jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Krzysztof Kuchta | Joanna Towpik | Anna Biernacka | Jan Kutner | Andrzej Kudlicki | Krzysztof Ginalski | Maga Rowicka
Source: Scientific Reports (article, 2018-09-10)
Field: Chemistry — Spectroscopy

Predicting proteome dynamics using gene expression data While protein concentrations are physiologically most relevant, measuring them globally is challenging. mRNA levels are easier to measure genome-wide and hence are typically used to infer the corresponding protein abundances. The steady-state condition (assumption that protein levels remain constant) has typically been used to calculate protein concentrations, as it is mathematically convenient, even though it is often not satisfied. Here, we propose a method to estimate genome-wide protein abundances without this assumption. Instead, we assume that the system returns to its baseline at the end of the experiment, which is true for cyclic phenomena (e.g. cell cycle) and many time-course experiments. Our approach only requires availability of gene expression and protein half-life data. As proof-of-concept, we predicted proteome dynamics associated with the budding yeast cell cycle, the results are available for browsing online at http://dynprot.cent.uw.edu.pl/ . The approach was validated experimentally by verifying that the predicted protein concentration changes were consistent with measurements for all proteins tested. Additionally, if proteomic data are available as well, we can also infer changes in protein half-lives in response to posttranslational regulation, as we did for Clb2, a post-translationally regulated protein. The predicted changes in Clb2 abundance are consistent with earlier observations. Proteome ; Computational biology ; Genome ; Proteomics ; Gene expression ; Biology ; Systems biology ; Gene ; Protein expression ; Cell cycle ; Messenger RNA ; Computer science ; Bioinformatics ; Genetics Advanced Proteomics Techniques and Applications ; Microbial Metabolic Engineering and Bioproduction ; Gene Regulatory Network Analysis
Authors: Dhruv Raj Karana | Rashmi Rekha Sahoo
Source: Heat Transfer-Asian Research (article, 2018-10-17)
Field: Materials Science — Materials Chemistry

Performance effect on the TEG system for waste heat recovery in automobiles using ZnO and SiO<sub>2</sub> nanofluid coolants Abstract Enhancement in heat transfer of the cold side is vital to amplify the performance of a thermoelectric generator (TEG). With enriched thermophysical properties of nanofluids, significant improvement in heat transfer process can be obtained. The current study concerns the performance comparison of an automobile waste heat recovery system with EG‐water (EG‐W) mixture, ZnO, and SiO 2 nanofluid as coolants for the TEG system. The effects on performance parameters, that is, circuit voltage, conversion efficiency, and output power with exhaust inlet temperature, the total area of TEG, Reynolds number, and particle concentration of nanofluids for the TEG system have been investigated. A detailed performance analysis revealed an increase in voltage, power output, and conversion efficiency of the TEG system with SiO 2 nanofluid, followed by ZnO and EG‐W coolants. The electric power and conversion efficiency for SiO 2 nanofluid at an exhaust inlet temperature of 500K were enhanced by 11.80% and 11.39% respectively, in comparison with EG‐W coolants. Moreover, the model speculates that an optimal total area of TEGs exists for the maximum power output of the system. With SiO 2 nanofluid as a coolant, the total area of TEGs can be diminished by up to 34% as compared with EG‐W, which brings significant convenience for the placement of TEGs and reduces the cost of the TEG system. Nanofluid ; Coolant ; Materials science ; Heat transfer ; Thermoelectric generator ; Waste heat recovery unit ; Nuclear engineering ; Voltage ; Thermodynamics ; Thermoelectric effect ; Heat exchanger ; Nanoparticle ; Electrical engineering ; Nanotechnology Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Phase Change Materials Research
Authors: Zhuo Ma | Yang Yang | Martin Kearns | Kevin Cowan | Huajie Yi | D.M. Hepburn | Chengke Zhou
Source: High Voltage (article, 2018-01-25)
Field: Materials Science — Materials Chemistry

Fractal‐based autonomous partial discharge pattern recognition method for MV motors On‐line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium‐voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase‐reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory‐based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase‐resolved PD pattern into a circular form addresses the lack of phase information in on‐line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi‐source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on‐site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single‐source PD but also multi‐source PDs. Waveform ; Partial discharge ; Fractal ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Voltage ; Line (geometry) ; Power (physics) ; Electronic engineering ; Engineering ; Electrical engineering ; Mathematics ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Peng Wang | Yecheng Shao | Hongtao Wang | Wei Yang
Source: Extreme Mechanics Letters (article, 2018-08-22)
Field: Materials Science — Materials Chemistry

Accurate interatomic force field for molecular dynamics simulation by hybridizing classical and machine learning potentials Force field (fiction) ; Molecular dynamics ; Handshaking ; Embedded atom model ; Statistical physics ; Interatomic potential ; Computation ; Ab initio ; Field (mathematics) ; Computer science ; Scalability ; Atom (system on chip) ; Range (aeronautics) ; Physics ; Computational science ; Algorithm ; Materials science ; Artificial intelligence ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Microstructure and mechanical properties ; X-ray Diffraction in Crystallography
Authors: Parastoo Vahdati Yekta | Farzad Jaafari Honar | Mohammad Naghiyan Fesharaki
Source: Computational Materials Science (article, 2018-12-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modelling of hysteresis loop and magnetic behaviour of Fe-48Ni alloys using artificial neural network coupled with genetic algorithm Artificial neural network ; Mean squared error ; Permalloy ; Simulated annealing ; Magnetic hysteresis ; Hysteresis ; Annealing (glass) ; Genetic algorithm ; Nonlinear system ; Materials science ; Algorithm ; Magnetic field ; Biological system ; Magnetization ; Computer science ; Mathematics ; Condensed matter physics ; Physics ; Artificial intelligence ; Machine learning ; Statistics ; Metallurgy Magnetic Properties and Applications ; Neural Networks and Applications ; Grey System Theory Applications
Authors: Mardochee Reveil | Paulette Clancy
Source: Molecular Systems Design & Engineering (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Classification of spatially resolved molecular fingerprints for machine learning applications and development of a codebase for their implementation Direct mapping between material structures and properties for various classes of materials is often the ultimate goal of materials researchers. Codebase ; Computer science ; Artificial intelligence ; Machine learning ; Human–computer interaction ; Software ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electronic and Structural Properties of Oxides
Authors: Shweta Jindal | Satya S. Bulusu
Source: The Journal of Chemical Physics (article, 2018-11-16)
Field: Materials Science — Materials Chemistry

A transferable artificial neural network model for atomic forces in nanoparticles We have designed a new method to fit the energy and atomic forces using a single artificial neural network (SANN) for any number of chemical species present in a molecular system. The traditional approach for fitting the potential energy surface for a multicomponent system using artificial neural network (ANN) is to consider <i>n</i> number of networks for <i>n</i> number of chemical species in the system. This shoots the computational cost and makes it difficult to apply to a system containing more number of species. We present a new strategy of using a SANN to compute energy and forces of a chemical system. Since atomic forces are significant for geometry optimizations and molecular dynamics simulations for any chemical system, their accurate prediction is of utmost importance. So, to predict the atomic forces, we have modified the traditional way of fitting forces from underlying energy expression. We have applied our strategy to study geometry optimizations and dynamics in gold-silver nanoalloys and thiol protected gold nanoclusters. Also, force fitting has made it possible to train smaller sized systems and extrapolate the parameters to make accurate predictions for larger systems. This proposed strategy has definitely made the mapping and fitting of atomic forces easier and can be applied to a wide variety of molecular systems. Artificial neural network ; Molecular dynamics ; Energy (signal processing) ; Potential energy ; Chemical energy ; Energy minimization ; Atomic force microscopy Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Chemical and Physical Properties of Materials
Authors: Tao Yan | Baichuan Sun | Amanda S. Barnard
Source: Nanoscale (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Predicting archetypal nanoparticle shapes using a combination of thermodynamic theory and machine learning Machine learning is a useful way of identifying representative or pure nanoparticle shapes as part of a larger ensemble, but its predictive capabilities can be limited when a large dataset of candidate structures must already exist. Ideally one would like to use machine learning to define the ideal dataset for future, more computationally intensive, studies before a significant amount of resources are consumed. In this work we combine an established analytical phenomenological model and statistical machine learning to predict the archetypes and prototypes of a diverse ensemble of 2380 platinum nanoparticle morphologies developed with less than twenty input electronic structure simulations. By parameterising a size- and shape-dependent thermodynamic model, probabilities are assigned to seventeen different shapes between three and thirty nanometres, which together with structural features such as nanoparticle diameter, surface area, sphericity and facet configuration form the basis for archetypal analysis and K-means clustering. Using this approach we rapidly identify six "pure" archetypes and twelve "representative" prototypes that can be used in future computational studies of properties such as catalysis. Sphericity ; Computer science ; Nanoparticle ; Facet (psychology) ; Cluster analysis ; Artificial intelligence ; Archetype ; Machine learning ; Curse of dimensionality ; Platinum nanoparticles ; Materials science ; Nanotechnology ; Mathematics ; Platinum ; Chemistry ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Hyunjun Ji | Yousung Jung
Source: The Journal of Chemical Physics (article, 2018-06-22)
Field: Materials Science — Materials Chemistry

