jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifei Wang | Kai Wang | Zhao Zhou | Wenli Du
Source: Frontiers of Chemical Science and Engineering (article, 2019-04-23)
Field: Chemistry — Analytical Chemistry

Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm Transfer of learning ; Similarity (geometry) ; Computer science ; Near-infrared spectroscopy ; Process (computing) ; Raw data ; Feature (linguistics) ; Infrared ; Infrared spectroscopy ; Artificial intelligence ; Chemometrics ; Biological system ; Algorithm ; Process engineering ; Data mining ; Pattern recognition (psychology) ; Machine learning ; Chemistry ; Engineering ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Robin Findlay Hendry
Source: Synthese (article, 2019-07-24)
Field: Chemistry — Physical and Theoretical Chemistry

Elements and (first) principles in chemistry Abstract The first principle of chemical composition is that elements are actually present in their compounds. It is a golden thread running through the history of compositional thinking in chemistry since before the chemical revolution. Opposed to this principle, which I call Actually Present Elements (APE), is the idea that elements are merely potentially present in their compounds: although not actually present, it is possible to recover them. In this paper I follow that golden thread, and then discuss the status of APE itself: is it true? What arguments were there for it, and when? I argue that APE is a metaphysical principle, albeit at a lower level of generality and abstraction than the term ‘metaphysical’ usually suggests. I critically examine a range of different views on how metaphysical principles might be involved in research programmes in empirical science, and conclude by endorsing Elie Zahar’s view that metaphysical principles such as APE are to be found at the very heart of science. Moreover, they can be recipients of empirical support just like other parts of scientific theory. Metaphysics ; Philosophy of science ; Generality ; Epistemology ; Philosophy of language ; Philosophy ; Abstraction ; Scientific theory ; Thread (computing) ; Chemistry ; History of science ; Computer science ; Psychology History and advancements in chemistry ; Philosophy and History of Science ; Computational Drug Discovery Methods
Authors: Saleh S. Hayek
Source: Advances in Materials Science and Engineering (article, 2019-11-27)
Field: Materials Science — Materials Chemistry

Synthesis and Characterization of CeGdZn-Ferrite Nanoparticles as Magnetic Hyperthermia Application Agents This study focuses on the synthesis and characterization of a magnetic nanomaterial used in magnetic hyperthermia. Cerium gadolinium zinc-iron (CeGdZnFe) magnetic nanoparticles were synthesized by a coprecipitation method for application as magnetic hyperthermia agents. Determination of phase purity and their identification was achieved by X-ray diffraction studies using a Phillips powder diffractometer with Cu Kα radiation. Typical TEM micrographs of the dispersion of CeGdZn-ferrite nanoparticles and CeGdZn-ferrite PEG-encapsulated nanoparticles in ethanol deposited over a Cu grid were taken as part of the characterization techniques to be used for newly developed materials. It was then fitted by a Gaussian distribution with mean diameter dm ±1.0 nm. The investigation of magnetic properties showed that adjusting, Gd and Zn contributes to the nanoparticles added to the adjustment of all magnetic properties of CeGdZnFe. Materials science ; Coprecipitation ; Nanoparticle ; Magnetic hyperthermia ; Diffractometer ; Ferrite (magnet) ; Magnetic nanoparticles ; Characterization (materials science) ; Nanomaterials ; Gadolinium ; Magnetism ; Chemical engineering ; Nuclear magnetic resonance ; Analytical Chemistry (journal) ; Nanotechnology ; Composite material ; Metallurgy ; Chromatography ; Scanning electron microscope Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Daichi Minami | Tokuteru Uesugi | Yorinobu Takigawa | Kenji Higashi
Source: International Journal of Modern Physics B (article, 2019-03-21)
Field: Materials Science — Materials Chemistry

Artificial neural network assisted by first-principles calculations for predicting transformation temperatures in shape memory alloys A key property for the design of new shape memory alloys is their working temperature range that depends on their transformation temperature T 0 . In previous works, T 0 was predicted using a simple linear regression with respect to the energy difference between the parent and the martensitic phases, [Formula: see text]E[Formula: see text]. In this paper, we developed an accurate method to predict T 0 based on machine learning assisted by the first-principles calculations. First-principles calculations were performed on 15 shape memory alloys; then, we proposed an artificial neural network method that used not only computed [Formula: see text]E[Formula: see text] but also bulk moduli as input variables to predict T 0 . The prediction error of T 0 was improved to 49 K for the proposed artificial neural network compared with 188 K for simple linear regression. Artificial neural network ; Shape-memory alloy ; Transformation (genetics) ; Simple (philosophy) ; Range (aeronautics) ; Computer science ; Diffusionless transformation ; Energy (signal processing) ; Regression ; Linear regression ; Algorithm ; Artificial intelligence ; Materials science ; Thermodynamics ; Martensite ; Machine learning ; Mathematics ; Physics ; Statistics ; Chemistry Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; High Entropy Alloys Studies
Authors: Hasan Sh. Majdi | A. N. Saud | S. N. Saud
Source: Materials (article, 2019-05-29)
Field: Materials Science — Ceramics and Composites

Modeling the Physical Properties of Gamma Alumina Catalyst Carrier Based on an Artificial Neural Network Porous γ-alumina is widely used as a catalyst carrier due to its chemical properties. These properties are strongly correlated with the physical properties of the material, such as porosity, density, shrinkage, and surface area. This study presents a technique that is less time consuming than other techniques to predict the values of the above-mentioned physical properties of porous γ-alumina via an artificial neural network (ANN) numerical model. The experimental data that was implemented was determined based on 30 samples that varied in terms of sintering temperature, yeast concentration, and socking time. Of the 30 experimental samples, 25 samples were used for training purposes, while the other five samples were used for the execution of the experimental procedure. The results showed that the prediction and experimental data were in good agreement, and it was concluded that the proposed model is proficient at providing high accuracy estimation data derived from any complex analytical equation. Artificial neural network ; Porosity ; Materials science ; Shrinkage ; Biological system ; Experimental data ; Sintering ; Catalysis ; Composite material ; Computer science ; Machine learning ; Mathematics ; Statistics ; Chemistry Advanced ceramic materials synthesis ; Machine Learning in Materials Science ; Anodic Oxide Films and Nanostructures
Authors: Hirofumi Hazama | Susumu Sobue | Shin Tajima | Ryoji Asahi
Source: Inorganic Chemistry (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Phosphorescent Material Search Using a Combination of High-Throughput Evaluation and Machine Learning High-throughput experiments including combinatorial chemistry are useful for generating large amounts of data within a short period of time. Machine learning can be used to predict the regularity of a response variable using a statistical model of a data set. Because a combination of these methods can accelerate the material development, we applied such a combination to a search of semiconducting thin films prepared on an Eu and Dy codoped SrAl<sub>2</sub>O<sub>4</sub>-based phosphorescent material to improve the lifetime of its afterglow. Oxide targets MgO, GeO<sub>2</sub>, Ga<sub>2</sub>O<sub>3</sub>, ZnO, Bi<sub>2</sub>O<sub>3</sub>, Ta<sub>2</sub>O<sub>5</sub>, TiO<sub>2</sub>, and Y<sub>2</sub>O<sub>3</sub> were deposited to form a thin film on a SrAl<sub>2</sub>O<sub>4</sub> substrate as a combinatorial library with a systematical change in these ratios. The sample was calcined under several conditions, and a data set of 800 examples was obtained using a high-throughput evaluation. The 800 examples were then randomly divided into training and test data sets. The lifetime of the afterglow was interpolated through machine learning using the film thickness of each element and the calcined condition of the training data set as explanatory variables. The accuracy of the interpolation was evaluated using a correlation coefficient and the root mean squared error of the predicted values with respect to the experimental values of the test data set. As a result, it was found that a MgO thin film is effective at improving the lifetime of the afterglow and that its optimum condition is a film thickness of approximately 100 nm with calcination at 400-600 °C in air. Chemistry ; Throughput ; Phosphorescence ; Nanotechnology ; Combinatorial chemistry ; Telecommunications Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials
Authors: I. Balasundar | T. Raghu | B.P. Kashyap
Source: Materials Performance and Characterization (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Correlation between Microstructural Features and Tensile Properties in Near-α Titanium Alloy IMI 834 Processed in the α + β Regime Abstract The mechanical properties of titanium alloys are highly sensitive to the morphology, topology, and dimensions of the α and β phases present in the material. An attempt has been made here to systematically evaluate the effect of heat treatment parameters, that is, solution treatment temperature and cooling rate, on the microstructure and room temperature tensile properties of a near-α titanium alloy IMI 834. Seven important microstructural features that affect the mechanical properties of a near-α titanium alloy IMI 834 processed in the α + β regime were quantified and used to establish a direct microstructure-mechanical property correlation with the aid of artificial neural network. The established neural network has been used to evaluate and understand the effect of each individual microstructural feature on the tensile property of the material. The results obtained are presented and discussed here. Materials science ; Microstructure ; Ultimate tensile strength ; Titanium alloy ; Alloy ; Titanium ; Composite material ; Artificial neural network ; Metallurgy ; Machine learning Titanium Alloys Microstructure and Properties ; Welding Techniques and Residual Stresses ; Intermetallics and Advanced Alloy Properties
Authors: Minh‐Vien Le | Tuan-Anh Nguyen | Thi Anh Nga Nguyen
Source: Journal of Chemistry (article, 2019-06-20)
Field: Materials Science — Materials Chemistry

