenivasa Rao Parcha
Source: International Journal of Biological Macromolecules (article, 2025-01-30)
Field: Materials Science — Biomaterials

Design and optimization of tamarind seed polysaccharide-based scaffold for tissue engineering applications using statistical modeling and machine learning, and it's in-vitro validation Response surface methodology ; Materials science ; Chitosan ; Scaffold ; Tissue engineering ; Chemical engineering ; Swelling ; Porosity ; Biomaterial ; Neural tissue engineering ; Biomedical engineering ; Composite material ; Chemistry ; Chromatography ; Nanotechnology Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; 3D Printing in Biomedical Research
Authors: Thanh Mien Nguyen | Thu M. T. Nguyen | Sung‐Jo Kim | Seok Kyung Kang | Seungju Lee | Youn Joo Jung | Hyun Yul Kim | Jin‐Woo Oh
Source: ACS Sensors (article, 2025-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microcapillary-Derived Plasmonic-Enhanced Cluster through the Self-Assembly Process for Breast Cancer Diagnosis Artificial intelligence (AI)-based surface-enhanced Raman scattering (SERS) is a powerful system for cancer diagnosis, leveraging its unique advantages by combining the high sensitivity of the SERS technique with the advanced classification capabilities provided by computing power. While previous studies have yielded significant results through using exosomes, miRNA, and phenotypic biomarkers for detecting breast cancer, these methods frequently entail time-consuming and complex pretreatment steps, demanding highly skilled handling. Here, we present a free-label SERS platform with faster sampling without any pretreat using blood plasma for breast cancer diagnosis. In this study, a cluster structure of gold nanoparticles within a confines space of microcapillary was fabricated to generate close-packing nanoparticles for enhancing electromagnetic field and large number of "hot spot." We demonstrate that our SERS platform can significantly amplify the Raman signal through standard chemical detection of R6G molecules. Consequently, a solution mixed appropriately between blood plasma collected from participants with gold nanoparticles to build the hybrid cluster in the microcapillary for SERS measurement. With the support of a machine learning model, the breast cancer diagnosis has successfully classified between patients and normal participants with a high accuracy of 87.5%. Cluster (spacecraft) ; Plasmon ; Nanotechnology ; Breast cancer ; Process (computing) ; Materials science ; Cancer ; Medicine ; Computer science ; Optoelectronics ; Internal medicine Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jun Pang | Z. Wang | Tao Song | Zhen-Bang Zhang | Yu‐Feng Meng | Sichao Zhang | Long Zhang | Weiyi Xing | Shu‐Hong Yu
Source: National Science Review (article, 2025-03-17)
Field: Materials Science — Polymers and Plastics

A nacre-inspired structural material with thermochromic properties and mechanical robustness by atomic-level design Advanced structural materials are often required to exhibit a combination of light weight, high strength and superior toughness. Biomimetic strategies hold promise for achieving these seemingly conflicting mechanical properties simultaneously. However, current biomimetic structural materials lack active fire-warning and passive flame-retardant functionalities, which poses risks for their application in fire-prone scenarios. Herein, we present a nacre-mimetic alumina-cyanate resin composite (NAC) that has a combination of mechanical robustness with thermochromic and flame-retardant properties. Through controlled atomic doping, chromium atoms are incorporated into alumina microplatelets, forming solid-solution assembly units that exhibit reversible thermochromism and a solid-solution-strengthening effect. The bioinspired 'brick-and-mortar' structure endows the NAC with high strength (∼290.1 MPa) and fracture toughness (∼11.1 MPa m<sup>1/2</sup>). Coupled with a machine-learning-based image-recognition system, the NAC leverages its thermochromic properties to deliver a rapid fire warning within 9 s at 250°C, which is significantly faster than traditional electronic fire alarms. Its layered structure effectively impedes oxygen flow, achieving an oxygen-limiting index of 50%, and thus ensuring excellent flame-retardant performance. This design delays the combustion peak and reduces the heat-release value, thereby enhancing the flame-retardant performance. This work demonstrates the effective integration of a structural and functional design for active early fire warning and passive flame retardancy, paving the way for structural materials in advanced fire-warning systems in challenging environments. Fire retardant ; Materials science ; Limiting oxygen index ; Toughness ; Composite material ; Thermochromism ; Nanocomposite ; Combustion ; Fracture toughness ; Robustness (evolution) ; Composite number ; Nanotechnology ; Char Flame retardant materials and properties ; Polymer composites and self-healing ; Magnesium Oxide Properties and Applications
Authors: Sanchit Saxena | Hrishikesh Sharma
Source: Scientific Reports (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Prediction and assessment of optimal concrete compositions for overall radiation protection and reduced global warming potential Developing an efficient radiation-shielding concrete composition holds paramount importance for nuclear, medical, and defence facilities. The intricate interactions between various radiation particles and materials across different energy ranges present challenges in designing effective and resilient overall shielding structures. This study presents a novel approach that integrates machine learning and genetic algorithms (GA) to optimize concrete compositions for enhanced radiation shielding against gamma and neutron rays across a wide energy spectrum. By leveraging these advanced techniques, six compositions (concrete_1-concrete_6) spanning different density ranges were derived from an extensive database developed from the previous experimental researches. Subsequently, the shielding effectiveness of these compositions against all radiation particles was evaluated and compared using the OpenMC Code. The findings revealed that the proposed concrete_5 and concrete_6 compositions, comprising iron, boron, nickel, and tungsten at specified weight fractions, outperform other state-of-the-art compositions in overall radiation shielding. Furthermore, the analysis indicated a 65.89% reduction in Global Warming Potential (GWP) with the adoption of concrete_6 composition compared to conventional concrete composition. Global warming ; Environmental science ; Computer science ; Climate change ; Ecology ; Biology Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Radioactivity and Radon Measurements