A local environment descriptor for machine-learned density functional theory at the generalized gradient approximation level We propose a grid-based local representation of electronic quantities that can be used in machine learning applications for molecules, which is compact, fixed in size, and able to distinguish different chemical environments. We apply the proposed approach to represent the external potential in density functional theory with modified pseudopotentials and demonstrate its proof of concept by predicting the Perdew-Burke-Ernzerhof and local density approximation electronic density and exchange-correlation potentials by kernel ridge regression. For 16 small molecules consisting of C, H, N, and O, the mean absolute error of exchange-correlation energy was 0.78 kcal/mol when trained for individual molecules. Furthermore, the model is shown to predict the exchange-correlation energy with an accuracy of 3.68 kcal/mol when the model is trained with a small fraction (4%) of all 16 molecules of the present dataset, suggesting a promising possibility that the current machine-learned model may predict the exchange-correlation energies of an arbitrary molecule with reasonable accuracy when trained with a sufficient amount of data covering an extensive variety of chemical environments. Density functional theory ; Kernel (algebra) ; Molecule ; Representation (politics) ; Correlation ; Local-density approximation ; Statistical physics ; Energy (signal processing) ; Ridge ; Mathematics ; Chemistry ; Computer science ; Machine learning ; Physics ; Computational chemistry ; Quantum mechanics ; Statistics ; Combinatorics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Darija Obradović | Filip Andrić | Mario Zlatović | Danica Agbaba
Source: Journal of Chemometrics (article, 2018-01-12)
Field: Chemistry — Spectroscopy

Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites Abstract Aripiprazole and ziprasidone are atypical antipsychotic drugs with the effect on positive and negative symptoms of schizophrenia, mania, and mixed states of bipolar disorder. Hansen's solubility parameters, δ d , δ p , and δ h , which account for dispersive, polarizable, and hydrogen bonding contributions to the overall cohesive energy of a compound, are often used to assess pharmacokinetic properties of drugs. However, no data exist of solubility parameters for the drugs of interest in this study. Therefore, in the present study, partial least square regression (PLS), artificial neural networks (ANNs), regression trees (RT), boosted trees (BT), and random forests (RF) were applied to estimate Hansen's solubility parameters of ziprasidone, aripiprazole, and their impurities/metabolic derivatives, targeting their biopharmaceutical classes and absorption routes. A training set of 47 structurally diverse and pharmacologically active compounds and 290 molecular descriptors and pharmaceutically important properties were used to build the prediction models. The modeling approaches were compared by the sum of ranking differences, using the consensus values as a reference for the unknowns and the experimentally determined values as a gold standard for the calibration set. In both instances, the PLS models, together with ANNs, demonstrated better performance than RT, BT and especially RF. Based on the best scored models, we were able to pinpoint the most probable absorption sites for each drug and the corresponding metabolite, i.e., the upper parts of the gastrointestinal tract, small intestine, or absorption along entire length of gastrointestinal tract. Chemistry ; Ziprasidone ; Aripiprazole ; Molecular descriptor ; Mathematics ; Quantitative structure–activity relationship ; Antipsychotic ; Computer science ; Stereochemistry ; Schizophrenia (object-oriented programming) Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Analytical Methods in Pharmaceuticals
Authors: Alireza Samiee | Gilberto Casillas | Mansur Ahmed | Dmytro G. Savvakin | Ryan Naseri | Elena V. Pereloma
Source: Metals (article, 2018-01-30)
Field: Materials Science — Materials Chemistry

Formation of Deformation-Induced Products in a Metastable-β Titanium Alloy during High Temperature Compression A metastable-β titanium alloy, Ti-10V-3Fe-3Al (wt. %), was subjected to thermo-mechanical processing including the compression test at 725 °C, which is below the β transus temperature (780 °C), and at strain rate of 10−3 s−1. The presence of phases was determined using transmission electron microscopy and X-ray diffraction. Although the dynamic recovery took place together with slip, both deformation-induced α” martensite and ω were detected as other operating mechanisms for the first time in metastable-β titanium alloy deformed in α + β region. The volume fraction of stress-induced α” was higher than that of the same alloy deformed at room temperature due to a higher strain applied. Stress-induced twinning was not operational, which could be related to the priority of slip mechanism at high temperature resulted from thermally-assisted nucleation and lateral migration of kink-pairs. Materials science ; Alloy ; Metastability ; Crystal twinning ; Nucleation ; Slip (aerodynamics) ; Titanium alloy ; Deformation (meteorology) ; Strain rate ; Volume fraction ; Martensite ; Titanium ; Metallurgy ; Deformation mechanism ; Composite material ; Transmission electron microscopy ; Compression (physics) ; Microstructure ; Thermodynamics ; Chemistry Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Intermetallics and Advanced Alloy Properties
Authors: Hongyuan Yan | Jixiong Zhang | Jingxian Gao | Huang Yangming | Yanmei Xiong | Shungeng Min
Source: Scientific Reports (article, 2018-09-27)
Field: Chemistry — Analytical Chemistry

Towards improvement in prediction of iodine value in edible oil system based on chemometric analysis of portable vibrational spectroscopic data Iodine value (IV) is a significant parameter to illustrate the quality of edible oil. In this study, three portable spectroscopy devices were employed to determine IV in mixed edible oil system, a new Micro-Electro-Mechanical-System (MEMS) Fourier Transform Infrared Spectrometer (MEMS-FTIR), a MicroNIR<sup>TM</sup>1700 and an i-Raman Plus-785S. Quantitative model was built by Partial least squares (PLS) regression model and four variable selection methods were applied before PLS model, which are Monte Carlo uninformative variables elimination (MCUVE), competitive reweighted sampling (CARS), bootstrapping soft shrinkage approach (BOSS) and variable combination population analysis (VCPA). The coefficient of determination (R<sup>2</sup>), and the root mean square error prediction (RMSEP) were used as indicators for the predictability of the PLS models. In MicroNIR<sup>TM</sup>1700 dataset, MCUVE gave the lowest RMSEP (2.3440), in MEMS-FTIR dataset, CARS showed the best performance with RMSEP (2.2185), in i-Raman Plus-785S dataset, BOSS gave the lowest RMSEP (2.5058). They all had great improvements than full spectrum PLS model. Four variable selection methods take a smaller number of variables and perform significant superiority in prediction accuracy. It was demonstrated that three new portable instruments would be suitable for the on-site determination of edible oil quality in infrared and Raman field. Iodine value ; Iodine ; Value (mathematics) ; Computer science ; Data mining ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Machine learning ; Food science ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Appadurai Anitha Angeline
Source: Journal of Thermal Engineering (article, 2018-04-10)
Field: Materials Science — Materials Chemistry

POWER GENERATION FROM COMBUSTED “SYNGAS” USING HYBRID THERMOELECTRIC GENERATOR AND FORECASTING THE PERFORMANCE WITH ANN TECHNIQUE Gasification and combustion of de-oiled Pongamia Pinnata seed cake is done to produce higher energy biomass waste heat “syngas” for generating power using hybrid thermoelectric generator (TEG). A test rig is fabricated and experiments conducted with synthetic oil (Therminol-55) as the heating fluid under water and air-cooled methods. The hot side temperature is varied from 200 - 250ºC while the coolant temperature is maintained at 30 oC for both water and air respectively. Experimental results showed 22.27% enhancement in electric power at a constant hot side temperature of 250 ºC under water cooled method. In addition, simulation results for the above mentioned conditions using artificial neural networks (ANN) tool in MATLAB also agreed well with the sample experimental results. The performance parameters such as open circuit voltage, maximum output power and matched load resistance are forecasted using ANN upto maximum possible hot side temperature of 350ºC. Further, the financial evaluation of Biomass gasified-thermoelectric system ($0.0018/kWh and in terms of Indian currency is ₨ 0.0676/kWh) is found to be almost negligible on comparison with other available renewable energy technologies. &amp;nbsp; Renewable energy ; Environmental science ; Thermoelectric generator ; Electricity generation ; Materials science ; Nuclear engineering ; Syngas ; Combustion ; Coolant ; Automotive engineering ; Electric power ; Power (physics) ; Process engineering ; Thermoelectric effect ; Mechanical engineering ; Electrical engineering ; Engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Energy and Environment Impacts ; Solar Thermal and Photovoltaic Systems
Authors: Zahra Basati | Mansour Rasekh | Yousef Abbaspour‐Gilandeh
Source: International Agrophysics (article, 2018-04-01)
Field: Chemistry — Analytical Chemistry

Using different classification models in wheat grading utilizing visual features Abstract Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively Classifier (UML) ; Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Decision tree ; Preprocessor ; Segmentation ; Gluten ; Feature selection ; Mathematics ; Computer science ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies