jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Moataz K. Abdrabou | Xue Han | Yimin Zeng | Ying Zheng
Source: Energy & Fuels (article, 2024-10-15)
Field: Chemistry — Analytical Chemistry

Enhancing the Bitumen Partial Upgrading Process with Fe<sub>3</sub>O<sub>4</sub>-Coated Cenospheres and Artificial Neural Network-Driven Process Optimization This study introduces a new approach to partially upgrade oil sand bitumen utilizing waste fly ash cenospheres coated with an Fe3O4 layer. Through the use of a combination of Fe2+ and Fe3+ precursors, the cenospheres were covered with a layer of Fe3O4 to form a new catalyst denoted as (Fe-Ceno), which was subsequently characterized in detail using scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The characterization results investigated the catalyst’s morphology, microstructure, crystalline structure, and surface chemistry, confirming that the Fe3O4 layer was successfully applied. During the catalytic bitumen upgrading, the Fe-Ceno catalyst was dispersed in a liquid hydrogen donor solution to facilitate the transformation of oil sand bitumen into a partially upgraded liquid oil product. The results of the upgrading process demonstrated that the introduction of only 1 wt % of Fe-Ceno led to a significant enhancement in the quality of the upgraded oil, noted by the reduced olefin content to below 1 wt %, the improved phase stability, and the significant reduction in the oil’s viscosity and density to values below 300 cP and 940 kg/m3, respectively, to satisfy the pipeline transportation specifications. Additionally, this study builds beyond the experimental approach and develops a tailored artificial neural network (ANN) model that can accurately predict the rheological properties of the upgraded bitumen without the need to perform additional upgrading experiments. The developed artificial intelligence model was able to successfully predict values, such as viscosity and density, for the upgraded oil samples under different catalytic operating conditions, with a coefficient of determination (R2) of >0.99, an average absolute deviation (AAD) of <0.1%, and a root mean square error (RMSE) of <0.2. With the further leverage of more extensive data sets and the improvement of generalization, this methodology will exhibit the promising potential of ANN models to accelerate advancements in catalyst discovery and optimization to enhance upgrading process efficiency. Artificial neural network ; Cenosphere ; Process (computing) ; Asphalt ; Process engineering ; Materials science ; Chemical engineering ; Computer science ; Biological system ; Engineering ; Artificial intelligence ; Composite material ; Biology Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Asphalt Pavement Performance Evaluation
Authors: Zhengdi Liu | Wenwen Sun
Source: Additive manufacturing (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Enhancing classical Scheil–Gulliver model calculations by predicting generated phases and corresponding compositions through machine learning techniques Materials science ; Artificial intelligence ; Machine learning ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Jerome H. L. Hui | A. Müller | John M. Alred | Maxwell G. Martin | Carlos E. Soares | M. S. Anderson
Source: Journal of Spacecraft and Rockets (article, 2024-10-31)
Field: Chemistry — Organic Chemistry

Automated Identification of Outgassed Chemical Species Using Mass Spectrometry for Scientific Space Missions Current and planned space exploration and life detection missions such as NASA Jet Propulsion Laboratory’s Mars 2020, Mars Sample Return, and Europa Clipper require record-low contamination levels. In this work, we develop a novel automated outgassing contamination species identification method using mass spectrometry data. Thanks to the existing knowledge of ASTM-E1559 and direct analysis in real-time mass spectrometry material testing methods, we improved species outgassing deconvolution and physical characterization techniques. We developed a brand-new combined approach that leverages both tests’ complementary insights into contaminants’ chemical identities. Namely, we rely on the Dromey–Foyster algorithm and the Seven Golden Rules of mass spectrometry analysis to narrow down the probable identified molecules by a factor of 10 compared to the sole use of ASTM-E1559. Furthermore, thanks to recent advances in machine learning spectral analysis, we developed a composite spectral score that ranks all candidate contaminants based on their similarity to the measurements. Ultimately, we validated an automated species identification algorithm on samples of NuSil CV4-2946, Dacron, and a spacecraft cable component. The techniques established in this work enable accurate estimations of outgassed contaminant species’ chemical structure, leading to a comprehensive understanding of the effects of outgassing on scientific objectives. Mass spectrometry ; Astrobiology ; Identification (biology) ; Moon landing ; Space (punctuation) ; Aerospace engineering ; Environmental science ; Computer science ; Physics ; Engineering ; Apollo Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Harpriya Minhas | Arnab Majumdar | Biswarup Pathak
Source: Journal of Physics Condensed Matter (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Comparison of lattice thermal conductivity using <i>ab-initio</i> DFT, machine learning interatomic potentials, and temperature dependent effective potential: a case study of hexagonal BN and BP bilayer Discovering high thermal conductivity materials is essential for various practical applications, particularly in electronic cooling. The significance of two-dimensional (2D) materials lies in their unique properties that emerge due to their reduced dimensionality, making them highly promising for a wide range of applications. Hexagonal boron nitride (BN), both monolayer and bilayer forms, has garnered attention for its fascinating properties. In this work, we focus on bilayer boron phosphide (BP), which is isostructural to its BN analogue. The lattice thermal conductivity of both bilayer BN and BP have been calculated using<i>ab-initio</i>density functional theory, machine learning with the moment tensor potential method, and the temperature-dependent effective-potential method (TDEP). The TDEP approach gives more accurate results for both BN and BP materials. The lattice thermal conductivity of bilayer BP is lower than that of bilayer BN at room temperature, attributed to increased phonon anharmonicity. This study highlights the importance of understanding phonon scattering mechanisms in determining the thermal conductivity of 2D materials, contributing to the broader understanding and potential applications of these materials in future technologies. Thermal conductivity ; Materials science ; Bilayer ; Phonon ; Condensed matter physics ; Ab initio ; Boron nitride ; Bilayer graphene ; Anharmonicity ; Chemical physics ; Graphene ; Nanotechnology ; Chemistry ; Membrane ; Physics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Jiaqi Chen | Junqing Li | Ziyi Liu | Shitao Sun | Shijia Zhou | Dongqi Wang
Source: Artificial Intelligence Chemistry (article, 2024-12-07)
Field: Materials Science — Materials Chemistry

