jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gnana Praveena Nethala | Ravindar Tadi | Aroli Venkateswara Anupama | Satish Laxman Shinde | V. Veeraiah
Source: Materials Science-Poland (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Correlation between Structural, Magnetic and Spectroscopic Properties of Mg Substituted CoFe<sub>2</sub>O<sub>4</sub> Abstract Mg substituted cobalt ferrite spinel powder samples with the general formula Mg x CO 1-x Fe 2 O 4 (x = 0 to 0.25) were synthesized chemically through sol-gel method and annealed at 1100 °C for 2 h. They were initially screened for the structural and morphological properties by X-ray diffraction and field emission scanning electron microscopy, respectively. Vibrational properties of the samples were studied by Raman and infrared spectroscopies. X-ray diffraction confirmed the formation of single pure or near-pure phase with cubic spinel structure for all the samples with expected occupancy values. The field emission scanning electron microscopy revealed a decrease in the particle size with an increase in Mg concentration. Both structural and magnetic properties of the samples were characterized using Mössbauer spectroscopy while the magnetic properties were studied using vibrating sample magnetometry. The changes in magnetic moment of ions, their coupling with neighboring ions and cation exchange interactions were confirmed from the Mössbauer spectroscopy analysis. Saturation magnetization and coercivity values can be explained based on the Slater-Pauling curve. The magnetometry results showed a decrease in saturation magnetization of the samples with increase in Mg concentration Materials science ; Analytical Chemistry (journal) ; Coercivity ; Raman spectroscopy ; Magnetic moment ; Spinel ; Magnetization ; Magnetometer ; Scanning electron microscope ; Spectroscopy ; Saturation (graph theory) ; Mössbauer spectroscopy ; Nuclear magnetic resonance ; Crystallography ; Magnetic field ; Chemistry ; Condensed matter physics ; Metallurgy Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Iron oxide chemistry and applications
Authors: Bin Li | Shuo-Cheng Zhang | Yao-Dan Chi
Source: Journal of Spectroscopy (article, 2018-01-01)
Field: Chemistry — Spectroscopy

Development and Integration of a CO Detection System Based on Wavelength Modulation Spectroscopy Using Near-Infrared DFB Laser A wavelength modulation spectroscopy- (WMS-) based gas sensing system was established to measure concentration of carbon monoxide (CO) in the range 0–100%. The CO absorption line at 1563.06 nm was scanned with a tunable distributed feedback (DFB) laser, and two InGaAs photodiodes were applied to perform optic-electric conversion. Without using commercial instruments, essential electrical circuits were self-developed and integrated, including laser temperature controller, laser current driver, signal generator, and digital lock-in amplifier. The gas cell deployed in the system was fiber coupled with a total effective optical path length of 50 cm. The second-order harmonic signal was extracted, and experiments of gas detection were carried out to investigate the performance of the sensor, including detection repeatability, detection accuracy, response time, and limit of detection (LoD). Experiment results show that the sensor is reliable and has acceptable probing performance. The maximum relative detection error is less than 3.8%, suggesting good detection stability. Benefiting from the self-developed sensor, the whole CO detection system has small size, affordable expense, and application potential. Laser ; Materials science ; Lock-in amplifier ; Tunable diode laser absorption spectroscopy ; Optoelectronics ; Time delay and integration ; Distributed feedback laser ; SIGNAL (programming language) ; Modulation (music) ; Optics ; Spectroscopy ; Repeatability ; Tunable laser ; Wavelength ; Amplifier ; Computer science ; Chemistry ; Physics Spectroscopy and Laser Applications ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Sensors
Authors: Azam Karimian
Source: Journal of Heterocyclic Chemistry (article, 2018-01-05)
Field: Chemistry — Organic Chemistry

Synthesis of New Pyrimido[5′,4′:5,6][1,4]thiazino[2,3‐<i>b</i>]quinoxaline Derivatives in One Step As a continuation of our search for new heterocyclic compounds, the synthesis of pyrimido[5′,4′:5,6][1,4]thiazino[2,3‐ b ]quinoxaline ring system is described. A series of new derivatives of this heterocyclic system ( 3a–d ) have been synthesized through the one‐pot heterocyclization of the appropriate 5‐amino6‐methylpyrimidine‐4‐thiols and 2,3‐dichloroquinoxaline in the presence of K 2 CO 3 in dimethylformamide under reflux. N‐alkylation of the synthesized compounds with alkyl halides in KOH/dimethylformamide also gave the desired new derivatives of N ‐alkylated pyrimido[5′,4′:5,6][1,4]thiazino[2,3‐ b ]quinoxalines ( 4a–h ). All the synthesized products were characterized and confirmed by their spectroscopic and microanalytical data. Chemistry ; Quinoxaline ; Alkylation ; Dimethylformamide ; Ring (chemistry) ; Alkyl ; Halide ; Combinatorial chemistry ; Medicinal chemistry ; Organic chemistry ; Catalysis Synthesis and Biological Evaluation ; Quinazolinone synthesis and applications ; Synthesis and biological activity
Authors: Volker L. Deringer | Noam Bernstein | Albert P. Bartók | Matthew J. Cliffe | Rachel N. Kerber | Lauren E. Marbella | Clare P. Grey | Stephen R. Elliott | Gábor Cśanyi
Source: arXiv (Cornell University) (preprint, 2018-03-07)
Field: Materials Science — Materials Chemistry

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics. Amorphous silicon ( a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 1011 K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials. Amorphous solid ; Silicon ; Molecular dynamics ; Diffraction ; Materials science ; Atom (system on chip) ; Structure factor ; Amorphous silicon ; Chemical physics ; Interatomic potential ; Crystalline silicon ; Crystallography ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Optoelectronics ; Optics Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; X-ray Diffraction in Crystallography
Authors: Davor Antanasijević | Jelena Antanasijević | Viktor Pocajt
Source: Engineering Applications of Artificial Intelligence (article, 2018-03-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of the transition temperature of bent-core liquid crystals using fuzzy “digital thermometer” model based on artificial neural networks Ranking (information retrieval) ; Artificial neural network ; Computer science ; Quantitative structure–activity relationship ; Mean squared error ; Approximation error ; Mean absolute error ; Artificial intelligence ; Fuzzy logic ; Core (optical fiber) ; Thermometer ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Biological system ; Data mining ; Machine learning ; Algorithm ; Mathematics ; Statistics ; Thermodynamics ; Physics Liquid Crystal Research Advancements ; Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality
Authors: Michael J. Willatt | Félix Musil | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2018-06-30)
Field: Materials Science — Materials Chemistry

A Data-Driven Construction of the Periodic Table of the Elements Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to improve the accuracy of the model for a given amount of reference data. When using a description of the structures that is transparent and well-principled, optimizing the representation might reveal insights into the chemistry of the data set. Here we show how one can generalize the SOAP kernel to introduce a distance-dependent weight that accounts for the multi-scale nature of the interactions, and a description of correlations between chemical species. We show that this improves substantially the performance of ML models of molecular and materials stability, while making it easier to work with complex, multi-component systems and to extend SOAP to coarse-grained intermolecular potentials. The element correlations that give the best performing model show striking similarities with the conventional periodic table of the elements, providing an inspiring example of how machine learning can rediscover, and generalize, intuitive concepts that constitute the foundations of chemistry. Periodic table ; Representation (politics) ; Computer science ; Kernel (algebra) ; Table (database) ; Set (abstract data type) ; Stability (learning theory) ; Component (thermodynamics) ; Scale (ratio) ; Theoretical computer science ; Algorithm ; Biological system ; Data mining ; Machine learning ; Chemistry ; Mathematics ; Physics ; Pure mathematics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Dmitrij Rappoport | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2018-06-22)
Field: Materials Science — Materials Chemistry

Predicting Feasible Organic Reaction Pathways Using Heuristically Aided Quantum Chemistry Studying organic reaction mechanisms using quantum chemical methods requires from the researcher an extensive knowledge of both organic chemistry and first-principles computation. The need for empirical knowledge arises because any reasonably complete exploration of the potential energy surfaces (PES) of organic reactions is computationally prohibitive. We have previously introduced the Heuristically-Aided Quantum Chemistry (HAQC) approach to modeling complex chemical reactions, which abstracts the empirical knowledge in terms of chemical heuristics—simple rules guiding the PES exploration—and combines them with structure optimizations using quantum chemical methods. The HAQC approach makes use of heuristic kinetic criteria for selecting reaction paths that are not only plausible, that is, consistent with the empirical rules of organic reactivity, but also feasible under the reaction conditions. In this work, we develop heuristic kinetic feasilibity criteria, which correctly predict feasible reaction pathways for a wide range of simple polar (substitutions, additions, and eliminations) and pericyclic organic reactions (cyclizations, sigmatropic shifts, and cycloadditions). In contrast to knowledge-based reaction mechanism prediction methods, the same kinetic heuristics are successful in classifying reaction pathways as feasible or infeasible across this diverse set of reaction mechanisms. We discuss the energy profiles of HAQC and their potential applications in machine learning of chemical reactivity. Pericyclic reaction ; Heuristics ; Heuristic ; Computer science ; Chemistry ; Computational chemistry ; Biochemical engineering ; Quantum ; Quantum chemistry ; Biological system ; Reaction mechanism ; Artificial intelligence ; Organic chemistry ; Physics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Taylor D. Sparks | Steven Kauwe | Taylor Welker
Source: ChemRxiv (preprint, 2018-10-23)
Field: Materials Science — Materials Chemistry

Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning The field of materials science has seen an explosion in the amount of accessible high quality data. With this sudden surge of data, the application of machine learning (ML) onto materials data has led to great results. Particular success has been found in training models based on chemical formula. Such models have traditionally focused on learning from density functional theory (DFT) or experimental data. Though some researchers have explored the use of DFT calculated properties as features for learning, this has not gained much traction since the machine learning predictions would be limited by the DFT computation time and accuracy. In this work, we explore the use of a stacked ensemble learning system that combines machine learning from DFT calculations to improve learning on experimental data. This is accomplished by handling the DFT and experimental data separately, training distinct models for each. The DFT models are used to generate a "predicted DFT" value for the formulae in the experimental data. A meta-learner-trained using predictions generated by the experimental models combined with predictions from the DFT models-is shown to improve root-mean-squared-error by over 9% in the test data, when compared to a baseline model that only learns from the training data. Experimental data ; Computer science ; Machine learning ; Artificial intelligence ; Density functional theory ; Computation ; Mean squared error ; Ensemble learning ; Test data ; Algorithm ; Mathematics ; Computational chemistry ; Chemistry ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ruslan Skrynkovskyy | Volodymyr Yuzevych | Olha Ogirko | Grzegorz Pawlowski | Zaklad Handlowo-Uslugowy BHP, Gorzyca, Poland
Source: Journal of Engineering Sciences (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue This paper presents results of the use of Big Data approach and neural network for the pipelines diagnosis problem. In this case the pipeline is in the conditions of crack growth of corrosion fatigue and exposed to hydrogen. It is proposed to use graphene protective coatings. The mathematical model for estimating the changes in the effective surface energy of WPL during plastic deformation, electrochemical overstrain, polarization potential and current density of the metal dissolution reaction at the top of the crack on the pipeline surface during its mechanical loading in an aqueous electrolyte solution is given. The dissolution of the metal is considered on the juvenile surface, taking into account the anode and cathode regions based on the approaches of surface physics and electrochemistry. An element of a mathematical model is a quality functional, taking into account information flows and a sensitivity coefficient. Functional quality is used to specify the feedback between the investment project methodology and risk estimates, as well as to optimize the information flows of enterprises and improve the system of protection of metallic underground pipelines that operate under conditions of corrosion fatigue. The purpose of this project is to improve the relevant regulatory and technical documents as well as software. Pipeline transport ; Corrosion ; Materials science ; Anode ; Polarization (electrochemistry) ; Computer science ; Structural engineering ; Forensic engineering ; Mechanical engineering ; Metallurgy ; Engineering Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Industrial Engineering and Technologies
Authors: Ali Mohsenian | Mohamadreza Zakerzadeh | Masoud Shariat Panahi | Alireza fakhrzade
Source: Applied and Computational Mechanics (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Modeling SMA actuated systems based on Bouc-Wen hysteresis model and feed-forward neural network Despite the fact that shape-memory alloy (SMA) has several mechanical advantages as it continues being used as an actuator in engineering applications, using it still remains as a challenge since it shows both non-linear and hysteretic behavior. To improve the efficiency of SMA application, it is required to do research not only on modeling it, but also on control hysteresis behavior of these materials which are the fundamentals of several research opportunities in this area. Having considered these requirements, we have introduced a mathematical model to describe the hysteresis behavior of a mechanical system attached to SMA wire actuators using Bouc-Wen hysteresis model and feed-forward neural network. Due to inability of linear mass-spring-damper equations of classic Bouc-wen model to explain the hysteresis behavior of SMA actuators, in this paper we have applied changes in the mentioned equations of classic Bouc-Wen model to describe hysteresis loops of model. We also have used flexibility of the neural network systems to describe Bouc-Wen output in the main equation. Parameters of the developed model have been trained for a real mechanical system using simulation data after selecting proper configuration for the selected neural network. Finally, we have checked the accuracy of our model by applying two different series of validation data. The result shows the acceptable accuracy of the developed model. SMA* ; Hysteresis ; Artificial neural network ; Actuator ; Control theory (sociology) ; Shape-memory alloy ; Computer science ; Feed forward ; Flexibility (engineering) ; Nonlinear system ; Feedforward neural network ; Control engineering ; Engineering ; Algorithm ; Artificial intelligence ; Control (management) ; Mathematics ; Physics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Pablo Márquez-Neila | Chloe Fisher | Raphael Sznitman | Kevin Heng
Source: arXiv (Cornell University) (preprint, 2018-06-11)
Field: Chemistry — Spectroscopy