Modeling and Optimization of the BSCF-Based Single-Chamber Solid Oxide Fuel Cell by Artificial Neural Network and Genetic Algorithm Fuel cells could be a highly effective and eco-friendly technology to transform chemical energy stored in fuel to useful electricity and thus are presently appraised as a standout among the most encouraging advancements for future energy demand. Solid oxide fuel cells (SOFCs) have several advantages over other types of fuel cells, such as the flexibility of fuel used, high energy conversion, and relatively inexpensive catalysts due to high-temperature operation. The single chambers, wherein the anode and cathode are exposed to the same mixture of fuel, are promising for the portable power application due to the simplified, compact, sealing-free cell structure. The empirical regression models, such as artificial neural networks (ANNs), can be used as a black-box tool to simulate systems without solving the complicated physical equations merely by utilizing available experimental data. In this study, the performance of the newly proposed BSCF/GDC-based cathode SOFC was modeled using ANNs. The cell voltage was estimated with cathode preparation temperature, cell operating temperature, and cell current as input parameters by the one-layer feed-forward neural network. In order to acquire the appropriate model, several network structures were tested, and the network was trained by backpropagation algorithms. The data used during the training, validation, and test are the actual experimental results from our previous study. The optimum conditions to achieve maximum power of the cell were then determined by the genetic algorithm and the developed ANN. Artificial neural network ; Cathode ; Anode ; Backpropagation ; Chemical energy ; Solid oxide fuel cell ; Algorithm ; Flexibility (engineering) ; Process engineering ; Genetic algorithm ; Power (physics) ; Biological system ; Chemistry ; Computer science ; Engineering ; Artificial intelligence ; Electrical engineering ; Machine learning ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalysis and Oxidation Reactions
Authors: Yong‐Jie Hu | Ge Zhao | Mingfei Zhang | Bin Bin | Tyler Del Rose | Qian Zhao | Qun Zu | Yang Chen | Xuekun Sun | Maarten de Jong | Liang Qi
Source: arXiv (Cornell University) (preprint, 2019-11-06)
Field: Materials Science — Ceramics and Composites

Predicting densities and elastic moduli of SiO2-based glasses by machine learning Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales. Here we show that the densities and elastic moduli of SiO2-based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (&gt;10) types of additive oxides besides SiO2. Our machine learning approach relies on a training set generated by high-throughput molecular dynamic (MD) simulations, a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding, and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine (GBM-LASSO). The predictions of the ML model are comprehensively compared and validated with a large amount of both simulation and experimental data. By just training with a dataset only composed of binary and ternary glass samples, our model shows very promising capabilities to predict the density and elastic moduli for k-nary SiO2-based glasses beyond the training set. As an example of its potential applications, our GBM-LASSO model was used to perform a rapid and low-cost screening of many (~105) compositions of a multicomponent glass system to construct a compositional-property database that allows for a fruitful overview on the glass density and elastic properties. Elastic modulus ; Moduli ; Lasso (programming language) ; Ternary operation ; Binary number ; Interatomic potential ; Set (abstract data type) ; Materials science ; Machine learning ; Artificial intelligence ; Computer science ; Algorithm ; Function (biology) ; Statistical physics ; Mathematics ; Physics ; Chemistry ; Molecular dynamics ; Composite material ; Computational chemistry Glass properties and applications ; Phase-change materials and chalcogenides ; Pigment Synthesis and Properties
Authors: Jessica Torres-Gamez | José A. Rodrı́guez | María Elena Páez-Hernández | Carlos Andrés Galán-Vidal
Source: International Journal of Analytical Chemistry (article, 2019-01-02)
Field: Chemistry — Analytical Chemistry

Application of Multivariate Statistical Analysis to Simultaneous Spectrophotometric Enzymatic Determination of Glucose and Cholesterol in Serum Samples A method using UV-Vis spectroscopy and multivariate tools for simultaneous determination of glucose and cholesterol was developed in this paper. The method is based on the development of the reaction between the analytes (cholesterol and glucose) and enzymatic reagents. The spectra were analyzed by partial least squares regression and artificial neural networks. The precision estimated between nominal and calculate concentration demonstrate that artificial neural network model was adequate to quantify both analytes in serum samples, since the % relative error obtained was in the interval from 5.1 to 8.3. The proposed model was applied to analyze blood serum samples, and the results are similar compared to those obtained employing the reference method. Multivariate statistics ; Analyte ; Chromatography ; Partial least squares regression ; Reagent ; Artificial neural network ; Chemistry ; Analytical Chemistry (journal) ; Mathematics ; Computer science ; Statistics ; Artificial intelligence ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Chromatography
Authors: Nouamane Laanait | Qian He | Albina Y. Borisevich
Source: arXiv (Cornell University) (preprint, 2019-02-19)
Field: Materials Science — Surfaces, Coatings and Films

Reconstruction of 3-D Atomic Distortions from Electron Microscopy with Deep Learning Deep learning has demonstrated superb efficacy in processing imaging data, yet its suitability in solving challenging inverse problems in scientific imaging has not been fully explored. Of immense interest is the determination of local material properties from atomically-resolved imaging, such as electron microscopy, where such information is encoded in subtle and complex data signatures, and whose recovery and interpretation necessitate intensive numerical simulations subject to the requirement of near-perfect knowledge of the experimental setup. We demonstrate that an end-to-end deep learning model can successfully recover 3-dimensional atomic distortions of a variety of oxide perovskite materials from a single 2-dimensional experimental scanning transmission electron (STEM) micrograph, in the process resolving a longstanding question in the recovery of 3-D atomic distortions from STEM experiments. Our results indicate that deep learning is a promising approach to efficiently address unsolved inverse problems in scientific imaging and to underpin novel material investigations at atomic resolution. Electron microscope ; Microscopy ; Nanotechnology ; Materials science ; Engineering physics ; Geology ; Physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: Ziling Wu | Ting Yang | Zhifei Deng | Baokun Huang | Han Liu | Yu Wang | Yuan Chen | Mary Caswell Stoddard | Ling Li | Yunhui Zhu
Source: Integrating materials and manufacturing innovation (article, 2019-11-25)
Field: Materials Science — Biomaterials

Automatic Crack Detection and Analysis for Biological Cellular Materials in X-Ray In Situ Tomography Measurements Abstract We introduce a novel methodology, based on in situ X-ray tomography measurements, to quantify and analyze 3D crack morphologies in biological cellular materials during damage process. Damage characterization in cellular materials is challenging due to the difficulty of identifying and registering cracks from the complicated 3D network structure. In this paper, we develop a pipeline of computer vision algorithms to extract crack patterns from a large volumetric dataset of in situ X-ray tomography measurement obtained during a compression test. Based on a hybrid approach using both model-based feature filtering and data-driven machine learning, the proposed method shows high efficiency and accuracy in identifying the crack pattern from the complex cellular structures and tomography reconstruction artifacts. The identified cracks are registered as 3D tilted planes, where 3D morphology descriptors including crack location, crack opening width, and crack plane orientation are registered to provide quantitative data for future mechanical analysis. This method is applied to two different biological materials with different levels of porosity, i.e., sea urchin ( Heterocentrotus mamillatus ) spines and emu ( Dromaius novaehollandiae ) eggshells. The results are verified by experienced human image readers. The methodology presented in this paper can be utilized for crack analysis in many other cellular solids, including both synthetic and natural materials. In situ ; Tomography ; Characterization (materials science) ; Computer science ; Plane (geometry) ; Feature (linguistics) ; Process (computing) ; Materials science ; Artificial intelligence ; Orientation (vector space) ; Porosity ; Pattern recognition (psychology) ; Biological system ; Biomedical engineering ; Composite material ; Geometry ; Engineering ; Optics ; Nanotechnology ; Mathematics Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Drilling and Well Engineering
Authors: Guillaume Fraux | Siwar Chibani | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2019-02-18)
Field: Chemistry — Inorganic Chemistry

Modelling of framework materials at multiple scales: current practices and open questions The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction. Field (mathematics) ; Data science ; Computer science ; Current (fluid) ; Point (geometry) ; Open research ; Management science ; Systems engineering ; Engineering ; World Wide Web ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Farhad Ghazvinian Zanjani | Andreas Panteli | Svitlana Zinger | Fons van der Sommen | Tao Tan | Benjamin Balluff | D. R. N. Vos | Shane R. Ellis | Ron M. A. Heeren | Marit Lucas | Henk A. Marquering | Ilaria Jansen | C. Dilara Savci‐Heijink | Daniël M. de Bruin | Peter H. N. de With
Source:  (article, 2019-04-01)
Field: Chemistry — Spectroscopy

Cancer Detection in Mass Spectrometry Imaging Data by Recurrent Neural Networks Mass spectrometry imaging (MSI) reveals the localization of a broad scale of compounds ranging from metabolites to proteins in biological tissues. This makes MSI an attractive tool in biomedical research for studying diseases. Computer-aided diagnosis (CAD) systems facilitate the analysis of the molecular profile in tumor tissues to provide a distinctive fingerprint for finding biomarkers. In this paper, the performance of recurrent neural networks (RNNs) is studied on MSI data to exploit their learning capabilities for finding irregular patterns and dependencies in sequential data. In order to design a better CAD model for tumor detection/classification, several configurations of Long Short-Time Memory (LSTM) are examined. The proposed model consists of a 2-layer bidirectional LSTM, each containing 100 LSTM units. The proposed RNN model outperforms the state-of-the-art CNN model by 1.87% and 1.45% higher accuracy in mass spectra classification on lung and bladder cancer datasets with a sixfold faster training time. Mass spectrometry ; Computer science ; Mass spectrometry imaging ; Artificial intelligence ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies