jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ismail Abdulazeez | Sani I. Abba | Jamilu Usman | A. G. Usman | Isam H. Aljundi
Source: ACS Applied Nano Materials (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions The rising global demand for brine resources necessitates the exploration of alternative sources to complement existing natural sources. It is imperative to explore innovative approaches, such as emerging machine learning-aided tools, to ensure sustainable and secure brine resources. We proposed a kernel support vector regression (k-SVR), and Gaussian process regression (GPR) based on several feature engineering selectivity approaches that were employed for modeling adsorption energy (kcal/mol). For this purpose, two different scenarios of crown-embedded 2D materials using first-principles density functional theory simulations were obtained. Subsequently, ensemble machine learning (ML) was employed to improve the accuracy of prediction skills of the 2D materials. The data for the successful creation of ion transmission channels utilizing 9-crown-3 (distance within cavity O9–O6 = 3.105 Å, O3–O6 = 2.934 Å, O3–O9 = 2.961 Å) and 12-crown-4 (distance within cavity O3–O9 = 4.538 Å, O6–O12 = 3.223 Å) molecules on graphene, hexagonal boron nitride, and silicene nanosheets were used in this study. The predictive results proved that GPR-C1 with a numerical comparison of RMSE = 0.096, NSE = 0.9610 in the training phase and RMSE = 0.6630, NSE = 0.911 in the testing phase outperformed the other model combinations. The study also proposed federated learning for reliable modeling and recovery of complex and poor selectivity of brine resources. The present study utilizes ML algorithms to provide insights into brine resource recovery, which contributes to multiple sustainable development goals, addressing environmental, economic, and social dimensions of sustainable development. Machine learning ; Support vector machine ; Brine ; Artificial intelligence ; Computer science ; Kriging ; Graphene ; Materials science ; Algorithm ; Process engineering ; Nanotechnology ; Engineering ; Chemistry Machine Learning in Materials Science ; Chemical Synthesis and Characterization ; Covalent Organic Framework Applications
Authors: Xiaoya Chang | Qingzhao Chu | Dongping Chen
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Monitoring the melting behavior of boron nanoparticles using a neural network potential The melting behavior of metal additives is fundamental for various propulsion and energy-conversion applications. A neural network potential (NNP) is proposed to examine the size-dependent melting behaviors of boron nanoparticles. Our NNP model is proven to possess a desirable computational efficiency and retain <i>ab initio</i> accuracy, allowing investigation of the physicochemical properties of bulk boron crystals from an atomic perspective. In this work, a series of NNP-based molecular dynamics simulations were conducted and numerical evidence of the size-dependent melting behavior of boron nanoparticles with diameters from 3 to 6 nm was reported for the first time. Evolution of the intermolecular energy and the Lindemann index are used to monitor the melting process. A liquid layer forms on the particle surface and further expands with increased temperature. Once the liquid layer reaches the core region, the particle is completely molten. The reduced melting temperature of the boron nanoparticle decreases with its particle size following a linear relationship with reciprocal size, similar to other commonly used metals (Al and Mg). Additionally, boron nanoparticles are more sensitive to particle size than Al particles and less sensitive than Mg particles. These findings provide an atomistic perspective for developing manufacturing techniques and tailoring combustion performance in practical applications. Boron ; Nanoparticle ; Artificial neural network ; Nanotechnology ; Melting temperature ; Materials science ; Biological system ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; nanoparticles nucleation surface interactions
Authors: Bowen Ma | Fangyuan Yu | Ping Zhou | Xiao Wu | Chunlin Zhao | Cong Lin | Min Gao | Tengfei Lin | Baisheng Sa
Source: Journal of Materials Informatics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning accelerated discovery of high transmittance in (K0.5Na0.5)NbO3-based ceramics High optical transmittance (T%) has always been an important indicator of transparent-ferroelectric ceramics for optoelectronic coupling. However, the pathway of pursuing high transparency has been at the experimental trial-and-error stage over the past decades, manifesting major drawbacks of being time-consuming and resource-wasting. The present work introduces a machine learning (ML) accelerated development of highly transparent-ferroelectrics by taking potassium-sodium niobate (KNN)-based ceramics as the model material. It is highlighted that by using a small data set of 118 sample data and four key features, we predict the T% of un-synthesized KNN-based ceramics and evaluate the importance of key features. Meanwhile, the screened (K0.5Na0.5)0.956Tb0.004Ba0.04NbO3 ceramics were successfully realized by the conventional solid-state synthesis, and the experimental measured T% is in full agreement with the predicted results, exhibiting a satisfactory high T% of ~78% at 800 nm. In addition, ML is also used to explore the best experimental parameters, and the prediction results of T% are particularly sensitive to changes in sintering temperature (ST). Eventually, the predicted optimal ST is highly consistent with the experimental one. This study constructs a new avenue for exploring high T% ferroelectric KNN ceramics based on ML, ascertaining optimal process parameters, and guiding the development of other transparent-ferroelectrics in optoelectronic fields. Ceramic ; Materials science ; Transmittance ; Transparency (behavior) ; Ferroelectricity ; Transparent ceramics ; Computer science ; Optoelectronics ; Machine learning ; Artificial intelligence ; Composite material ; Dielectric Ferroelectric and Piezoelectric Materials
Authors: Yating Hu | Benxue Ma | Huting Wang | Yujie Li | Yuanjia Zhang | Guowei Yu
Source: Foods (article, 2023-04-25)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables. Hyperspectral imaging ; Extreme learning machine ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Genetic algorithm ; Preprocessor ; Pesticide residue ; Algorithm ; Computer science ; Pesticide ; Machine learning ; Artificial neural network ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Noah Hoffmann | Tiago F. T. Cerqueira | Pedro Borlido | Antonio Sanna | Jonathan Schmidt | Miguel A. L. Marques
Source: npj Computational Materials (article, 2023-08-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Searching for ductile superconducting Heusler X2YZ compounds Abstract Heusler compounds attract a great deal of attention from researchers thanks to a wealth of interesting properties, among which is superconductivity. Here we perform an extensive study of the superconducting and elastic properties of the cubic (full-)Heusler family using a mixture of ab initio methods, as well as interpretable and predictive machine-learning models. By analyzing the statistical distributions of these properties and comparing them to anti-perovskites, we recognize universal behaviors that should be common to all conventional superconductors while others turn out to be specific to the material family. In total, we discover a total of eight hypothetical materials with critical temperatures above 10 K to be compared with the current record of T c = 4.7 K in this family. Furthermore, we expect most of these materials to be highly ductile, making them potential candidates for the manufacture of wires and tapes for superconducting magnets. Superconductivity ; Condensed matter physics ; Materials science ; Ab initio ; Magnet ; Ab initio quantum chemistry methods ; Physics ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Junxiong Hu | Yulei Han | Xiao Chi | Ganesh Ji Omar | Mohammed Mohammed Esmail Al Ezzi | Jian Gou | Xiaojiang Yu | Andrivo Rusydi | Kenji Watanabe | Takashi Taniguchi | Andrew T. S. Wee | Zhenhua Qiao | Ariando Ariando
Source: Advanced Materials (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Tunable Spin‐Polarized States in Graphene on a Ferrimagnetic Oxide Insulator Spin-polarized two-dimensional (2D) materials with large and tunable spin-splitting energy promise the field of 2D spintronics. While graphene has been a canonical 2D material, its spin properties and tunability are limited. Here, this work demonstrates the emergence of robust spin-polarization in graphene with large and tunable spin-splitting energy of up to 132 meV at zero applied magnetic fields. The spin polarization is induced through a magnetic exchange interaction between graphene and the underlying ferrimagnetic oxide insulating layer, Tm<sub>3</sub> Fe<sub>5</sub> O<sub>12</sub> , as confirmed by its X-ray magnetic circular dichroism (XMCD). The spin-splitting energies are directly measured and visualized by the shift in their Landau-fan diagram mapped by analyzing the measured Shubnikov-de-Haas (SdH) oscillations as a function of applied electric fields, showing consistent fit with the first-principles and machine learning calculations. Further, the observed spin-splitting energies can be tuned over a broad range between 98 and 166 meV by field cooling. The methods and results are applicable to other 2D (magnetic) materials and heterostructures, and offer great potential for developing next-generation spin logic and memory devices. Ferrimagnetism ; Spintronics ; Graphene ; Condensed matter physics ; Materials science ; Spin polarization ; Magnetic circular dichroism ; Spin (aerodynamics) ; Magnetic field ; Spin pumping ; Heterojunction ; X-ray magnetic circular dichroism ; Topological insulator ; Ferromagnetism ; Spin Hall effect ; Physics ; Nanotechnology ; Electron ; Magnetization ; Spectral line ; Quantum mechanics Graphene research and applications ; 2D Materials and Applications ; Quantum and electron transport phenomena
Authors: Fenglei Han | Yang Lv | Yan Liu | Xuefu Zhang | Wenbing Yu | Chongsheng Cheng | Wei Yang
Source: Construction and Building Materials (article, 2023-05-31)
Field: Chemistry — Spectroscopy

Exploring interpretable ensemble learning to predict mechanical strength and thermal conductivity of aerogel-incorporated concrete Thermal conductivity ; Compressive strength ; Aerogel ; Materials science ; Ensemble learning ; Machine learning ; Flexural strength ; Hyperparameter optimization ; Composite material ; Artificial intelligence ; Support vector machine ; Computer science Aerogels and thermal insulation ; Building materials and conservation ; Surface Modification and Superhydrophobicity
Authors: Jintao Zhang | Jinchang Yin | Ruiran Lai | Yue Wang | Baorui Mao | Haonan Wu | Li Tian | Yuanzhi Shao
Source: Nanomaterials (article, 2023-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation Gold nanorods (GNRs) coated with silica shells are excellent photothermal agents with high surface functionality and biocompatibility. Understanding the correlation of the coating process with both structure and property of silica-coated GNRs is crucial to their optimizing preparation and performance, as well as tailoring potential applications. Herein, we report a machine learning (ML) prediction of coating silica on GNR with various preparation parameters. A total of 306 sets of silica-coated GNRs altogether were prepared via a sol-gel method, and their structures were characterized to extract a dataset available for eight ML algorithms. Among these algorithms, the eXtreme gradient boosting (XGboost) classification model affords the highest prediction accuracy of over 91%. The derived feature importance scores and relevant decision trees are employed to address the optimal process to prepare well-structured silica-coated GNRs. The high-throughput predictions have been adopted to identify optimal process parameters for the successful preparation of dumbbell-structured silica-coated GNRs, which possess a superior performance to a conventional cylindrical core-shell counterpart. The dumbbell silica-coated GNRs demonstrate an efficient enhanced photothermal performance in vivo and in vitro, validated by both experiments and time domain finite difference calculations. This study epitomizes the potential of ML algorithms combined with experiments in predicting, optimizing, and accelerating the preparation of core-shell inorganic materials and can be extended to other nanomaterial research. Materials science ; Coating ; Photothermal therapy ; Nanorod ; Nanotechnology ; Nanomaterials ; Biocompatibility ; Computer science ; Machine learning Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Machine Learning in Materials Science
Authors: Kyle R. Lennon | Gareth H. McKinley | James W. Swan
Source: Data-Centric Engineering (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A data-driven method for automated data superposition with applications in soft matter science Abstract The superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences. Typically, this superposition is performed manually, or recently through the application of one of a few automated algorithms. However, these methods are often heuristic in nature, are prone to user bias via manual data shifting or parameterization, and lack a native framework for handling uncertainty in both the data and the resulting model of the superposed data. In this work, we develop a data-driven, nonparametric method for superposing experimental data with arbitrary coordinate transformations, which employs Gaussian process regression to learn statistical models that describe the data, and then uses maximum a posteriori estimation to optimally superpose the data sets. This statistical framework is robust to experimental noise and automatically produces uncertainty estimates for the learned coordinate transformations. Moreover, it is distinguished from black-box machine learning in its interpretability—specifically, it produces a model that may itself be interrogated to gain insight into the system under study. We demonstrate these salient features of our method through its application to four representative data sets characterizing the mechanics of soft materials. In every case, our method replicates results obtained using other approaches, but with reduced bias and the addition of uncertainty estimates. This method enables a standardized, statistical treatment of self-similar data across many fields, producing interpretable data-driven models that may inform applications such as materials classification, design, and discovery. Interpretability ; Computer science ; Nonparametric statistics ; Superposition principle ; Machine learning ; Parametric statistics ; Artificial intelligence ; Data mining ; Heuristic ; Salient ; Data point ; Experimental data ; Process (computing) ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sarah E. Bamford | Dilek Yalçın | Wil Gardner | David A. Winkler | Thomas M. Kohl | Benjamin W. Muir | Shaun C. Howard | E.A. Bruton | Paul J. Pigram
Source: Analytical Chemistry (article, 2023-05-12)
Field: Chemistry — Electrochemistry

Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data The self-organizing map with relational perspective mapping (SOM-RPM) is an unsupervised machine learning method that can be used to visualize and interpret high-dimensional hyperspectral data. We have previously used SOM-RPM for the analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) hyperspectral images and three-dimensional (3D) depth profiles. This provides insightful visualization of features and trends of 3D depth profile data, using a slice-by-slice view, which can be useful for highlighting structural flaws including molecular characteristics and transport of contaminants to a buried interface and characterization of spectra. Here, we apply SOM-RPM to stitched ToF-SIMS data sets, whereby the stitched data are used to train the same model to provide a direct comparison in both 2D and 3D. We conduct an analysis of spin-coated polyaniline (PANI) films on indium tin oxide-coated glass slides that were subjected to heat treatment under atmospheric conditions to model PANI as a conformal aerospace industry coating. Replicates were shown to be precisely equivalent, both spatially and by composition, indicating a clear threshold for annealing of the film. Quantitative assessment was performed on the chemical breakdown trends accompanying annealing based on peak ratios, while spectral analysis alone shows only very subtle differences which are difficult to evaluate quantitatively. The SOM-RPM method considers data sets in their totality and highlights subtle differences between samples often simply differences in peak intensity ratios. Hyperspectral imaging ; Polyaniline ; Chemistry ; Self-organizing map ; Analytical Chemistry (journal) ; Annealing (glass) ; Artificial intelligence ; Computer science ; Materials science ; Cluster analysis ; Polymer ; Composite material ; Chromatography Electrochemical Analysis and Applications ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Ruihao Yuan | Abinash Kumar | Shihao Zhuang | Nicholas Cucciniello | Teng Lü | Deqing Xue | Aubrey Penn | Alessandro R. Mazza | Q. X. Jia | Yun Liu | Dezhen Xue | Jinshan Li | Jia‐Mian Hu | James M. LeBeau | Aiping Chen
Source: Nano Letters (article, 2023-05-24)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State Heterogeneities in structure and polarization have been employed to enhance the energy storage properties of ferroelectric films. The presence of nonpolar phases, however, weakens the net polarization. Here, we achieve a slush-like polar state with fine domains of different ferroelectric polar phases by narrowing the large combinatorial space of likely candidates using machine learning methods. The formation of the slush-like polar state at the nanoscale in cation-doped BaTiO<sub>3</sub> films is simulated by phase field simulation and confirmed by aberration-corrected scanning transmission electron microscopy. The large polarization and the delayed polarization saturation lead to greatly enhanced energy density of 80 J/cm<sup>3</sup> and transfer efficiency of 85% over a wide temperature range. Such a data-driven design recipe for a slush-like polar state is generally applicable to quickly optimize functionalities of ferroelectric materials. Slush ; Ferroelectricity ; Polar ; Materials science ; Polarization (electrochemistry) ; Nanotechnology ; Optics ; Optoelectronics ; Chemistry ; Physics ; Composite material ; Dielectric Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Multiferroics and related materials
Authors: Fei Liang | Jing Ding | Xiao‐Lan Wei | Gechuanqi Pan | Shule Liu
Source: International Journal of Heat and Mass Transfer (article, 2023-09-21)
Field: Materials Science — Materials Chemistry

Interfacial heat and mass transfer at silica/binary molten salt interface from deep potential molecular dynamics Molten salt ; Materials science ; Molecular dynamics ; Chemical physics ; Heat transfer ; Thermal diffusivity ; Mass transfer ; Thermodynamics ; Microstructure ; Chemical engineering ; Chemistry ; Composite material ; Computational chemistry ; Metallurgy Thermal properties of materials ; Phase Change Materials Research ; Material Dynamics and Properties