Supervised Machine Learning for Analysing Spectra of Exoplanetary Atmospheres The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find the best-fit model. Known as atmospheric retrieval, it is a technique that originates from the Earth and planetary sciences. Such methods are very time-consuming and by necessity there is a compromise between physical and chemical realism versus computational feasibility. Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods. Here, we report an adaptation of the random forest method of supervised machine learning, trained on a pre-computed grid of atmospheric models, which retrieves full posterior distributions of the abundances of molecules and the cloud opacity. The use of a pre-computed grid allows a large part of the computational burden to be shifted offline. We demonstrate our technique on a transmission spectrum of the hot gas-giant exoplanet WASP-12b using a five-parameter model (temperature, a constant cloud opacity and the volume mixing ratios or relative abundance by number of water, ammonia and hydrogen cyanide). We obtain results consistent with the standard nested-sampling retrieval method. Additionally, we can estimate the sensitivity of the measured spectrum to constraining the model parameters and we can quantify the information content of the spectrum. Our method can be straightforwardly applied using more sophisticated atmospheric models and also to interpreting an ensemble of spectra without having to retrain the random forest. Exoplanet ; Hyperparameter optimization ; Computer science ; Machine learning ; Grid ; Opacity ; Artificial intelligence ; Algorithm ; Support vector machine ; Physics ; Mathematics ; Stars ; Computer vision ; Optics Molecular spectroscopy and chirality ; Spectroscopy and Laser Applications
Authors: Hiroki Nada
Source: ACS Omega (article, 2018-05-30)
Field: Materials Science — Biomaterials

A New Methodology for Evaluating the Structural Similarity between Different Phases Using a Dimensionality Reduction Technique A new methodology for definitively evaluating the structural similarity between different phases in an impartial manner is proposed. This methodology utilizes a dimensionality reduction (DR) technique that was developed in the fields of machine learning and statistics. The basis of the proposed methodology is that the structural similarity between different phases can be evaluated by the geometrical similarity of pair and/or angular distribution functions that reflect the atomic-scale structure of each phase. The DR technique is used for the analysis of this geometrical similarity. In this study, the proposed methodology is applied to evaluate the similarity in the atomic-scale structure, as obtained from molecular dynamics simulations, between amorphous CaCO<sub>3</sub> and CaCO<sub>3</sub> crystal phases in the presence or absence of additives, namely, Mg<sup>2+</sup> ions, Sr<sup>2+</sup> ions, and water molecules. The results indicate that in the absence of additives, the structure of the amorphous phase is closer to that of vaterite than to those of calcite or aragonite. However, the degree of structural similarity between the amorphous phase and vaterite decreases if Mg<sup>2+</sup> ions are present. This tendency is also evident when Sr<sup>2+</sup> ions are present, although these ions do not influence the structure of the amorphous phase as strongly as Mg<sup>2+</sup> ions. In addition, the results indicate that at a high water concentration, the amorphous phase is separated into small particles by hydrogen-bonded networks of water molecules and the structure of the amorphous phase more closely approaches that of vaterite. The proposed methodology is widely applicable to the evaluation of the structural similarity between different phases for complex multicomponent systems. Vaterite ; Amorphous solid ; Similarity (geometry) ; Aragonite ; Phase (matter) ; Ion ; Molecular dynamics ; Materials science ; Atomic units ; Chemical physics ; Chemistry ; Crystallography ; Calcite ; Computational chemistry ; Physics ; Mineralogy ; Computer science ; Artificial intelligence ; Organic chemistry Calcium Carbonate Crystallization and Inhibition ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: F. Menacer | Abdelmalek Kadr | Zohir Dibi
Source: International Journal of Automation and Computing (article, 2018-12-06)
Field: Materials Science — Materials Chemistry

Modeling of a Smart Nano Force Sensor Using Finite Elements and Neural Networks MOSFET ; Artificial neural network ; Sensitivity (control systems) ; Capacitive sensing ; Threshold voltage ; Voltage ; Transistor ; Materials science ; Linearization ; Nanoelectronics ; Carbon nanotube ; Field-effect transistor ; Nanosensor ; Electronic engineering ; Computer science ; Electrical engineering ; Nanotechnology ; Engineering ; Nonlinear system ; Physics ; Artificial intelligence Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Nanotechnology research and applications