jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhou Lin | Alexander W. Kohn | Troy Van Voorhis
Source: ChemRxiv (preprint, 2019-09-03)
Field: Materials Science — Materials Chemistry

Toward Prediction of Nonradiative Decay Pathways in Organic Compounds II: Two Internal Conversion Channels in BODIPYs Boron-dipyrromethene (BODIPY) molecules are widely used as laser dyes and have therefore become a popular research topic within recent decades. Numerous studies have been reported for the rational design of BODIPY derivatives based on their spectroscopic and photophysical properties, including absorption and fluorescence wavelengths (λabs and λfl), oscillator strength (f), nonradiative pathways, and quantum yield (ϕ). In the present work, we illustrate a theoretical, semi-empirical model that accurately predicts ϕ for various BODIPY compounds based on inexpensive electronic structure calculations, following the data-driven algorithm proposed and tested on the naphthalene family by us [Kohn, Lin, and Van Voorhis, J. Phys. Chem. C. 2019, 123, 15394]. The model allows us to identify the dominant nonradiative channel of any BODIPY molecule using its structure exclusively and to establish a correlation between the activation energy (Ea) and the fluorescence quantum yield (ϕfl). Based on our calculations, either the S1 → S0 or La → Lb internal conversion (IC) mechanism dominates in the majority of BODIPY derivatives, depending on the structural and electronic properties of the substituents. In both cases, the nonradiative rate (knr) exhibits a straightforward Arrhenius-like relation with the associated Ea. More interestingly, the S1 → S0 mechanism proceeds via a highly distorted intermediate structure in which the core BODIPY plane and the substituent at the 1-position are forced to bend, while the internal rotation of the very same substituent induces the La → Lb transition. Our model reproduces kfl, knr, and ϕfl to mean absolute errors (MAE) of 0.16 decades, 0.87 decades, and 0.26, when all outliers are considered. These results allow us to validate the predictive power of the proposed data-driven algorithm in ϕfl. They also indicate that the model has a great potential to facilitate and accelerate the machine learning aided design of BODIPY dyes for imaging and sensing applications, given sufficient experimental data and appropriate molecular descriptors. BODIPY ; Substituent ; Quantum yield ; Chemistry ; Internal conversion ; Arrhenius equation ; Molecule ; Photochemistry ; Fluorescence ; Computational chemistry ; Physical chemistry ; Activation energy ; Stereochemistry ; Physics ; Quantum mechanics ; Spectral line ; Organic chemistry Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Porphyrin and Phthalocyanine Chemistry
Authors: Oleksandra Berhilevych | Victoria Kasianchuk | I. V. Chernetskyi | Anastasia Konieva | Lubov Dimitrijevich | Тетяна Іванівна Маренкова
Source: Eastern-European Journal of Enterprise Technologies (article, 2019-03-18)
Field: Chemistry — Analytical Chemistry

Construction of a method for predicting the number of enterobacteria in milk using artifical neural networks It is now established that artificial neural networks (ANNs) provide better simulation and prediction of the number of microorganisms in raw materials and foodstuffs. In this case, ANNs could be used as informative, fast, and cost-effective means. According to the European requirements to food products, basic microbiological indicators are the total number of microorganisms and bacteria from the Enterobacteriaceae family, since they are most commonly associated with food-borne diseases and poisonings. The aim of this work was to devise a method for predicting the number of bacteria from the Enterobacteriaceae family in raw milk at its chilled storage and to estimate the predictive capability of ANN. Construction of the method included 4 stages. At the first stage, we examined the number of enterobacteria depending on the physical-chemical composition of raw milk, temperature and duration of storage in a refrigerator. At the second stage, we compiled a base of experimental data obtained from research models. At the next stage, we introduced the received database to ANN. And at the last stage we assessed effectiveness of the predicting technique. The constructed ANN consists of three layers: an input layer (5 parameters: milk storage temperature (4, 6, 8, and 10 °C), duration of milk storage (from 1 to 48 hours); the acidity of milk (17‒20 %), the fat content (3.2; 3.6; 4.0; 4.5 %) and protein content (2.9; 3.0; 3.3 %) in milk; hidden layers (with 30 neurons) and the output layer (the projected number of bacteria). In order to train and optimize the ANN, we used 1,200 experimental data, which revealed that the prediction had the highest rate of deviation of 2.497 % (or 370 bacterial cells per 1 ml). Thus, the devised predicting method could be used to predict the number of bacteria taking into consideration the complex of environmental variables in different food products. In addition, a given approach could be employed as artificial intelligence when assessing microbiological risks and for quick monitoring of food safety. Artificial neural network ; Raw milk ; Raw material ; Food science ; Duration (music) ; Computer science ; Artificial intelligence ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Advanced Chemical Sensor Technologies
Authors: Jiaqi Wang | Seungha Shin | Sangkeun Lee
Source: Advanced Theory and Simulations (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning Abstract Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. For developing an unbiased potential, a finite‐temperature dynamics machine learning (FTD‐ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD‐ML exhibits three distinguished features: 1) FTD‐ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD‐ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first‐principles data; 3) FTD‐ML is much more computationally cost effective than first‐principles simulations, especially when the system size increases over 10 3 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD‐ML approach exhibits good performance for general simulation purposes. Thus, the FTD‐ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental‐level accuracy. Interatomic potential ; Transferability ; Computer science ; Molecular dynamics ; Buckingham ; Experimental data ; Machine learning ; Space (punctuation) ; Artificial intelligence ; Algorithm ; Simulation ; Statistical physics ; Mathematics ; Chemistry ; Physics ; Computational chemistry ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Dong Xiao | Hongzong Li | Guotai Jiang
Source:  (article, 2019-07-12)
Field: Chemistry — Analytical Chemistry

Spectral and BP Neural Network Research on Classification of Iron Ore Iron ore is an important raw material for industrial production and daily use. The iron content of iron ore determines its grade and quality, which further affects the cost and economic benefits of mining ore. The classification of iron ore is an important attribute affecting its iron content. The judgment and prediction of iron ore category plays an important role in its iron content prediction. Therefore, this paper studies the prediction of iron ore types. The principal component analysis method is used to reduce the dimension of the spectral data of high-dimensional iron ore, and then the dimension-reduced data is constructed into BP neural network for training. The model is tested by the test set test model. The accuracy of many experiments is more than 90 percent. When the parameters are selected properly, it can reach 100 percent. Artificial neural network ; Iron ore ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Metallurgy ; Materials science Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Advanced Algorithms and Applications
Authors: Abhishek Agarwal | Pradeep Rathore | Vinay Jain | Beena Rai
Source:  (article, 2019-03-24)
Field: Materials Science — Materials Chemistry

In-silico Model for Predicting the Corrosion Inhibition Efficiency of Steel Inhibitors Abstract Quantitative Structure-Activity Relationships (QSAR) based models have been widely used for predicting corrosion inhibition performance of metals. However, one of the major limitations in these studies is that the authors have restricted themselves to use only a single class of molecules having similar molecular structure. In this study, a computational end-to-end framework was developed to investigate the properties of organic corrosion inhibitors which are responsible for inhibition of steel in acidic solution. The framework consists of modules like data preprocessing, descriptor selection and model building. A robust predictive model for multiple class of corrosion inhibitors was developed using advanced machine learning algorithm such as gradient boosting machine (GBM), random forest, support vector machines (SVM) etc. The descriptors were selected using novel integrated ensemble technique. The model based on GBM algorithm was able to predict the corrosion inhibition efficiency of inhibitors with significantly higher accuracy. In silico ; Corrosion ; Materials science ; Computer science ; Metallurgy ; Chemistry ; Biochemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Mourad Rahmani | Zahra Meziani | Zohir Dibi
Source:  (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Modelling graphene/n-Si Schottky junction solar cells by artificial neural networks Schottky junction solar cells (SJSC) using transparent electrodes (TE) are increasingly invading the photovoltaic (PV) field due to easy processing attributes and the ability of TE to reconcile good electrical conductivity with high transmittance. The market of SJSC using TE is dominated by the Indium oxide doped with Tin (ITO) with a resistivity of 30-80 Ω/sq and a transmittance of 90 % in the visible range. However, its high indium content increases its cost, which explains the encouragement of research on alternative materials necessary for the development of PV technologies. It is in this theme that graphene, a material with exceptional properties, is tested as a design material for graphene/n-Si Schottky junction solar cells (G/n-Si SJSC). In this article the model of simulation proposed uses the artificial neural networks (ANN model); this model illustrate the optical and electronical behaviour of G/n-Si SJSC and that by controlling: the layer number (N), working function (WF), transmittance (T) and the sheet resistance (Rsh) of graphene in order to achieve a complete adjustment for this parameters. Our study reported that a G/n-Si SJSC with N=3, WF=5.10eV, T=91.4% and Rsh = 4 Ω/sq leads to a solar efficiency (η) of 5.88%; these results reveal that G/n-Si SJSC is a promising concurrent because it provides a new insights in the field of solar cells. Graphene ; Sheet resistance ; Materials science ; Optoelectronics ; Transmittance ; Indium tin oxide ; Photovoltaic system ; Schottky barrier ; Indium ; Doping ; Schottky diode ; Electrical resistivity and conductivity ; Engineering physics ; Layer (electronics) ; Nanotechnology ; Electrical engineering ; Engineering Graphene research and applications ; Semiconductor materials and interfaces ; Nanowire Synthesis and Applications
Authors: Ruud van Deursen | Guillaume Godin
Source: ChemRxiv (preprint, 2019-10-11)
Field: Materials Science — Materials Chemistry

Deep Generative Model for Sparse Graphs using Text-Based Learning with Augmentation in Generative Examination Networks Graphs and networks are a key research tool for a variety of science fields, most notably chemistry, biology, engineering and social sciences. Modeling and generation of graphs with efficient sampling is a key challenge for graphs. In particular, the non-uniqueness, high dimensionality of the vertices and local dependencies of the edges may render the task challenging. We apply our recently introduced method, Generative Examination Networks (GENs) to create the first text-based generative graph models using one-line text formats as graph representation. In our GEN, a RNN-generative model for a one-line text format learns autonomously to predict the next available character. The training is stopped by an examination mechanism checking validating the percentage of valid graphs generated. We achieved moderate to high validity using dense g6 strings (random 67.8 +/- 0.6, canonical 99.1 +/- 0.2). Based on these results we have adapted the widely used SMILES representation for molecules to a new input format, which we call linear graph input (LGI). Apart from the benefits of a short compressible text-format, a major advantage include the possibility to randomize and augment the format. The generative models are evaluated for overall performance and for reconstruction of the property space. The results show that LGI strings are very well suited for machine-learning and that augmentation is essential for the performance of the model in terms of validity, uniqueness and novelty. Lastly, the format can address smaller and larger dataset of graphs and the format can be easily adapted to define another meaning of the characters used in the LGI-string and can address sparse graph problems in used in other fields of science. Computer science ; Generative grammar ; Generative model ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Curse of dimensionality ; Representation (politics) ; Graph Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Yanfen Gan | Sze‐Teng Liong | Yen‐Chang Huang
Source: arXiv (Cornell University) (preprint, 2019-01-15)
Field: Materials Science — Polymers and Plastics

Automatic Surface Area and Volume Prediction on Ellipsoidal Ham using Deep Learning This paper presents novel methods to predict the surface and volume of the ham through a camera. This implies that the conventional weight measurement to obtain in the object's volume can be neglected and hence it is economically effective. Both of the measurements are obtained in the following two ways: manually and automatically. The former is assume as the true or exact measurement and the latter is through a computer vision technique with some geometrical analysis that includes mathematical derived functions. For the automatic implementation, most of the existing approaches extract the features of the food material based on handcrafted features and to the best of our knowledge this is the first attempt to estimate the surface area and volume on ham with deep learning features. We address the estimation task with a Mask Region-based CNN (Mask R-CNN) approach, which well performs the ham detection and semantic segmentation from a video. The experimental results demonstrate that the algorithm proposed is robust as promising surface area and volume estimation are obtained for two angles of the ellipsoidal ham (i.e., horizontal and vertical positions). Specifically, in the vertical ham point of view, it achieves an overall accuracy up to 95% whereas the horizontal ham reaches 80% of accuracy. Ellipsoid ; Volume (thermodynamics) ; Artificial intelligence ; Surface (topology) ; Computer science ; Point (geometry) ; Segmentation ; Computer vision ; Task (project management) ; Deep learning ; Object (grammar) ; Pattern recognition (psychology) ; Mathematics ; Geometry ; Geography ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Animal Behavior and Welfare Studies
Authors: Jinlong Li | Laijun Sun
Source:  (article, 2019-08-01)
Field: Chemistry — Analytical Chemistry

Study on Detection Methods for Frying Times of Soybean Oil Based on NIRS In the process of deep frying, oil can produce deleterious compounds which are harmful to human health. On the basis of analyzing the changing mechanisms of chemistries in repeatedly used oil, the study proposed a method for rapid detecting the frying times of oil based on near infrared spectroscopy (NIRS) technology. First derivative (D1), second derivative (D2) and standard normal variable transformation (SNV) were served as pretreatment methods, and characteristic wavelengths which sensitive to frying times were extracted by correlation coefficient (CC) method. Support vector machine (SVM), partial least squares regression (PLSR) and radial basis function neural network (RBFNN) were utilized to establish qualitative and quantitative analysis models. It turned out that the qualitative and quantitative analysis models had the best performance when D2 was used to pretreatment spectra and six characteristic wavelengths were extracted. More precisely, classification accuracy of the best SVM model reached 94%. Also, the performance of the best PLSR model was superior to the best RBFNN model, in which the values of correlation coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), root mean square error of prediction (RMSEP), residual predictive deviation (RPD) were 0.9937, 0.3477 and 12.5803 respectively. The overall results indicated that the proposed method had a great potential to accurate detect frying times of oil. Soybean oil ; Computer science ; Pulp and paper industry ; Environmental science ; Food science ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Maryna Viktorivna Ivanova | Yurii Volodymyrovych Vuntesmeri | Liudmyla Mykolaivna Bukhtiiarova
Source: Microsystems Electronics and Acoustics (article, 2019-10-31)
Field: Materials Science — Biomaterials

Automated Detection and Classification of the Diatom Microscopic Photo Images The diatoms (Bacillariophyta) are a species-rich and extremely widespread microalgae. Many diatom species require a fairly narrow range of ecological conditions (temperature, pH, salinity, saprobity, trophic level of the water etc.). They are very sensitive to changes of different water chemical parameters that allows to use of the diatoms as the indicators in monitoring of water quality as well as for the ecological reconstruction using bottom sediments. Different methods of the diatom analysis are based on the species composition and species amount in the preparations of plankton, benthos and epyphyton. However this analysis requires highly qualified staff and the manual processing of hundreds samples. The development of the computer tools for the automatic diatom species detection and identification is an actual task to improve the effectiveness of ecological studies and simplify the time-consuming routine work of the researchers. The image processing methods were investigated for the diatom species automatic identification in the light microscopic photos of the samples that were collected for the routine monitoring. The methods of segmentation of the images based on the threshold processing and morphological differentiation of the images were studied. The two-steps segmentation of the diatom photos was proposed. The method of artificially expanding the training dataset by sequential image transformation was applied. Based on the prepared training dataset, two realizations of convolutional neural networks were trained and their accuracy evaluated. Diatom ; Artificial intelligence ; Computer science ; Remote sensing ; Computer vision ; Pattern recognition (psychology) ; Geology ; Oceanography Diatoms and Algae Research ; Water Quality Monitoring and Analysis
Authors: Shiling Zhang
Source:  (article, 2019-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Numerical Calculation of Multi-Physical Field Coupling for High Voltage Basin Insulator With requirement of high voltage and high current transmission, operation performance of basin insulators needs to be analyzed in the multi-physical coupling simulation. Electrical-thermal-mechanical performance parameters of basin insulator bulk materials are obtained through experiments. Based on the experimental data, multi-physical field finite element method for calculating electric-thermal-mechanical system is proposed. The non-linear relationship between dielectric constant, tangent value of the loss angle, the thermal conductivity and thermal expansion coefficient is taken into account in calculation of a single physical field. Three-dimensional finite element model of basin insulator is established to decouple the electric field, the temperature field and the stress field. The RBF neural network is used to learn the simulation data of the electric-thermal-mechanical multi-physical coupling field, so as to realize non-linear inversion of the optimal structural parameters of basin insulator. The application results show that the decoupling simulation of basin insulators can be realized by the multi-physical field finite element method. The maximum electric field strength of the basin body is also near the central conductor. The maximum electric field strength is 11.85 kV/mm. The maximum temperature of the basin body at the interface between the central conductor and the epoxy material at 6300A is 97 degree. Most significant displacement and stress concentration areas are mainly located at the flange side of the basin insulator. The research results of this paper can effectively guide the design of high voltage basin insulator structure, and have certain theoretical guidance value for fault diagnosis and pattern recognition of basin insulator in GIS/GIL equipment. Insulator (electricity) ; Electric field ; Finite element method ; Materials science ; Voltage ; Dielectric ; High voltage ; Mechanics ; Electrical engineering ; Structural engineering ; Physics ; Engineering ; Composite material ; Optoelectronics Multiferroics and related materials ; Smart Materials for Construction ; Climate change and permafrost
Authors: Cheng Chen | Xinyao Tang | Naren Vikram Raj Masna | Swarup Bhunia | Soumyajit Mandal
Source: Journal of Magnetic Resonance (article, 2019-12-12)
Field: Materials Science — Materials Chemistry

Single-shot spatially-localized NQR using field-dependent relaxation rates Nuclear quadrupole resonance ; Biological system ; Relaxation (psychology) ; Quadrupole ; Chemistry ; Field (mathematics) ; Sodium chlorate ; Materials science ; Nuclear magnetic resonance ; Computational physics ; Molecular physics ; Physics ; Atomic physics ; Mathematics Solid-state spectroscopy and crystallography ; Advanced NMR Techniques and Applications ; NMR spectroscopy and applications