jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wei Li | Feng-ning Xue | Peng-bo Zhao | Yong Lu
Source: Physica Scripta (article, 2025-02-07)
Field: Materials Science — Materials Chemistry

Vibrational properties and lattice thermal conductivity of bulk and monolayer ZrS<sub>2</sub> at finite temperatures Abstract The traditional three-phonon scattering approach cannot adequately reflect the anharmonic phonon properties of the ZrS 2 system due to its strong intrinsic anharmonicity. In this study, the vibrational properties and lattice thermal conductivity of both bulk and monolayer ZrS 2 were investigated using large-scale machine learning force field molecular dynamics simulations and the anharmonic phonon approach, where all orders of anharmonic interactions between phonons caused by temperature were considered. At finite temperatures, the predicted Raman-active in-plane E g mode of the bulk phase ZrS 2 decreases, while the inter-plane A 1 g mode shifts to the blue, in agreement with experimental observations. Due to the competition between the chemical bond ionicity and phonon anharmonicity, the vibrational stiffness and frequency of the A 1 g mode in monolayer ZrS 2 exhibit inverse temperature-dependent behavior compared to the bulk phase. Phonon linewidths and scattering rates increase significantly with temperature, indicating stronger anharmonic interactions in ZrS 2 . The lattice thermal conductivity of bulk ZrS 2 is significantly reduced when anharmonic phonon scattering beyond three-phonon processes is considered, with an average value of 1.91 ± 0.08 Wm −1 K −1 at 300 K, which aligns with experimental results. Acoustic phonon modes dominate heat transport, with the lifetime of the TA mode being particularly temperature-sensitive. Monolayer ZrS 2 exhibits enhanced lattice thermal conductivity, more than twice that of the bulk, due to the reduced unit cell volume and increased phonon lifetimes. Anharmonicity ; Phonon ; Condensed matter physics ; Thermal conductivity ; Materials science ; Monolayer ; Phonon scattering ; Scattering ; Lattice (music) ; Physics ; Nanotechnology ; Optics Thermal properties of materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Zied Hosni | Sofiene Achour | Fatma Saâdi | Jiann-Her Lin | Jie Sheng | Mohammed M. Al Qaraghuli
Source: Computational Materials Science (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Specific surface area (SSA) of perovskites with uncertainty estimation approach In recent years, the rapid development of computer technology and the Internet has enabled faster and more convenient data acquisition. Using big data and machine learning (ML) to accelerate the development of new materials is crucial. This study provides insight into the application of ML in predicting the specific surface area (SSA) of ABO 3 -type perovskite. In this article, we initially introduce the basic concepts of ML, its general workflow in materials research, and the specific algorithms used. We further discuss the structural features of ABO 3 -type perovskites and their development trends in photocatalytic applications. The prediction of SSA is vital for the application and performance optimization of perovskites, and ML provides an efficient and accurate means of prediction. In the modeling process, we collected experimental data on 50 ABO 3 -type perovskites from published literature and feature-engineered them in various ways to obtain three different datasets. By screening the features through principal component analysis (PCA) and Genetic Algorithm (GA), we found that GA is superior to the Random Forest (RF) model. At the same time, PCA is more suitable for the Support Vector Regression (SVR) model. The test results showed that the RF model has a prediction accuracy of 0.832, while the SVR model has a prediction accuracy of 0.808, and both methods exhibited high prediction accuracy. In addition, the leave-one-out cross-validation results further confirmed the robustness of both models. Finally, we conducted an in-depth analysis of the importance of features. The results showed that the calcination temperature (CT) and calcination time (AH) are the most critical features, which are positively and negatively correlated with the SSA of perovskite oxides, respectively. This provides valuable reference information for the design and optimization of perovskite materials. Overall, this study successfully predicted the SSA of ABO 3 -type perovskites using an ML method, which provides a new and effective tool for researching and applying perovskite materials. Estimation ; Materials science ; Environmental science ; Surface (topology) ; Mineralogy ; Chemistry ; Mathematics ; Engineering ; Systems engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Alexander A. Ksenofontov | Yulia V Eremeeva | Pavel S. Bocharov | Dmitriy M. Makarov
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

SpecML: web tool for predicting the spectral properties of BODIPYs Interpretability ; Quantum yield ; Singlet oxygen ; Absorption (acoustics) ; Fluorescence ; Quantitative structure–activity relationship ; Quantum ; BODIPY ; Yield (engineering) Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Nanoplatforms for cancer theranostics
Authors: P. C. Canfield | Tyler J. Slade
Source: Zeitschrift für anorganische und allgemeine Chemie (article, 2025-03-16)
Field: Materials Science — Materials Chemistry

Use of Frit‐Disc Crucible Sets to Make Solution Growth More Quantitative and Versatile The recent availability of step‐edge, frit‐disc crucible sets (generally sold as Canfield Crucible Sets or CCS) has led to multiple innovations associated with the group's use of solution growth. The use of CCS allows for the clean separation of liquid from solid phases during the growth process. This clean separation enables the reuse of the decanted liquid, either allowing for simple, economic, savings associated with recycling expensive precursor elements or allowing for the fractionation of a growth into multiple, small steps, revealing the progression of multiple solidifications. Clean separation of liquid from solid phases also allows for the determination of the liquidus line (or surface) and the creation, or correction, of composition–temperature phase diagrams. The reuse of clean decanted liquid has also allowed to prepare liquids ideally suited for the growth of large single crystals of specific phases by tuning the composition of the melt to the optimal composition for growth of the desired phase, often with reduced nucleation sites. Finally, it is discussed how solution growth and CCS use can be harnessed to provide a plethora of composition–temperature data points defining liquidus lines or surfaces with differing degrees of precision to either test or anchor artificial intelligence and/or machine‐learning‐based attempts to augment and extend the limited experimentally determined database. Crucible (geodemography) ; Frit ; Materials science ; Metallurgy ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Metallurgical Processes and Thermodynamics
Authors: Fei Yang | Cunyuan Wen | Sheng Zhu | Yucheng Feng | Zhile Ye | Hao-Kai Peng | Pengfei Guan
Source: Composites Part A Applied Science and Manufacturing (article, 2025-07-23)
Field: Materials Science — Polymers and Plastics

Machine learning model of plant fiber/PLA composite: Prediction and analysis of mechanical strength Materials science ; Composite material ; Composite number ; Fiber ; Mechanical strength Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; biodegradable polymer synthesis and properties
Authors: Zihao Xie | Samir I. Gadow | Kakhramon Fayzullaev | Usmonov Botir | Rui Xiao | Zhicheng Luo
Source: ACS Applied Nano Materials (article, 2025-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Feature Engineering Enhanced Machine Learning Prediction of Pore Properties in Lignin-Derived Nanoporous Carbon for High-Performance Supercapacitor Applications Lignin-derived porous carbon (LDPC), prepared through catalytic pyrolysis, has attracted significant attention for its use in energy storage. However, optimizing its nanopore structure is often hindered by the trial-and-error nature of experiments and the limited quality of the available data. In this work, we propose a hybrid machine learning framework that enhances data quality through feature generation, thereby improving the predictive accuracy of pore-related properties. By systematically transforming and combining existing features, we generated 2025 candidate features and identified 30 high-correlation features that were integrated into the original data set. This enriched feature space significantly improved model performance: the final regressor achieved R2 values of 0.99 for specific surface area (SSA) and total pore volume (TPV) and demonstrated strong generalization capability (R2 > 0.85). Interpretability analysis further confirmed that the final regressor captures meaningful physical mechanisms in the generated feature space. Our approach provides an efficient pathway to enhance data set quality and accelerate the discovery of high-performance nanoporous carbon materials from lignin. By emphasizing nanoscale pore characteristics, this work highlights the potential of LDPC for high-performance supercapacitor applications. Interpretability ; Nanopore ; Feature (linguistics) ; Nanoporous ; Artificial neural network ; Generalization ; Quality (philosophy) ; Supercapacitor Supercapacitor Materials and Fabrication ; Nanoporous metals and alloys ; Catalysis and Hydrodesulfurization Studies
Authors: Rashmi Aradhya | B. M. Madhu | J. Sundara Rajan
Source: Polymer Composites (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Low frequency dielectric analysis for understanding water absorption characteristics of epoxy nanocomposites Abstract Nanocomposites are versatile multifunctional engineering materials. For electrical applications, diminishing water diffusion and optimization of electrical properties are challenging. Low frequency dielectric measurements are preferred for the assessment of water diffusion because of the dominant low frequency response in the presence of water. Epoxy composites with organically modified montmorillonite of 2, 5, and 7 wt% are investigated. Filler at 5 wt% is observed to be optimal, with a 28% reduction in the diffusion coefficient. Higher filler content decreases the real permittivity due to interfacial polarization, and water diffusion for 240 h reveals minimal filler dependence of real permittivity. The relaxation peak of imaginary permittivity occurs at 0.08 Hz at 5 wt%, and at higher frequencies in other composites due to changes in the filler matrix interactions and electrical conductivity. Water diffusion tends to decrease imaginary permittivity. AC conductivity is not influenced by the filler and it ranges from 10 −10 to 10 −9 S/m over 10 −4 Hz–1 kHz, and its variations with water diffusion at lower frequencies are not significant. The differences in the nanocomposites are confirmed using FTIR analysis. The multi‐core water shell model is correlated to the variations observed in the dielectric properties of the nanocomposites. The utility of low frequency dielectric analysis is demonstrated for the assessment of water diffusion. Key application areas of low frequency dielectric analysis are the estimation of water diffusion mechanisms and it is an enabler of the development of data analytics using machine learning for improving the quality of nanocomposites. Highlights 5 wt% OMMT addition offers optimal water barrier performance in epoxy composites. Surface‐modified OMMT enhances filler distribution and interfacial adhesion. Dielectric loss peaks shift to 0.08 Hz at 5 wt% filler content. AC conductivity remains stable at 10 −10 –10 −9 S/m across frequencies. Low‐frequency dielectric analysis accurately models water absorption. Materials science ; Epoxy ; Composite material ; Absorption of water ; Nanocomposite ; Dielectric ; Absorption (acoustics) High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Shariq Haseen | Mitchell Falgoust | Peter Kroll
Source: Ceramics International (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Computational study of Raman spectra and vibrational modes at the interface between SiCO and free carbon Raman spectroscopy ; Materials science ; Interface (matter) ; Carbon fibers ; Spectral line ; Chemical physics ; Molecular physics ; Optics ; Composite material ; Physics ; Quantum mechanics Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis ; Graphene research and applications
Authors: Zhiyu Tao | Lin Su | Mingji Li | Xiuwei Xuan | Cuiping Li | Hongji Li
Source: Food Chemistry (article, 2025-07-28)
Field: Chemistry — Electrochemistry

A Sn-Ta-O-doped vertical graphene electrochemical sensor based on a machine learning prediction model for monitoring cadmium in beverages Graphene ; Detection limit ; Materials science ; Electrochemical gas sensor ; Electrode ; Tin ; Electrochemistry ; Doping ; Heteroatom ; Chemical vapor deposition ; Cadmium ; Analytical Chemistry (journal) ; Nanotechnology ; Optoelectronics ; Chemistry ; Environmental chemistry ; Metallurgy ; Chromatography Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Andrea Ruiz-Escudero | Maxim Belii | Karam Nader | Maite Insausti | Idoia Castellanos‐Rubio | Sonia Arrasate | M. M. Montemore | Humberto González‐Díaz
Source: Machine Learning Science and Technology (article, 2025-09-04)
Field: Materials Science — Biomaterials

IFE.PTML: advancing neurotherapeutic nanoparticle release-system design for enhanced brain delivery Abstract Neurodegenerative diseases (NDDs) are a major global health concern, exacerbated by the blood–brain barrier (BBB), which limits effective drug delivery to the brain. Designing nanoparticles (NPs) to cross the BBB and deliver drugs could greatly improve the treatment of NDDs; however, current approaches to designing materials for drug delivery are generally slow. This study introduces an approach integrating information fusion, Python encoding, perturbation theory, and machine learning to predict the behaviour of NPs as drug delivery systems. Using an extensive pharmacokinetic database, random forest, extreme gradient boosting, and decision tree algorithms are trained and optimised. The random forest model achieves accuracies of 95.1% and 89.7% on training and testing data, respectively. As a proof of concept for the model’s utility, four novel Fe 3 O 4 NP systems are synthesised via thermal decomposition and functionalised for water solubility. Structural, morphological, and magnetic characterisations are performed. The ML model is then applied to these synthesised NPs, with predictions identifying PMAO-coated NPs as promising candidates for BBB and neuronal cell lines. This design framework offers significant advancements in the efficient identification of promising potential NP candidates for NDDs drug delivery, enabling predictions of desirable bioactivity profiles. Nanoparticle ; Nanotechnology ; Delivery system ; Psychology ; Medicine ; Materials science ; Pharmacology Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced Drug Delivery Systems
Authors: Yujin Oh | Heesu Chae | Hyemin Jung | Sandeep Kumar | Sang‐Ho Nam | Yonghoon Lee
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Classification of roasted coffee bean products using laser-induced breakdown spectroscopy: a novel variable selection approach for multiclass modeling The classification of coffee beans by species, origin, and quality is essential in the coffee industry to ensure authenticity and consistency. While existing methods like spectroscopic and chromatographic techniques offer valuable insights, some require complex sample preparation, while others, such as near-infrared (NIR) and visible/near-infrared (VIS/NIR), rely on molecular information that is labile during coffee roasting. Laser-induced breakdown spectroscopy (LIBS), a fast and minimally invasive elemental analysis technique, shows promise for food authentication. In this study, we evaluated the feasibility of combining LIBS with the <i>k</i>-nearest neighbors (<i>k</i>-NN) algorithm to classify 12 roasted coffee bean products available in South Korean markets. LIBS spectra revealed emission peaks for elements such as Li, Na, K, Rb, Mg, Ca, C, H, and O, along with molecular emission bands of CN and C<sub>2</sub>. Using the newly developed statistical concept of the 'inter-to-intraclass variation ratio,' the emission intensities of Li, Na, and Rb were identified as key discriminatory variables for the classification model. The <i>k</i>-NN model achieved a classification accuracy of 96.0% with <i>k</i> = 1, which improved to 98.5% with standard deviation-based scaling and <i>k</i> = 3. It should be emphasized that the model based on the Li, Na, and Rb composition is not expected to be labile during the coffee bean roasting process. These findings underscore the potential of LIBS, combined with a simple machine-learning algorithm, as a practical and efficient tool for authenticating coffee products, leveraging its high sensitivity to alkali metal elements for rapid and accurate classification. Green coffee ; Consistency (knowledge bases) ; Selection (genetic algorithm) ; Laser-induced breakdown spectroscopy ; Quality (philosophy) ; Feature selection ; Chemistry ; Spectroscopy ; Coffee bean ; Chromatography ; Mathematics ; Food science ; Computer science ; Process engineering ; Biochemical engineering ; Machine learning ; Artificial intelligence ; Physics ; Engineering Spectroscopy and Chemometric Analyses ; Laser-induced spectroscopy and plasma ; Identification and Quantification in Food
Authors: Qiancheng Zhao | Hao Liu | Chuan Liu | Takeo Minari | Se Hyun Kim | Xiaowu Tang | Xuying Liu
Source: Materials Horizons (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Additive manufacturing of MXene electrodes: from rheology-tunable nanoinks to size-scalable integrated electronics Additive manufacturing (AM) has emerged as a transformative strategy for translating nanoscale materials into wafer-scale functional architectures, empowering the scalable fabrication of advanced electronics and energy systems. MXenes-a class of two-dimensional transition metal carbides and nitrides-exhibit exceptional electrical conductivity, tunable surface terminations, and mechanical compliance, positioning them as ideal candidates for AM-driven printed electronics. This review uniquely emphasizes the rheological-structural-functional correlations that underpin the evolution of MXene inks from individual nanosheets to architected wafer-scale systems. We dissect critical rheological parameters-including shear-thinning behavior, yield stress, and colloidal stability-and their decisive roles in shaping printability, pattern resolution, and post-print structural fidelity across AM techniques such as direct ink writing, spray coating, and electrohydrodynamic printing. Furthermore, the multiscale influence of rheology on printed micro-/nanostructures and their downstream impacts on electronic, electrochemical, and sensing performance are systematically analyzed. We summarize the latest advances in scalable MXene integration for applications including microsupercapacitors, field effect transistors (FETs), and self-powered biosensors. Finally, we highlight future research directions encompassing machine-learning-assisted ink formulation, hierarchical porous structure design, and eco-conscious processing paradigms. These insights pave the way for intelligent ink systems and hybrid device architectures, propelling MXene electronics toward multifunctional, sustainable, and industry-compatible futures. Electronics ; Nanomanufacturing ; Fabrication ; Printed electronics ; Flexible electronics ; Inkwell ; Nanoscopic scale ; Stretchable electronics MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Olivier Vincent | P. Bergeron | P. Dufour
Source: Monthly Notices of the Royal Astronomical Society (article, 2025-03-18)
Field: Chemistry — Analytical Chemistry

Synthetic Spectroscopy for White Dwarf Classification: Addressing Label Uncertainty and Class Imbalance Abstract With the imminent data releases from next-generation spectroscopic surveys, hundreds of thousands of white dwarf spectra are expected to become available within the next few years, increasing the data volume by an order of magnitude. This surge in data has created a pressing need for automated tools to efficiently analyze and classify these spectra. Although machine learning algorithms have recently been applied to classify large spectroscopic datasets, they remain constrained by the limited availability of training data. The Sloan Digital Sky Survey (SDSS) serves as the current standard training set, as it provides the largest collection of labeled spectra; however, it faces challenges related to severe class imbalance and uncertain label consistency across different surveys. In this work, we address these limitations by training histogram gradient-boosted classifiers on a synthetic SDSS dataset to identify six ubiquitous chemical signatures in the atmosphere of white dwarfs, and test them on 14,246 objects with SNR&amp;gt;10 SDSS spectroscopy. We show our approach not only surpasses human expert performance, but also enables subtype classification and effectively resolves label transferability issues. The methodology developed here is adaptable to any spectroscopic survey, providing a critical tool for the astronomical community. Physics ; White dwarf ; Spectroscopy ; Astrophysics ; Class (philosophy) ; Astronomy ; Stars ; Artificial intelligence Spectroscopy and Chemometric Analyses