jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jördis‐Ann Schüler | Steffen Neumann | Matthias Müller‐Hannemann | Wolfgang Brandt
Source: Journal of Mass Spectrometry (article, 2018-08-13)
Field: Chemistry — Spectroscopy

ChemFrag: Chemically meaningful annotation of fragment ion mass spectra Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule-based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra. Chemistry ; Annotation ; Fragmentation (computing) ; Fragment (logic) ; Mass spectrum ; Mass spectrometry ; Ion ; Molecule ; Biological system ; Computational chemistry ; Pattern recognition (psychology) ; Artificial intelligence ; Algorithm ; Chromatography ; Computer science ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Joohwi Lee | Nobuko Ohba | Ryoji Asahi
Source: RSC Advances (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Discovery of zirconium dioxides for the design of better oxygen-ion conductors using efficient algorithms beyond data mining It is important to find crystal structures with low formation (<i>E</i> <sub>v</sub>) and migration-barrier (<i>E</i> <sub>m</sub>) energies for oxygen vacancies for the development of fast oxygen-ion conductors. To identify crystal structures with lower <i>E</i> <sub>v</sub> and <i>E</i> <sub>m</sub> than those of ground-state ZrO<sub>2</sub>, we first reoptimize the crystal structures of various oxides reported in the database, and then directly construct them using an evolutionary algorithm. For efficient searching, we employ the linearized ridge regression model for <i>E</i> <sub>v</sub> using descriptors obtained from density functional theory calculations of the unit cells and apply the predicted <i>E</i> <sub>v</sub> as a fitness value in the evolutionary algorithm. We also find a correlation between the <i>E</i> <sub>v</sub> and <i>E</i> <sub>m</sub> for the crystal structures of ZrO<sub>2</sub>. On the basis of this correlation, we confirm that the newly constructed crystal structures, as well as certain reoptimized structures from the database, that possess low <i>E</i> <sub>v</sub> also have <i>E</i> <sub>m</sub> lower than that of ground-state ZrO<sub>2</sub>. Our successful strategy consisting of a combination of the evolutionary algorithm, first-principles calculations, and machine-learning techniques may be applicable to other oxide systems in finding crystal structures with low <i>E</i> <sub>v</sub> and <i>E</i> <sub>m</sub>. Crystal structure ; Crystal (programming language) ; Evolutionary algorithm ; Ion ; Algorithm ; Zirconium ; Density functional theory ; Computer science ; Materials science ; Chemical physics ; Machine learning ; Chemistry ; Physics ; Crystallography ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Advanced Photocatalysis Techniques
Authors: Hadi Samadian | Seyed Salman Zakariaee | Reza Faridi‐Majidi
Source: Journal of the Textile Institute (article, 2018-12-28)
Field: Materials Science — Biomaterials

Evaluation of effective needleless electrospinning parameters controlling polyacrylonitrile nanofibers diameter via modeling artificial neural networks In this study, we aimed to predict the effects of different needleless electrospinning parameters on the diameter of polyacrylonitrile (PAN) nanofibers via artificial neural network method. The various factors, including polymer solution concentration, applied voltage, and spinneret-to-collector distance, were designed to investigate the diameter of PAN nanofibers produced via needleless electrospinning system. Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient back-propagation algorithms were used for the analysis. The results indicated that PAN nanofiber diameters had a direct correlation with the polymer solution concentration and applied voltage and an inverse relation with the nozzle-to-collector distance. The Pearson correlation coefficients were significant at <.01% level for all prediction models. The Bayesian regularization mode I achieved the highest regression value (.9944) for the test data set. The regression value was also calculated and the maximum regression value (.9936) was obtained for the Bayesian regularization model I. Polyacrylonitrile ; Electrospinning ; Nanofiber ; Materials science ; Artificial neural network ; Biological system ; Regularization (linguistics) ; Nozzle ; Algorithm ; Composite material ; Polymer ; Computer science ; Artificial intelligence ; Mechanical engineering ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Scientific and Engineering Research Topics
Authors: Adel Belayadi | Ahmed Mougari | Mokhtar Zabat
Source: Journal of Solid State Electrochemistry (article, 2018-10-12)
Field: Chemistry — Electrochemistry

Modeling of electrochemical properties of potential-induced defects in butane-thiol SAMs by using artificial neural network and impedance spectroscopy data Dielectric spectroscopy ; Equivalent circuit ; Materials science ; Electrical impedance ; Artificial neural network ; Biological system ; Electrochemistry ; Monolayer ; Work (physics) ; Network model ; Voltage ; Computer science ; Thermodynamics ; Nanotechnology ; Chemistry ; Electrode ; Physical chemistry ; Electrical engineering ; Engineering ; Physics ; Artificial intelligence Electrochemical Analysis and Applications ; Electrodeposition and Electroless Coatings ; Conducting polymers and applications
Authors: Samuel S. Schoenholz
Source: Journal of Physics Conference Series (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Combining Machine Learning and Physics to Understand Glassy Systems Our understanding of supercooled liquids and glasses has lagged significantly behind that of simple liquids and crystalline solids. This is in part due to the many possibly relevant degrees of freedom that are present due to the disorder inherent to these systems and in part to non-equilibrium effects which are difficult to treat in the standard context of statistical physics. Together these issues have resulted in a field whose theories are under-constrained by experiment and where fundamental questions are still unresolved. Mean field results have been successful in infinite dimensions but it is unclear to what extent they apply to realistic systems and assume uniform local structure. At odds with this are theories premised on the existence of structural defects. However, until recently it has been impossible to find structural signatures that are predictive of dynamics. Here we summarize and recast the results from several recent papers offering a data driven approach to building a phenomenological theory of disordered materials by combining machine learning with physical intuition. Intuition ; Odds ; Degrees of freedom (physics and chemistry) ; Statistical physics ; Theoretical physics ; Field (mathematics) ; Context (archaeology) ; Simple (philosophy) ; Epistemology ; Complex system ; Computer science ; Physics ; Artificial intelligence ; Machine learning ; Mathematics ; Logistic regression ; Quantum mechanics ; Philosophy Material Dynamics and Properties ; Theoretical and Computational Physics ; Complex Systems and Time Series Analysis
Authors: Dhagash Mehta | Xiaojun Zhao | Edgar A. Bernal | David J. Wales
Source: Physical review. E (article, 2018-05-21)
Field: Materials Science — Materials Chemistry

Loss surface of XOR artificial neural networks Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these landscapes using optimization tools developed for potential energy landscapes in molecular science. The number of local minima and transition states (saddle points of index one), as well as the ratio of transition states to minima, grow rapidly with the number of nodes in the network. There is also a strong dependence on the regularization parameter, with the landscape becoming more convex (fewer minima) as the regularization term increases. We demonstrate that in our formulation, stationary points for networks with N_{h} hidden nodes, including the minimal network required to fit the XOR data, are also stationary points for networks with N_{h}+1 hidden nodes when all the weights involving the additional node are zero. Hence, smaller networks trained on XOR data are embedded in the landscapes of larger networks. Our results clarify certain aspects of the classification and sensitivity (to perturbations in the input data) of minima and saddle points for this system, and may provide insight into dropout and network compression. Maxima and minima ; Artificial neural network ; Saddle point ; Energy landscape ; Computer science ; Stationary point ; Process (computing) ; Function (biology) ; Mathematics ; Mathematical optimization ; Artificial intelligence ; Physics ; Biology ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Mady Elbahri | Ahmed B. Soliman | Kirsi Yliniemi | Ramzy Abdelaziz | Shahin Homaeigohar | Eman S. Zarie
Source: Journal of Chemical Education (article, 2018-07-18)
Field: Materials Science — Materials Chemistry

Innovative Education and Active Teaching with the Leidenfrost Nanochemistry The best learning outcomes are rarely achieved without motivating the learner, and in this regard, active teaching and learning methods have been proven useful. Here, we introduce the “learn and innovate” strategy to enhance the students’ interest, conceptual understanding, and deep learning under full guiding instructions. Moreover, by this strategy, we encourage the students to conduct an independent research while acquiring high-level thinking skills. Designing appropriate instructional strategies and innovative research tasks and media, which leads to collaborative/cooperative learning, enhances the students’ interaction with the course materials. The method supports the students to gain self-confidence, motivation, and scientific skills to address different research challenges at early stages while enhancing their learning level. In this context, the adopted strategy fosters a first semester master student to deeply understand the new “Leidenfrost Nanochemistry” phenomenon to synthesize Au nanoparticles. Acquiring such knowledge, the student can participate in solving an exploratory research problem and the related experimental challenges. The “learn and innovate” approach has been proven to be a pivotal and novel active teaching strategy to stimulate the active learning of students and the innovative education-based Aha! effect. Nanochemistry ; Context (archaeology) ; Active learning (machine learning) ; Mathematics education ; Exploratory research ; Conceptual change ; Pedagogy ; Computer science ; Nanotechnology ; Psychology ; Materials science ; Sociology Machine Learning in Materials Science ; Nanotechnology research and applications
Authors: Nevena Maljurić | Jelena Golubović | Biljana Otašević | Mira Zečević | Ana Protić
Source: Analytical and Bioanalytical Chemistry (article, 2018-02-13)
Field: Chemistry — Spectroscopy

Quantitative structure –retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases Cyclodextrin ; Chromatography ; Chemistry ; Impurity ; High-performance liquid chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Crystallization and Solubility Studies
Authors: Tâmara P. Teixeira | César Marques Salgado | Roos Sophia de Freitas Dam | William Luna Salgado
Source: Applied Radiation and Isotopes (article, 2018-08-10)
Field: Chemistry — Analytical Chemistry

Inorganic scale thickness prediction in oil pipelines by gamma-ray attenuation and artificial neural network Artificial neural network ; Collimated light ; Scale (ratio) ; Pipeline transport ; Deposition (geology) ; Attenuation ; Detector ; Process engineering ; Computer science ; Environmental science ; Materials science ; Biological system ; Optics ; Engineering ; Geology ; Artificial intelligence ; Physics ; Environmental engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Nuclear Physics and Applications
Authors: Yohandys A. Zulueta | Minh Tho Nguyen
Source: Journal of Physics and Chemistry of Solids (article, 2018-05-19)
Field: Materials Science — Materials Chemistry

Multisite occupation of divalent dopants in barium and strontium titanates Dopant ; Divalent ; Perovskite (structure) ; Barium ; Strontium ; Barium titanate ; Tetragonal crystal system ; Materials science ; Inorganic chemistry ; Doping ; Ionic radius ; Chemistry ; Mineralogy ; Crystallography ; Ion ; Crystal structure ; Ceramic ; Metallurgy Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Yun‐Fang Yang | Yuanbin She
Source: International Journal of Quantum Chemistry (article, 2018-09-12)
Field: Chemistry — Organic Chemistry

Computational exploration of Pd‐catalyzed C–H bond activation reactions Abstract Quantum chemistry is widely used to the mechanistic studies of organic and organometallic reactions. In this Review, we described the various mechanisms of palladium‐catalyzed C–H bond activation reactions, including oxidative addition, σ‐bond metathesis, 1,2‐addition, electrophilic aromatic substitution, concerted metalation‐deprotonation (CMD), and Heck‐type mechanism. The different CMD models with acetate as the base, with amidate as the base, and with the bimetallic complex are also highlighted. Metalation ; Chemistry ; Metathesis ; Bimetallic strip ; Deprotonation ; Palladium ; Electrophile ; Electrophilic aromatic substitution ; Catalysis ; Electrophilic substitution ; Oxidative addition ; Electrophilic addition ; Photochemistry ; Computational chemistry ; Reaction mechanism ; Combinatorial chemistry ; Medicinal chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Synthetic Organic Chemistry Methods
Authors: Rei Sonobe | Yuta Miura | Tomohito Sano | Hideki Horie
Source: Canadian Journal of Remote Sensing (article, 2018-03-04)
Field: Chemistry — Analytical Chemistry

Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance The highest-quality green tea is cultivated using shading treatments in Japan; however, shading can lead to early mortalities of tea due to excessive environmental stress. The allocation of photosynthetic pigments – chlorophyll a and b, and carotenoids – could be a good indicator for evaluating production or environmental stress in plants; thus, developing an in situ method to monitor photosynthetic pigments is useful for agricultural management. To assess the accuracy of the estimation of photosynthetic pigment contents with existing supervised learning models, 4 different approaches were compared including random forests, kernel-based extreme learning machine (KELM), deep belief nets and support vector machine. Overall, KELM had the highest performance, with a root mean square error of 1.95 ± 0.36 µg cm−2, 1.08 ± 0.11 µg cm−2 and 0.68 ± 0.10 µg cm−2 for estimating chlorophyll a, b and carotenoid contents, respectively. Shading ; Carotenoid ; Photosynthesis ; Hyperspectral imaging ; Chlorophyll a ; Chlorophyll ; Pigment ; Photosynthetic pigment ; Environmental science ; Chlorophyll b ; Horticulture ; Botany ; Mathematics ; Biology ; Geography ; Remote sensing ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Abu Naser Md Ahsanul Haque | Shamima Akter Smriti | Manwar Hussain | Nawshin Farzana | Fahmida Siddiqa | Md. Azharul Islam
Source: Fashion and Textiles (article, 2018-01-08)
Field: Materials Science — Polymers and Plastics

Prediction of whiteness index of cotton using bleaching process variables by fuzzy inference system Abstract A fuzzy prediction model has been built based on hydrogen peroxide concentration, temperature and time of bleaching as the input variables and knitted fabric whiteness index as the output variable. The process parameters affecting the whiteness index of cotton knitted fabrics are very non-linear. Fuzzy inference system is a prospective modeling tool as it can map effectively in nonlinear domain with minimum investigational data. Triangular-shaped membership functions were considered for the variables and total 48 rules were created in this study. It was found that the sole effect of the concentration of hydrogen peroxide on whiteness is pretty low, but is affected by temperature noticeably even in a fixed concentration of hydrogen peroxide. The model proposed in the present study has been verified by additional experimental data set. The root mean square, mean absolute error percentage and coefficient of determination ( R 2 ) between the predicted and experimental values were found to be 0.536, 0.798 and 0.959 respectively. The results validate that the model can be applied suitably for the prediction of fabric whiteness index in textile industries. Hydrogen peroxide ; Fuzzy logic ; Mean squared error ; Index (typography) ; Mathematics ; Fuzzy inference system ; Variable (mathematics) ; Rust (programming language) ; Nonlinear system ; Coefficient of determination ; Set (abstract data type) ; Inference ; Statistics ; Computer science ; Biological system ; Fuzzy control system ; Chemistry ; Artificial intelligence ; Adaptive neuro fuzzy inference system ; Mathematical analysis ; Physics Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color Science and Applications
Authors: Jinseob Kim | Hyuntai Kim | Gun-Yeal Lee | Juhwan Kim | Byoungho Lee | Yoonchan Jeong
Source: Applied Sciences (article, 2018-03-15)
Field: Materials Science — Surfaces, Coatings and Films

Numerical and Experimental Study on Multi-Focal Metallic Fresnel Zone Plates Designed by the Phase Selection Rule via Virtual Point Sources We propose a novel design method for multi-focal metallic Fresnel zone plates (MFZPs), which exploits the phase selection rule by putting virtual point sources (VPSs) at the desired focal points distant to the MFZP plane. The phase distribution at the MFZP plane reciprocally formed by the VPSs was quantized in a binary manner based on the phase selection rule, thereby leading to a corresponding on-off amplitude pattern for the targeted MFZP. The resultant phase distribution was dependent on the complex amplitudes of the VPSs, so that they could be determined from the perspective of both multi-focal functionality and fabrication feasibility. As a typical example, we utilized the particle swarm optimization algorithm to determine them. Based on the proposed method, we designed and numerically analyzed two types of novel MFZPs—one for a monochromatic multi-focal application and the other for a multi-chromatic mono-focal application—verifying the effectiveness and validity of the proposed method. We also fabricated them onto Au-deposited glass substrates, using electron beam evaporation and a focused ion beam milling process. We experimentally characterized them and also verified that they successfully demonstrated their feasibilities. The former produced distinct hot spots at three different focal distances of 10, 15, and 20 μ m for monochromatic incidence at 650 nm, and the latter produced a single hot spot at a focal distance of 15 μ m for multi-chromatic incidence at 660, 532, and 473 nm. The experimental results were also in good agreement with their corresponding numerical results. We expect that both MFZPs will have various applications, such as laser micromachining, optical trapping, biomedical sensing, confocal collimation, achromatic optics, etc. Optics ; Monochromatic color ; Cardinal point ; Chromatic aberration ; Focal point ; Phase (matter) ; Particle swarm optimization ; Materials science ; Chromatic scale ; Physics ; Computer science ; Algorithm Optical Coatings and Gratings ; Optical Polarization and Ellipsometry ; Orbital Angular Momentum in Optics
Authors: Mauricio A. Porras | Marcelo A. Villar | María A. Cubitto
Source: Journal of Microbiological Methods (article, 2018-03-23)
Field: Materials Science — Biomaterials

Improved intracellular PHA determinations with novel spectrophotometric quantification methodologies based on Sudan black dye Polyhydroxyalkanoates ; Absorbance ; Reagent ; Chromatography ; Chemistry ; Biological system ; Analytical Chemistry (journal) ; Biology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Collagen: Extraction and Characterization
Authors: Ginny Xiaohe Li | Christine Vogel | Hyungwon Choi
Source: Molecular Omics (article, 2018-06-01)
Field: Chemistry — Spectroscopy

PTMscape: an open source tool to predict generic post-translational modifications and map modification crosstalk in protein domains and biological processes While tandem mass spectrometry can detect post-translational modifications (PTM) at the proteome scale, reported PTM sites are often incomplete and include false positives. Computational approaches can complement these datasets by additional predictions, but most available tools use prediction models pre-trained for single PTM type by the developers and it remains a difficult task to perform large-scale batch prediction for multiple PTMs with flexible user control, including the choice of training data. We developed an R package called PTMscape which predicts PTM sites across the proteome based on a unified and comprehensive set of descriptors of the physico-chemical microenvironment of modified sites, with additional downstream analysis modules to test enrichment of individual or pairs of PTMs in protein domains. PTMscape is flexible in the ability to process any major modifications, such as phosphorylation and ubiquitination, while achieving the sensitivity and specificity comparable to single-PTM methods and outperforming other multi-PTM tools. Applying this framework, we expanded proteome-wide coverage of five major PTMs affecting different residues by prediction, especially for lysine and arginine modifications. Using a combination of experimentally acquired sites (PSP) and newly predicted sites, we discovered that the crosstalk among multiple PTMs occur more frequently than by random chance in key protein domains such as histone, protein kinase, and RNA recognition motifs, spanning various biological processes such as RNA processing, DNA damage response, signal transduction, and regulation of cell cycle. These results provide a proteome-scale analysis of crosstalk among major PTMs and can be easily extended to other types of PTM. Crosstalk ; Posttranslational modification ; Computational biology ; Computer science ; Biology ; Engineering ; Biochemistry ; Electronic engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms