jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunzhen Du | Jiaojiao Cheng | Jizheng Duan | Meiling Qi | Yanwei Chen | Yuan Yao | Wen‐Shan Duan | Lei Yang | Sheng Zhang | Ping Lin
Source: Computational Materials Science (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Thermal conductivity predictions in monolayer MoSi2N4: Integrating neural network potentials with phonon scattering analysis Phonon ; Thermal conductivity ; Monolayer ; Condensed matter physics ; Scattering ; Materials science ; Phonon scattering ; Thermal ; Conductivity ; Chemical physics ; Physics ; Nanotechnology ; Thermodynamics ; Optics ; Quantum mechanics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Duy Khanh Ninh | Kha Duy Phan | Thu Thi Anh Nguyen | Dang Minh Nhat | Nhan Le Thanh | Fabien Ferrero
Source: Applied Sciences (article, 2024-09-24)
Field: Chemistry — Analytical Chemistry

Classification of Urea Content in Fish Using Absorbance Near-Infrared Spectroscopy and Machine Learning Near-infrared (NIR) spectroscopy has become a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on urea content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes—Safe and Unsafe—based on their urea content. A comprehensive NIR dataset comprising 11,960 spectra collected from eight distinct positions within the fish body was obtained from 299 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their urea content exceeded the permissible limit of 1000 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques, as well as ML algorithms, were explored. The results demonstrated that utilizing NIR data specifically obtained from the outer skin of the stomach yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their first derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers, achieving an accuracy of up to 83.9%. Near-infrared spectroscopy ; Absorbance ; Fish <Actinopterygii> ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Environmental science ; Chemistry ; Chromatography ; Fishery ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Samer Adwan | Madeiha Qasmieh | Faisal Al-Akayleh | Ahmed S.A. Ali Agha
Source: Preprints.org (preprint, 2024-09-20)
Field: Chemistry — Organic Chemistry

Recent Advances in Ocular Drug Delivery: Insights into Lyotropic Liquid Crystals Background/Objectives: This review examines the evolution of lyotropic liquid crystals (LLCs) in ocular drug delivery, focusing on their ability to address the challenges associated with traditional ophthalmic formulations. The study aims to underscore the enhanced bioavailability, prolonged retention, and controlled release properties of LLCs that significantly improve therapeutic outcomes; Methods: The review synthesizes data from various studies on both bulk-forming LLCs and liquid crystal nanoparticles (LCNPs). It also considers advanced analytical techniques, including the use of machine learning and AI-driven predictive modeling, to forecast the phase behavior and molecular structuring of LLC systems. Emerging technologies in biosensing and real-time diagnostics are discussed to illustrate the broader applicability of LLCs in ocular health; Results: LLCs are identified as pivotal in promoting targeted drug delivery across different regions of the eye, with specific emphasis on the tailored optimization of LCNPs. The review highlights principal categories of LLCs used in ocular applications, each facilitating unique interactions with physiological systems to enhance drug efficacy and safety. Additionally, novel applications in biosensing demonstrate LLCs&amp;#039; capacity to improve diagnostic processes; Conclusions: Lyotropic liquid crystals offer transformative potential in ocular drug delivery by overcoming significant limitations of conventional delivery methods. The integration of predictive technologies and biosensing applications further enriches the utility of LLCs, indicating a promising future for their use in clinical settings. This review points to continued advancements and encourages further research in LLC technology to maximize its therapeutic benefits. Drug delivery ; Lyotropic ; Lyotropic liquid crystal ; Drug ; Materials science ; Nanotechnology ; Liquid crystal ; Medicine ; Liquid crystalline ; Pharmacology ; Optoelectronics Carbohydrate Chemistry and Synthesis ; Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality
Authors: Alice E. A. Allen | Gábor Cśanyi
Source: The Journal of Chemical Physics (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Toward transferable empirical valence bonds: Making classical force fields reactive The empirical valence bond technique allows classical force fields to model reactive processes. However, parametrization from experimental data or quantum mechanical calculations is required for each reaction present in the simulation. We show that the parameters present in the empirical valence bond method can be predicted using a neural network model and the SMILES strings describing a reaction. This removes the need for quantum calculations in the parametrization of the empirical valence bond technique. In doing so, we have taken the first steps toward defining a new procedure for enabling reactive atomistic simulations. This procedure would allow researchers to use existing classical force fields for reactive simulations, without performing additional quantum mechanical calculations. Valence bond theory ; Parametrization (atmospheric modeling) ; Quantum ; Valence (chemistry) ; Statistical physics ; Force field (fiction) ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Longsheng Feng | Sijia Huang | Tae Wook Heo | Juergen Biener
Source: ACS Applied Materials & Interfaces (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Integrated Framework to Model Microstructure Evolution and Decipher the Microstructure–Property Relationship in Polymeric Porous Materials Unraveling the microstructure-property relationship is crucial for improving material performance and advancing the design of next-generation structural and functional materials. However, this is inherently challenging because it requires both the comprehensive quantification of microstructural features and the accurate assessment of corresponding properties. To meet these requirements, we developed an efficient and comprehensive integrated modeling framework, using polymeric porous materials as a representative model system. Our framework generates microstructures using a physics-based phase-field model, characterizes them using various average and localized microstructural features, and evaluates microstructure-aware properties, such as effective diffusivity, using an efficient Fourier-based perturbation numerical scheme. Additionally, the framework incorporates machine learning methods to decipher the intricate microstructure-property relationships. Our findings indicate that the connectivity of phase channels is the most critical microstructural descriptor for determining effective diffusivity, followed by the domain shape represented by curvature distribution, while the domain size has a minor impact. This comprehensive approach offers a novel framework for assessing microstructure-property relationships in polymer-based porous materials, paving the way for the development of advanced materials for diverse applications. Microstructure ; Materials science ; Porosity ; DECIPHER ; Thermal diffusivity ; Curvature ; Nanotechnology ; Biological system ; Computer science ; Composite material ; Bioinformatics ; Thermodynamics ; Geometry Block Copolymer Self-Assembly ; Solidification and crystal growth phenomena ; Advanced Mathematical Modeling in Engineering
Authors: Chunsheng Yan | Zhongyi Cheng | Linquan Cao | Yingke Wen
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-01-06)
Field: Chemistry — Analytical Chemistry

Enhanced 3-D asynchronous correlation data preprocessing method for Raman spectroscopy of Chinese handmade paper Principal component analysis ; Computer science ; Artificial intelligence ; Preprocessor ; Pattern recognition (psychology) ; Data pre-processing ; Support vector machine ; Convolutional neural network ; Dot product ; Tensor (intrinsic definition) ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Ángel Díaz Carral | Martín Roitegui | Maria Fyta
Source: APL Materials (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Interpretably learning the critical temperature of superconductors: Electron concentration and feature dimensionality reduction A machine learning model for predicting the critical temperature of novel superconductors is proposed. The novelty of this approach is based solely on the choice of ab initio features, that is, descriptors directly and solely related to the electronic and atomic information of the single elements that chemically bond to form superconductors. We could show that selecting features, such as the electron concentration in the materials and the electronegativity from the available superconductor data, allows for a significant reduction in the learning dimensionality. At the same time, this choice provides a prediction accuracy in critical temperature up to 93% (relevant to a mean absolute error of 4.2 K) similar to more complex models using a significantly higher feature space. In total, the features could be reduced down to 11 in addition to the multidimensional electronic concentration (including 17 features for the atomic orbitals), emphasizing the significantly higher importance of electronegativity and electron concentration. The latter is mainly influenced by the 3s orbital followed by the 3p orbital. This choice is physically intuitive as it directly links to the electronic orbitals in the superconductors that mostly influence the learning, thus defining the accuracy of the predicted critical temperature. At the same time, the learning process is interpretable, providing a deep insight that could be proven invaluable in developing new theories on novel superconductor materials. Electronegativity ; Superconductivity ; Atomic orbital ; Curse of dimensionality ; Materials science ; Dimensionality reduction ; High-temperature superconductivity ; Machine learning ; Statistical physics ; Condensed matter physics ; Artificial intelligence ; Electron ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Addis Fuhr | Panchapakesan Ganesh | Rama K. Vasudevan | Kevin M. Roccapriore | Bobby G. Sumpter
Source: Applied Physics Letters (article, 2024-01-15)
Field: Materials Science — Materials Chemistry

Digital twins and deep learning segmentation of defects in monolayer MX2 phases Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments. Segmentation ; Artificial intelligence ; Monolayer ; Deep learning ; Computer science ; Materials science ; Identification (biology) ; Nanotechnology ; Pattern recognition (psychology) ; Machine learning MXene and MAX Phase Materials ; Semiconductor materials and devices ; Machine Learning in Materials Science
Authors: Mohammad Nashir Uddin | M. Mostafizur Rahman | M. Nur Alam Likhon | M. Sarwar Jahan
Source: Nordic Pulp & Paper Research Journal (article, 2024-11-28)
Field: Chemistry — Analytical Chemistry

Unlocking potential: the role of chemometric modeling in pulp and paper manufacturing Abstract The review focuses on application of chemometric modeling in raw material characterization and quantification of parameter in various steps of pulp and papermaking processes. These models are built by supervised or unsupervised machine-learning techniques. Chemometric models are predominantly developed with data from spectroscopic instruments like, Ultraviolet spectrophotometer (UV), Near Infrared spectrophotometer (NIR), Fourier Transform Infrared Spectrometer (FTIR), Raman Spectrometer etc. These spectroscopic data are large in size which are reduced by applying different dimension reduction techniques. Moreover, these data contain instrumental noise in most of the cases, and they are de-noised or smoothed by several preprocessing techniques for developing better performing models. Among the popular calibration techniques for classification, Soft Independent Modeling for Classification Algorithm (SIMCA), Discrimination Analysis (DA) are mentionable. Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Square Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM) etc., are used for quantification of different physical, morphological and chemical properties of wood, pulp and paper properties. Based on chemometric models, sensors for online measurements of different parameters in pulping and papermaking processes are being developed in recent years. Through this review, better performing multivariate analysis based chemometric modeling techniques have been identified for determining different parameters by comparing the existing ones which could be used in different processes in pulp and papermaking industries. Industrial chemistry ; Pulp (tooth) ; Biochemical engineering ; Process engineering ; Pulp and paper industry ; Materials processing ; Chemometrics ; Environmental science ; Computer science ; Engineering ; Machine learning Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Soil Geostatistics and Mapping
Authors: Q. Ge | Weiping Zhu
Source: ACS Applied Nano Materials (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Partial Solution-Guided Machine Learning Enhanced the Impact Resistance Analysis of Prestretched Graphenes This study aims to investigate the effect of prestretching on a graphene’s ballistic limit velocity under impact loading, which is the maximum impact velocity that graphenes can withstand without being penetrated by a bullet. Molecular dynamics simulations and machine learning were employed to analyze the ballistic limit velocity of graphenes under various prestretching conditions. A machine learning model was developed by integrating a partial solution into the loss function to expedite the determination of the ballistic limit velocity. The physical mechanisms of the prestretching effect under impact loading were explained based on the stiffness theory of corrugated plates. The study revealed that uniaxial stretching is superior to equibiaxial stretching in inducing the prestretching effect. The machine learning model effectively predicted the ballistic limit velocity of graphene at different tensions and bullet sizes. When the bullet radius is small, the ballistic limit velocity exhibits significant fluctuations with increasing tension, whereas it decreases monotonically with increasing tension when the bullet radius is large. Although the positive effects of prestretching diminish with an increase in the number of graphene layers, prestretching significantly reduces the interlayer spacing of the graphene Whipple structure. These results provide valuable insights into the application of graphene in engineering, particularly in enhancing its ballistic performance. The findings suggest that prestretching can be an effective engineering manipulation to improve the impact resistance of graphene-based materials, which can be crucial for designing more effective protective materials and devices. Computer science ; Materials science ; Machine learning Graphene research and applications ; Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications
Authors: Markus Ostermann | Lukas Kalchgruber | Jürgen Schodl | Peter A. Lieberzeit | Pierluigi Bilotto | Markus Valtiner
Source: Carbon Trends (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Tailoring the properties of graphene nanosheets during electrochemical exfoliation The large-scale production of graphene remains a significant bottleneck in harnessing the potential of this material. Electrochemical exfoliation offers a green, sustainable production protocol that is suitable for industrial scale-up. However, the material produced often suffers from a low yield and limited functional groups, which restricts its use in advanced applications. In this study, we introduce a mathematical model that elucidates the intricate influences of production parameters, such as temperature and potential, on the characteristics of the product. A comprehensive understanding of the exfoliation process is achieved through detailed insights provided by X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray diffraction, and powder conductivity measurements. Design-of-Experiment and Pareto analysis are employed to determine the optimal production conditions. As a result, graphene nanosheets, tailored with specific physical and chemical properties ( e.g. , functional groups, conductivity), can be produced. Furthermore, we describe the significant influence of the cation during sulfate-based anodic exfoliation, which allows for efficiency and cost optimization. In general, the tailoring aspect of this work paves the way towards the industrial production of graphene nanosheets, tailored to the intended application. Simultaneously, the experimental design lays the foundation for a data-driven machine learning method for the optimal synthesis of sustainable two-dimensional materials. • Detailed description of parameter influences on electrochemical graphene nanosheet production. • Establishment of a mathematical model to tailor graphene nanosheet production to the respective needs. • Investigation of cationic influences in an anodic exfoliation process. Graphene ; Exfoliation joint ; Electrochemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Electrode Graphene research and applications ; Advancements in Battery Materials ; Semiconductor materials and devices
Authors: Minjeong Park | Yong Gyun Shin | Belal Almomani | Yoon‐Suk Chang
Source: Nuclear Engineering and Design (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Comprehensive integrity assessment of spent nuclear fuel cladding during normal and postulated conditions of transportation Structural integrity ; Cladding (metalworking) ; Nuclear engineering ; Nuclear fuel ; Forensic engineering ; Materials science ; Engineering ; Structural engineering ; Metallurgy Nuclear Materials and Properties ; Nuclear and radioactivity studies ; Graphite, nuclear technology, radiation studies
Authors: Kehao Qiang | Shisong Wang | Haowen Wang | Zhulin Zeng | Liangzhao Qi
Source: Materials (article, 2024-01-27)
Field: Materials Science — Materials Chemistry

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions The hot deformation behavior of titanium matrix composites plays a crucial role in determining the performance of the formed components. Therefore, it is significant to establish an accurate constitutive relationship between material deformation parameters and flow stress. In this study, hot compression experiments were conducted on a (2.5 vol%TiB + 2.5 vol%TiC)/TC4. The experiments were performed under temperatures ranging from 1013.15 to 1133.15 K and strain rates ranging from 0.001 to 0.1 s<sup>-1</sup>. Based on the stress-strain data obtained from the experiment, the constitutive models were established by using the Arrhenius model and the BP neural network algorithm, respectively. Considering the relationship between strain rate, hot working temperature, and flow stress, a comparative analysis was conducted to evaluate the prediction accuracy of two different constitutive models. The research results indicate that the flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 increases with decreasing temperature and increasing strain rate, and the stress-strain curve shows obvious work hardening and softening behaviors. Both the Arrhenius model and the BP neural network algorithm are effective in predicting the hot compression flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4, but the average relative error and root mean square error of the BP neural network algorithm are smaller and the correlation coefficient is higher, thus possessing higher accuracy and reliability. Flow stress ; Materials science ; Constitutive equation ; Composite material ; Strain rate ; Deformation (meteorology) ; Compression (physics) ; Arrhenius equation ; Strain hardening exponent ; Stress (linguistics) ; Hardening (computing) ; Structural engineering ; Activation energy ; Finite element method ; Engineering Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Advanced materials and composites