Small-dataset-orientated data-driven screening for catalytic propane activation This work aims at the proper application of machine learning screening of direct propane dehydrogenation (PDH) reaction and oxidative dehydrogenation (ODH) of propane, which are two main protocols to convert propane to propylene and featured by limited available experimental data. Current studies mainly adopt trial-and-error strategy, which is time consuming and raises concerns on environment and health owing to the release of chemical waste. This motivates the introduction of data-driven research paradigm to alleviate the deficiency of the traditional trial-and-error strategy, which however relies on large quantity of high quality data. In this work, a dataset enveloping PDH and ODH data was constructed, and the performance of machine learning algorithms in the study of light alkane activation was evaluated, based on which a strategy appropriate for small dataset was proposed: for small unbalanced datasets, it is sensible to train the model by treating the dataset as a whole rather than to fuse multiple specific models based on divided smaller pieces of data. The results show that the trained models using ensemble algorithms exhibited the best predictability of propylene selectivity, i.e. CatBoost and random forest for PDH and LightGBM for ODH, respectively. Based on the optimal model, the key influencing factors in PDH and ODH were identified. This study demonstrates the proper use of data-driven strategy in the catalytic science, which can be adopted in other scientific problems that suffer from the limited available high quality data and contribute to the gain of novel understanding, e.g. the rational design and optimization of the catalytic systems. • Created a dataset enveloping PDH and ODH data, and proposed a small-dataset-orientated workflow to conduct the data-driven study of the catalytic systems. • Evaluated the performance of machine learning algorithms in the study of light alkane activation, and recommended ensemble algorithms as the algorithms of choice for PDH and ODH. • Ranked the influencing factors in PDH and ODH to implicate to the rational design and optimization of the catalytic systems. Propane ; Catalysis ; Computer science ; Business ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Maridurai Thirupathy | V. Muthuraman
Source:  (article, 2024-02-01)
Field: Materials Science — Polymers and Plastics

Experimental Investigation on the Mechanical Properties of Jute Fiber and Silica Nano Particles Using Artificial Neural Network This study explores the impact of silica nanoparticles on jute fiber-reinforced composites with epoxy resin matrices. Silica nanoparticles were synthesized at three concentrations (3%, 6%, and 9%) and incorporated into composites at varying fiber–resin weight ratios. The composites were subjected to tests for tensile strength, flexural strength, impact strength, and hardness. The Taguchi signal-to-noise ratio method was employed for optimization. Results indicate that a 9% addition of silica nanoparticles significantly enhances the mechanical properties of jute fiber-reinforced composites. Tensile and flexural strength increased with higher silica nanoparticle content, while impact strength and hardness also improved. Notably, a 9% silica addition achieved a maximum tensile strength of 72 MPa, resulting in a 10% increase over that yielded by the 3% addition. Flexural and impact strengths improved by 23% and 20%, respectively, when compared to the 3% silica addition. Furthermore, a neural network model accurately predicted the composite’s mechanical characteristics with 100% accuracy. These findings hold promise for the automobile and aircraft industries, as they require high-performance materials. The integration of jute fibers and silica nanoparticles into composites offers a sustainable and eco-friendly alternative to conventional materials. The enhancement strategy employed in this analysis can be applied to enhance the mechanical properties of other composite materials. Nano- ; Artificial neural network ; Materials science ; Fiber ; Composite material ; Computer science ; Artificial intelligence Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Nanocomposite Films for Food Packaging
Authors: Lennard Wunsch | Martin Hubold | Rico Nestler | Gunther Notni
Source: Sensors (article, 2024-12-14)
Field: Chemistry — Analytical Chemistry

Realisation of an Application Specific Multispectral Snapshot-Imaging System Based on Multi-Aperture-Technology and Multispectral Machine Learning Loops Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi aperture imaging, which enables a simultaneous capture (snapshot) of spectrally selective and spatially resolved scene information. There are some limiting factors for the spectral resolution when implementing this imaging principle, e.g., usable sensor resolutions and area, and required spatial scene resolution or optical complexity. Careful analysis is therefore needed for the specification of the multispectral system properties and its realisation. In this work we present a systematic approach for the application-related implementation of this kind of MSI. We focus on spectral system modeling, data analysis, and machine learning to build a universally usable multispectral loop to find the best sensor configuration. The approach presented is demonstrated and tested on the classification of waste, a typical application for multispectral imaging. Multispectral image ; USable ; Computer science ; Snapshot (computer storage) ; Spectral imaging ; Artificial intelligence ; Computer vision ; Image resolution ; Remote sensing ; Database ; Geology ; Multimedia Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Isaiah A. Moses | Wesley F. Reinhart
Source: Machine Learning Science and Technology (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Transfer learning for multi-material classification of transition metal dichalcogenides with atomic force microscopy Abstract Deep learning models based on atomic force microscopy enhance efficiency in inverse design and characterization of materials. However, the limited and imbalanced data of experimental materials that are typically available is a major challenge. Also important is the need to interpret trained models, which are normally complex enough to be uninterpretable by humans. Here, we present a systemic evaluation of transfer learning strategies to accommodate low-data scenarios in materials synthesis and a model latent feature analysis to draw connections to the human-interpretable characteristics of the samples. While we imagine this framework can be used in downstream analysis tasks such as quantitative characterization, we demonstrate the strategies on a multi-material classification task for which the ground truth labels are readily available. Our models show accurate predictions in five classes of transition metal dichalcogenides (TMDs) (MoS 2 , WS 2 , WSe 2 , MoSe 2 , and Mo-WSe 2 ) with up to 89% accuracy on held-out test samples. Analysis of the latent features reveals a correlation with physical characteristics such as grain density, Difference of Gaussian blob, and local variation. The transfer learning optimization modality and the exploration of the correlation between the latent and physical features provide important frameworks that can be applied to other classes of materials beyond TMDs to enhance the models’ performance and explainability which can accelerate the inverse design of materials for technological applications. Transition metal ; Atomic force microscopy ; Materials science ; Nanotechnology ; Microscopy ; Chemical physics ; Chemistry ; Physics ; Optics ; Catalysis 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Gizem Yucegonul | Kerim Karadağ | Eyyüp Karaoğul
Source: DÜMF Mühendislik Dergisi (article, 2024-05-07)
Field: Chemistry — Analytical Chemistry

A Novel Chemometric Learning Of Virgin And Deep Frying Olive-Oil By Fourier Transform Infrared Spectroscopy (FT-IR) The aim of this study is to examine the machine learning of chemometrically fried oils in virgin olive oil and eight times used olive oil compared using Fourier Transform Infrared spectroscopy (FT-IR). Deep-Frying Oils (DFO was carried out 8 times for 20 minutes. Because of chemical quality of oils, Cis, Trans, Ester, Methyl, Carbonyl, peroxide, unsatrated peroxide and ether groups were used in These results were evaluated by classification and regression using machine learning methods. For these evaluations, firstly classification and regression were made using all properties of these index. In classification models, Support Vector Machines (SVM), K Closest Neighborhood (KNN), Decision Tree (DT) were used. The evaluation was carried out in two stages. In the first stage, half of the dataset was used for training and the other half for testing. In the second stage, all data data was used for training and testing using cross validation (CV) method. The success results obtained using the all data set were 94% with Support Vector Machines and K Nearest Neighborhood methods. According to chemometric strategy, differences between virgin olive oils and DFO were found by high accurancy in this study. This phenomenon also could be possible for other oil type and degree of purity. Results illustrated that the method is very suitable and exact for detection deteriotion of olive oil. Support vector machine ; Olive oil ; Artificial intelligence ; Fourier transform infrared spectroscopy ; Mathematics ; Peroxide ; Chemometrics ; Pattern recognition (psychology) ; Acid value ; Regression analysis ; Training set ; Chemistry ; Computer science ; Statistics ; Chromatography ; Food science ; Engineering ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Gajanan Anne | Nagaraj Bhat | H. M. Vishwanatha | S. Ramesh | Maruthi Prashanth B H | Priyaranjan Sharma | Aditya Kudva S | C. Jagadeesh | Yashwanth Nanjappa
Source: Materials Research Express (article, 2024-08-27)
Field: Materials Science — Biomaterials

Effect of addition of Ce and accumulative roll bonding on structure-property of the Mg-Ce-Al hybrid composite and its prediction and comparison using artificial neural network (ANN) approach Abstract Light alloys play a crucial role in realizing the national strategy for energy conservation and emission reduction, as well as promoting the upgrading of manufacturing industries. Mg/Al composite laminates combine the corrosion resistance and ductility of aluminium alloy with the lightweight characteristics of magnesium alloy. The addition of Ce (rare earth elements) can improve the mechanical properties of magnesium via grain refinement and improve the ductility of the hybrid composites. In the present work, an investigation on addition of Ce into the Mg/Al matrix through Accumulative Roll Bonding (ARB) has been presented. The Mg/Ce/Al hybrid composite consists of Mg-4%Zn alloy and Al 1100 alloy with 0.2% Ce particles added between the dissimilar layers. The changes occurred in the evaluation of microstructure, corrosion and mechanical properties of the Mg/Ce/Al hybrid composite as a result of deformation process and also the addition of Ce have been explicated. The ARB parameters: temperature, rolling speed, percentage reduction, and aging time, have been studied. An increase of about 2.36 times in strength and hardness of the hybrid composite, has been reported. Further, the structure–property relations in the Mg/Ce/Al hybrid composites were aslo predict and compare using machine learning models: Decision Tree and Multi-Layer Perceptron (MLP) models. Artificial neural network ; Composite number ; Property (philosophy) ; Materials science ; Artificial intelligence ; Composite material ; Computer science ; Philosophy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Advanced Welding Techniques Analysis
Authors: Wei Wei | Jun Fang | Ning Yang | Qi Li | Lin Hu | Jie Han | Lanbo Zhao
Source: AIP Advances (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

OPNet: Optimized multi-head graph attention network for polymer properties prediction The optimized multi-head graph attention network (OPNet) uses the multi-head graph attention network to predict both the thermal decomposition temperature with a 5% mass loss (Td5%) and the glass transition temperature (Tg) of polymers as a machine learning model. The OPNet model provides reliable performance predictions for the Td5% and Tg datasets. The OPNet regression evaluation metrics R2 of Td5% is 0.76, which is the best, and the regression evaluation metrics R2 of Tg is 0.91, which is better than the current existing method models (the best model R2 ≈ 0.90). The OPNet model is an end-to-end network model, eliminating the need for manual data filtering or feature extraction. By analyzing the feature weights of the OPNet model, it is found that structures such as benzene rings play a more important role. At the same time, we have confirmed through other literature that such structures do have better stability and a higher thermal decomposition temperature and glass transition temperature. Therefore, the OPNet model exhibits interpretability and holds significant reference value for the field of materials science. Interpretability ; Computer science ; Glass transition ; Graph ; Thermal stability ; Algorithm ; Artificial intelligence ; Materials science ; Polymer ; Theoretical computer science ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Harun Al Azies | Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono
Source: Chemistry Africa (article, 2024-09-30)
Field: Chemistry — Inorganic Chemistry

Robust Machine Learning for Predicting Thermal Stability of Metal-Organic Framework Stability (learning theory) ; Thermal stability ; Metal-organic framework ; Artificial intelligence ; Machine learning ; Computer science ; Chemistry ; Engineering ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yuechen Gao | Haoxiang Lin | Xi Zhu
Source: The Journal of Physical Chemistry Letters (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

General Aqueous System Simulation through an AI-Embedded Metaverse Chemistry Laboratory Recent decades have witnessed the rapid development of autonomous laboratories and artificial intelligence, where experiments can be automatically run and optimized. Although human work is reduced, the total time of experimental optimization is still consuming due to limitations of the current ab metaverse framework, which accurately predicts the future state of the system by receiving and analyzing in situ experimental data. To substitute for traditional simulation methods, we designed a physically endorsed deep learning model to predict the future system picture ranging from atomic image to bulk appearance, intensively using the correlations between properties of the system. Through this framework, we studied the general aqueous system, covering 100+ common ionic solutions. We can accurately simulate properties for a general aqueous system as well as predict the time of solvation of ionic compounds ahead of real experiments. In this way, the experiments can be optimized more efficiently without waiting for the end of a bad iteration. We hope our work offers a fresh direction for the digitization of chemical information, enhancing access to and use of experimental data in advancing the field of physical chemistry. Computer science ; Aqueous solution ; Digitization ; Field (mathematics) ; Solvation ; Work (physics) ; Experimental data ; Biological system ; Artificial intelligence ; Chemistry ; Ion ; Physics ; Thermodynamics ; Physical chemistry ; Mathematics ; Computer vision Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies