jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aprilyani Nur Safitri | Gustina Alfa Trisnapradika | Achmad Wahid Kurniawan | Wahyu Aji Eko Prabowo | Muhamad Akrom
Source: Journal of Multiscale Materials Informatics (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors. Benzimidazole ; Corrosion ; Computer science ; Chemistry ; Artificial intelligence ; Materials science ; Metallurgy ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Material Properties and Failure Mechanisms
Authors: Xusheng Gao | Junxia Ma | Fengfu Li | Qian Zhou | Dan Gao
Source: Preparative Biochemistry & Biotechnology (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Optimization of the extraction process of total steroids from <i>Phillinus gilvus</i> (Schwein.) Pat. by artificial neural network (ANN)-response surface methodology and identification of extract constituents <i>Phillinus gilvus</i> (Schwein.) Pat has pharmacological effects such as tonifying the spleen, dispelling dampness, and strengthening the stomach, in which sterol is one of the main compounds of <i>P. gilvus</i>, but there has not been thought you to its extraction and detailed identification of its composition, in the present study, we used artificial neural network (ANN) and response surface methodology (RSM) to optimize the conditions of ultrasonic-assisted extraction, and the parameters of the independent and interaction effects were evaluated. Ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF-MS/MS) was used to identify the major components in the purified extract. The results showed that the optimal extraction process conditions were: ultrasonic time 96 min, ultrasonic power 140 W, liquid to material ratio 1:25 g/ml, and ultrasonic temperature 30.7 °C. The compliance rates of the predicted and experimental values for the artificial neural network model and the response surface model were 98.3% and 96.12%, respectively, indicating that both models have the potential to be used for optimizing the extraction process of <i>P. gilvu</i>s in industry. A total of 120 compounds and 30 major steroids were identified by comparison with the reference compounds. Among the major steroidal components are these findings will contribute to the isolation and utilization of active ingredients in <i>P. gilvus.</i> Chromatography ; Extraction (chemistry) ; Response surface methodology ; Chemistry ; Artificial neural network ; Artificial intelligence ; Computer science Chromatography in Natural Products ; Phytochemical Studies and Bioactivities ; Steroid Chemistry and Biochemistry
Authors: Prakriti P. Joshi | Davis Unruh | Nasim Mirzajani | Thomas E. Gage | Luqing Wang | Ruiyu Li | Haihua Liu | Liangbo Liang | Ilke Arslan | Maria K. Y. Chan | Sarah B. King
Source: ChemRxiv (preprint, 2024-06-20)
Field: Materials Science — Materials Chemistry

Anomalous reduction in black phosphorus phonon velocities driven by lattice anisotropy Phonon dynamics and transport determines how heat is utilized or dissipated in materials. In anisotropic 2D materials for miniaturized optoelectronics or thermoelectrics, the impact of material geometry and nanoscale material structure on phonon propagation is central to controlling thermal conduction. Here, we directly observe {in-plane} phonon propagation and dynamics in intrinsically anisotropic black phosphorus (BP) using ultrafast electron microscopy. We directly measure coherent acoustic phonon group velocities on the nanoscale along each of the Bravais lattice vectors (zigzag and armchair) and in an intermediate lattice direction that is a linear combination of the Bravais lattice vectors. We identify an anomalously low group velocity, deviating strongly from theoretical calculations, for coherent acoustic phonons travelling in the intermediate lattice direction. A machine learning-based model, calculating the phonon properties of an >8000 atom supercell, reveals the low group phonon velocity results from a mixing of the transverse and longitudinal acoustic phonons in BP that is independent of material morphology. This work demonstrates how transport of coherent phonons may be controlled through material geometry, while also highlighting how new avenues for directional control of nanoscale heat transport remain undiscovered even within well-known corrugated layered materials like BP. Anisotropy ; Black phosphorus ; Phonon ; Condensed matter physics ; Lattice (music) ; Reduction (mathematics) ; Materials science ; Physics ; Geometry ; Quantum mechanics ; Mathematics ; Optoelectronics 2D Materials and Applications
Authors: Akash Talapatra | Bahareh Nojabaei | Pooya Khodaparast
Source: SPE Improved Oil Recovery Conference (article, 2024-04-22)
Field: Chemistry — Organic Chemistry

A Data-Based Continuous and Predictive Viscosity Model for the Oil-Surfactant-Brine Microemulsion Phase Abstract This study presents a computationally produced data-based model/correlation that can accurately estimate the magnitude and predict the peaks of microemulsion viscosity at dynamic reservoir conditions. Equilibrium molecular dynamics (MD) simulation is used on a decane-SDS-brine interfacial system to generate a dataset of viscosity values as a function of different temperatures, surfactant concentrations, and salinities. The viscosity testing and training data are computationally measured using the Einstein relation of the Green-Kubo formula. Several machine learning (ML) based regression algorithms, including K-nearest Neighbors (KNN), Support Vector regression (SVR), Multivariate Polynomial Regression (MLPR), Light Gradient Boosting Machine (LGBM), and Decision Tree (DT), are used to train the model. The SVR regression provides the best performputaance for our model compared to other methods with an R2 (0.978 and 0.963 for train and test data, respectively) and mean absolute error value (0.059 and 0.072 for train and test data, respectively). The chosen model is then used to predict microemulsion viscosity for different reservoir conditions. The proposed model aims to accurately estimate microemulsion viscosity at dynamic reservoir conditions with variable input parameters such as pressure, temperature, brine salinity, and surfactant concentration, enabling accurate estimation and prediction of the transport properties of reservoir fluids and present phases at reservoir conditions, which is key to achieving maximum recovery during chemical EOR. Brine ; Microemulsion ; Pulmonary surfactant ; Viscosity ; Materials science ; Chemical engineering ; Phase (matter) ; Petroleum engineering ; Enhanced oil recovery ; Chromatography ; Chemistry ; Composite material ; Geology ; Organic chemistry ; Engineering Surfactants and Colloidal Systems ; Enhanced Oil Recovery Techniques ; Rheology and Fluid Dynamics Studies
Authors: Jianping Xu | Tao Zu | Yi‐Cheng Hsu | Yi Sun | Yi Zhang
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Deep Reconstruction Framework with Self-calibration Mechanisms (DEISM) for Accelerated CEST Imaging The widespread clinical adoption of chemical exchange saturation transfer (CEST) imaging has been hampered by its prolonged scan time, due to multiple data acquisitions over the varying saturation offset frequencies. In this work, we utilize the artifact suppression algorithm and propose an effective deep reconstruction framework with self-calibration mechanisms (DEISM). The DEISM method was validated on brain tumor patients at 3T. In conjunction with deep-learning multi-coil image reconstruction and data-driven artifact suppression mechanisms, DEISM can provide reliable reconstructions of highly accelerated CEST data, yielding superior performance compared to state-of-the-art methods. Offset (computer science) ; Iterative reconstruction ; Artifact (error) ; Calibration ; Computer science ; Artificial intelligence ; Chemistry ; Physics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Ya. N. Gladchenko-Djevelekis | Darya Tolchina | V. V. Srabionyan | В. А. Дурыманов | L. A. Avakyan | Л. А. Бугаев
Source: Nanobiotechnology Reports (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Determining the Architecture of Ensembles of Bimetallic PtCu Nanoparticles Based on Atomic Radial Distribution Functions It is known that the catalytic properties of materials based on bimetallic PtCu nanoparticles depend on both the composition and the distribution of atoms in these particles. Therefore, the development of new materials with improved catalytic properties requires the application of an accurate and reliable experimental method for determining the architecture of nanoparticles (NPs) (random solid solution, Janus, core–shell or “gradient”). Our previous study demonstrated through machine-learning simulations that the architecture of single bimetallic nanoparticles can be determined using accurate theoretically calculated paired atomic radial distribution functions (RDFs), which can also be obtained from the most common sources of NP structural information, such as the X-ray absorption spectroscopy (XAS) and X-ray diffraction (XRD) techniques. This work is a logical continuation of the research mentioned above and is devoted to a theoretical study of the influence of errors in determining the RDFs, as well as the influence of the size and composition distributions of nanoparticles on the possibility of determining the architecture of nanoparticles from their RDFs. Bimetallic strip ; Nanoparticle ; Nanotechnology ; Architecture ; Distribution (mathematics) ; Materials science ; Computer science ; Mathematics ; Metal ; Metallurgy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: D. A. Mitkovskiy | Alexei А. Lazutin | А. Л. Талис | V. V. Vasilevskaya
Source: Soft Matter (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Self-assembly of amphiphilic homopolymers grafted onto spherical nanoparticles: complete embedded minimal surfaces and a machine learning algorithm for their recognition By means of computer modelling, the self-assembly of amphiphilic A-<i>graft</i>-B macromolecules, grafted onto a spherical nanoparticle, is studied. In a solvent, that is poor for side pendants, the macromolecules self-assemble into thin membrane-like ABBA bilayers deviated from spherical nanoparticles. The bilayers form morphological structures that depend on the grafting density and macromolecular polymerization degree and can be referred to as the classical family of complete embedded minimal surfaces. The plane disk, catenoid, helicoid, Costa and Enneper surfaces as well as "double" helicoid and "complex minimal surface" were identified, and the fields of their stability were defined. The surfaces can be grouped according to the sequences of conformal transformations that transform them into each other. These surfaces arise in different experiments situationally. Results are summarized in a pie diagram constructed using a machine learning algorithm based on matching grafting points with a specially created planar graphic image. Amphiphile ; Nanoparticle ; Algorithm ; Materials science ; Self-assembly ; Computer science ; Nanotechnology ; Copolymer ; Polymer ; Composite material Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications
Authors: Ahsan Javed | Shahzad Akhtar Ali | Muhammad Asif | Rafi Ullah
Source: Materials Letters (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Structural and electronic properties of monolayers: Enhancing computational accuracy Materials science ; Monolayer ; Electronic structure ; Nanotechnology ; Computational chemistry 2D Materials and Applications ; Molecular Junctions and Nanostructures ; Graphene research and applications
Authors: Keiichiro Takahashi | Keita Sakakibara | Hiroshi Watanabe | Yoshinobu Tsujii | Hiroshi Mamitsuka
Source: ChemRxiv (preprint, 2024-01-17)
Field: Materials Science — Materials Chemistry

Estribec: Fast, Precise Estimation of Stribeck Curves in Tribology Tribology is the study on friction, wear and lubrication for contacting and moving two solid materials with fluid lubricants. Friction can be found easily in our life and should be reduced, and lubricants are a powerful answer for this purpose. The mechanism behind friction and lubrication has been primarily analyzed by the Stribeck curve, which should be 1) interpretable, 2) non-homogeneous in variances and 3) a mixture of Stribeck curves further. We propose a machine learning approach, Estribec, to estimate the Stribeck curves from observed data in tribology. Estribec is, considering all above three characteristics, a finite mixture model, with a component of a piecewise function, where each piece is a comprehensible simple (primarily, linear) function with a unique variance. Entirely our method keeps linear time complexity for all processes, including parameter estimation and prediction. Empirical results with synthetic data showed Estribec achieved favorable predictive performance against Gaussian process regression (GPR) and its tree variant (TGPR). Importantly Estribec ran always around 20 to 150 times faster than GPR and TGPR, which would be a sizable difference for conducting high-throughput experiments. Finally Estribec showed latent properties of real data which would be useful for tribology research. Tribology ; Piecewise linear function ; Lubrication ; Piecewise ; Kriging ; Gaussian process ; Function (biology) ; Materials science ; Mathematics ; Applied mathematics ; Computer science ; Gaussian ; Statistics ; Mathematical analysis ; Physics ; Composite material Machine Learning in Materials Science ; Lubricants and Their Additives ; Gear and Bearing Dynamics Analysis
Authors: Hiromichi Nakahara | Kazutaka Koga | K. Matsuoka
Source: Molecules (article, 2024-10-19)
Field: Chemistry — Organic Chemistry

Distinct Solubilization Mechanisms of Medroxyprogesterone in Gemini Surfactant Micelles: A Comparative Study with Progesterone The solubilization behavior of medroxyprogesterone (MP) within gemini surfactant micelles (14-6-14,2Br<sup>-</sup>) was investigated and compared with that of progesterone to uncover distinct solubilization mechanisms. We employed <sup>1</sup>H-NMR and 2D ROESY spectroscopy to elucidate the spatial positioning of MP within the micelle, revealing that MP integrates more deeply into the micellar core. This behavior is linked to the unique structural features of MP, particularly its 17β-acetyl group, which promotes enhanced interactions with the hydrophobic regions of the micelle, while the 6α-methyl group interacts with the hydrophilic regions of the micelle. The 2D ROESY correlations specifically highlighted interactions between the hydrophobic chains of the surfactant and two protons of MP, H22 and H19. Complementary machine learning and electron density analyses supported these spectroscopic findings, underscoring the pivotal role of the molecular characteristics of MP in its solubilization behavior. These insights into the solubilization dynamics of MP not only advance our understanding of hydrophobic compound incorporation in gemini surfactant micelles but also indicate the potential of 14-6-14,2Br<sup>-</sup> micelles for diverse drug delivery applications. Solubilization ; Micelle ; Pulmonary surfactant ; Medroxyprogesterone acetate ; Medroxyprogesterone ; Chemistry ; Biochemistry ; Hormone ; Organic chemistry Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies
Authors: Maarten Konings | Tomás González‐Lezana | Simen Camps | Jérôme Loreau
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Quantum and statistical state-to-state studies of cold Ar + H<sub>2</sub><sup>+</sup> collisions In this work we present new state-to-state integral scattering cross sections and initial-state selected rate coefficients for the <sup>36</sup>Ar (<sup>1</sup>S) + H<sub>2</sub><sup>+</sup> (X<sup>2</sup>Σ<sub>g</sub><sup>+</sup>,<i>v</i> = 0,<i>j</i>) reactive system for collision energies up to 0.1 eV (with respect to the <sup>36</sup>Ar (<sup>1</sup>S) + H<sub>2</sub><sup>+</sup> (X<sup>2</sup>Σ<sub>g</sub><sup>+</sup>,<i>v</i> = 0,<i>j</i> = 0) channel). To the best of our knowledge, these cross sections are the first fully state resolved ones that were obtained by performing time-independent quantum mechanical and quantum statistical calculations. For this purpose a new full-dimensional ground state <sup>2</sup>A' adiabatic electronic potential energy surface was calculated at the MRCI+Q/aug-cc-pVQZ level of theory, which was fitted by means of machine learning methods. We find that a statistical quantum method and a statistical adiabatic channel model reproduce quantum mechanical initial-state selected cross sections fairly well, thus suggesting that complex-forming mechanisms seem to be playing an important role in the reaction dynamics of the reaction that was studied. State (computer science) ; Scattering ; Work (physics) ; Atomic physics ; Physics ; Quantum ; Chemistry ; Nuclear physics ; Thermodynamics ; Quantum mechanics ; Mathematics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Cold Atom Physics and Bose-Einstein Condensates
Authors: Balázs Nagyillés | Gergely Nagy | Bálint Kiss | E. Cormier | Péter Földi | Katalin Varjú | Subhendu Kahaly | Mousumi Upadhyay Kahaly | Zsolt Divéki
Source: Optics Express (article, 2024-09-19)
Field: Chemistry — Spectroscopy

MIR laser CEP estimation using machine learning concepts in bulk high harmonic generation Monitoring the carrier-envelope phase (CEP) is of paramount importance for experiments involving few-cycle intense laser fields. Common measurement techniques include f-2f interferometry or stereo-ATI setups. Here we demonstrate a new concept, both by simulations and by experiments, for CEP estimation in the mid-infrared regime using machine learning (ML) techniques that rely on the observation of the spectrum of high harmonic generation (HHG) in bulk material. Once the ML model is trained, the method provides a way for cheap and compact in-situ CEP tagging. This technique can complement other CEP monitoring methods, can capture the complex correlation between the CEP and the observable HHG spectra, and is readily generalizable for any laser wavelengths. Optics ; High harmonic generation ; Laser ; Second-harmonic generation ; Computer science ; Harmonic ; Laser beams ; Materials science ; Physics ; Acoustics Spectroscopy and Laser Applications ; Laser Design and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jashanpreet Singh | Satish Kumar | Hitesh Vasudev | Ranvijay Kumar
Source: Recent Patents on Mechanical Engineering (article, 2024-03-26)
Field: Materials Science — Materials Chemistry

Erosion Prediction Gaussian Process Regression Algorithm for Alumina and Chromia Reinforced Nickel-Based High-Velocity Oxy-Fuel Coatings Background: Machine learning (ML) methodologies have demonstrated efficacy in the determination of erosion rates and material removal. In this context, a novel Erosion Prediction Gaussian Process Regression Algorithm (EPGPRA) was developed to predict the volumetric erosion in thermal spray coatings. Aim: In this patent, a novel EPGPRA based model was developed to predict the volumetric loss of 30Al2O3 and 20Cr2O3 reinforced Ni-based coatings deposited using a high-velocity oxy-fuel (HVOF) process. Objective: The objective of this patent is to develop a GPR model for the prediction of Ni-30Al2O3 and Ni-20Cr2O3 coatings. Methods: Spraying powders were applied to the SS316L steel substrate in order to develop coatings. An erosion tester was used in order to investigate the wear resistance of HVOF-coated steel. The gathered experimental dataset is put to use in the construction of a powerful GPR model. The outcomes from GPR model were then measured against the values obtained from the experiments. To demonstrate the accuracy of the GPR model, the produced model is evaluated against various cutting-edge machine learning methods. Results: This innovation was successful in terms of developing a new GPR model for wear prediction. The discrepancy between the actual and expected values is the smallest for Matern 5/2 (M5/2) GPR in the validation set. It was also lesser as compared to Ensemble Boosted Trees, Support Vector Machine, Linear regression, and Fine Tree. In terms of MSE, MAE, RMSE, and R2 the accuracy performance of the M5/2 GPR model was determined to be 9.8565×10-5, 0.0048884, 0.009928, and 0.93 correspondingly. Ni-Chromia coating performed better than the Ni-Alumina. Conclusion: As per this patent, a novel EPGPRA-based model was developed, which is the better machine learning technique for wear prediction of Ni-based HVOF coatings. Chromia ; Kriging ; Regression ; Process (computing) ; Nickel ; Materials science ; Gaussian ; Gaussian process ; Computer science ; Metallurgy ; Algorithm ; Alloy ; Mathematics ; Machine learning ; Statistics ; Chemistry ; Computational chemistry Catalytic Processes in Materials Science ; Coal Combustion and Slurry Processing ; Radiative Heat Transfer Studies