jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nasser M. Al-Hajri | Abdullah Alghamdi
Source:  (article, 2019-10-16)
Field: Materials Science — Biomaterials

Scale Prediction and Inhibition Design Using Machine Learning Techniques Abstract This paper will introduce a data driven methodology to predict scale formation and design its inhibition program in petroleum wells. The proposed methodology integrates and adds to the existing principles of production surveillance, chemistry, machine learning and probability theory in a comprehensive decision workflow to achieve its purpose. The proposed model was applied on a large and representative field sample to verify its results. The method starts by collecting data such as ionic composition, pH, sample collection and inspection dates, and scale formation event. Then, collected data are classified or grouped based on production conditions. Calculation of chemical scale indexes is then made using techniques such as water saturation level, Langelier saturation index, Ryznar saturation index, and Puckorius scaling index. The machine learning part of the method starts by dividing the data into training and test sets (80% and 20%, respectively). Classification models such as support vector machine, K-nearest neighbors, gradient boosting, and decision tree classifier are all applied on collected data. Prediction results are then classified into a confusion matrix to be used as inputs for the probabilistic inhibition design model. The scale inhibition program design is based on a probabilistic model that quantifies the uncertainty associated with each machine learning method. The scale prediction capability compared to actual inspection is presented into probability equations that are used in the cost model. The expected financial impact associated with applying any of the machine learning method is obtained from defining costs for scale removal and scale inhibition. These costs are factored into the probability equations in a manner that presents incurred costs and saved or avoided expenses expected from field application of any given machine learning model. The forecasted cost model is built on a base case method (i.e. current situation) to be used as a benchmark and foundation for the new scale inhibition program. As will be presented in the paper, the results of applying the above techniques resulted in a scale prediction accuracy of 95% and realized three-folds of cost savings figures compared to existing programs. Machine learning ; Decision tree ; Computer science ; Artificial intelligence ; Probabilistic logic ; Probabilistic classification ; Gradient boosting ; Data mining ; Support vector machine ; Boosting (machine learning) ; Classifier (UML) ; Naive Bayes classifier ; Random forest Calcium Carbonate Crystallization and Inhibition ; Hydraulic Fracturing and Reservoir Analysis ; Oil and Gas Production Techniques
Authors: Ryoichi Doi
Source: Journal of Analytical Methods in Chemistry (article, 2019-04-09)
Field: Chemistry — Analytical Chemistry

Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, ammonium ion, and phosphate ion using a pseudocolor imaging method. Each PackTest product underwent reactions with standard solutions. The generated color was scanner-read. From the color image, ten grayscale images representing the intensity values of red, green, blue, cyan, magenta, yellow, key black, and <i>L</i> <sup><i>∗</i></sup> , and the values of <i>a</i> <sup><i>∗</i></sup> and <i>b</i> <sup><i>∗</i></sup> were generated. Using the grayscale images representing the red, green, and blue intensity values, 73 other grayscale images were generated. The grayscale intensity values were used to prepare datasets for the ten and 83 (=10 + 73) images. For both datasets, chemical oxygen demand quantification was successful, resulting in values of normalized mean absolute error of less than 0.4% and coefficients of determination that were greater than 0.9996. However, the quantification of ammonium and phosphate ions commonly provided false positive results for the standard solution that contained no ammonium ion/phosphate ion. For ammonium ion, multiple regression markedly improved the accuracy using the pseudocolor method. Phosphate ion quantification was also improved by avoiding the use of an estimated value for the reference solution that contained no phosphate ion. Real details of the measurements and the perspectives were discussed. Grayscale ; Artificial intelligence ; Algorithm ; Computer science ; Pixel Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Marina Ņikitina | И. М. Чернуха | V.A. Pchelkina
Source: IOP Conference Series Earth and Environmental Science (article, 2019-09-01)
Field: Chemistry — Analytical Chemistry

Artificial neural network technologies as a tool to histological preparation analysis Abstract Food fraud has become one of the great internal and external threats nowadays. For effective and objective detection of adulterated products, real-time control systems are used.Microstructural analysis is one of these methods. It allows rapid quality and composition assessment of food’s raw materials and the finished products. Human skills and expertise/involvement in interpreting the resultsof microstructural analysis is still absolutely necessary, despite the possibility this is subjective. Modern hardware/software systems for histological analysis with neural network technologies will exclude the subjectivity of that result interpretation. For convolutional neural network operating, the “learning with the teacher” model is used. With this training method, a set of data is prepared, whichacts as a series of observations, and for which the values of the input and output variables are indicated, such as: histological analysis data &gt; conclusion about the sample composition &gt; adulteration definition. The network learns to establish connections between them. This paper presents the classification parameters for vegetable food component identification as a part of meat raw materials and finished products. A unified database structure has been developedfor structuring and summarizing the main microstructural characteristics of histological sections for various types of meat raw materials. The production-rule systems of “ If … then … else ” are given, and vegetable protein components were chosen as an example. Based on these rules, training of convolutional neural network occurs. Structuring ; Convolutional neural network ; Computer science ; Artificial neural network ; Sample (material) ; Identification (biology) ; Raw material ; Artificial intelligence ; Raw data ; Set (abstract data type) ; Quality (philosophy) ; Component (thermodynamics) ; Pattern recognition (psychology) ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Food Industry and Aquatic Biology
Authors: Arvind Keprate | R. M. Chandima Ratnayake
Source:  (article, 2019-06-09)
Field: Materials Science — Materials Chemistry

Data Mining for Estimating Fatigue Strength Based on Composition and Process Parameters Abstract Accurately estimating the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and often fatal consequences of fatigue failures. The main objective of this manuscript is to perform data mining on the fatigue dataset for steel available from the National Institute of Material Science (NIMS) MatNavi. The cross-industry process for data mining (CRISP-DM) approach was followed in the paper, in order to gain meaningful insights from the dataset and to estimate the fatigue strength of carbon and low alloy steels, using composition and processing parameters. Of the six steps of the CRISP-DM approach, special emphasis has been placed on steps 2 to 5 (i.e. data understanding, data preparation, modeling and evaluation). In step 4 (i.e. modeling), a range of machine learning (parametric and non-parametric) is explored to predict the fatigue strength, based on the composition and process parameters. Various algorithms were trained and tested on the dataset and finally evaluated, using metrics such as root mean square error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2) and Explained Variance Score (EVS). Mean squared error ; Parametric statistics ; Process (computing) ; Computer science ; Range (aeronautics) ; Data mining ; Fatigue limit ; Statistics ; Engineering ; Mathematics ; Structural engineering Metal Alloys Wear and Properties ; Fatigue and fracture mechanics ; Advanced machining processes and optimization
Authors: Zijiang Yang | Tetsushi Watari | Daisuke Ichigozaki | Kei Morohoshi | Yoshinori Suga | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source:  (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Data-Driven Insights from Predictive Analytics on Heterogeneous Experimental Data of Industrial Magnetic Materials Data-driven methods are becoming increasingly popular in the field of materials science. While most data-driven models are trained on simulation data as it is relatively easier to collect a large amount of data from physics-based simulations, there are many challenges in applying data-driven methods on experiments: 1) experimental data is usually not clean; and 2) it generally has a greater degree of heterogeneity. In this project, we have developed a data-driven methodology to address these challenges on an industrial magnet dataset, where the goal is to predict magnetic properties (forward models) at different stages of the experimental workflow. The data-driven methodology consists of data cleaning, data preprocessing, feature extraction, and model development using traditional machine learning and deep learning methods to accurately predict magnet properties. In particular, we have developed three different types of predictive models: 1) numerical model using only numerical data containing composition and processing information; 2) image model using image data representing structure information; and 3) combination model using both types of data together. In addition to predictive models, the analysis and comparison of results across the models provide several interesting data-driven insights. Such data-driven analytics has the potential to help guide future experiments and realize the inverse models, which could significantly reduce costs and accelerate the discovery of new magnets with superior properties. The proposed models are already deployed in Toyota Motor Corporation. Computer science ; Data pre-processing ; Workflow ; Data-driven ; Data modeling ; Predictive modelling ; Data mining ; Field (mathematics) ; Predictive analytics ; Data type ; Machine learning ; Data analysis ; Experimental data ; Analytics ; Preprocessor ; Artificial intelligence Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Katja Ovchinnikova | Alexander Rakhlin | Lachlan Stuart | Sergey Nikolenko | Theodore Alexandrov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-09-08)
Field: Chemistry — Spectroscopy

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images Abstract Motivation Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. Results We present ColocAI, an artificial intelligence approach addressing this gap. With the help of 42 imaging MS experts from 9 labs, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using tf-idf and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings respectively). We illustrate these measures by inferring co-localization properties of 10273 molecules from 3685 public METASPACE datasets. Availability and Implementation https://github.com/metaspace2020/coloc Contact theodore.alexandrov@embl.de Artificial intelligence ; Thresholding ; Pattern recognition (psychology) ; Computer science ; Deep learning ; Artificial neural network ; Image (mathematics) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Xiwen Chen | Bing Lu | Dandan Zhang | Jun Zhang | Jingting Chen
Source: Journal of Physics Conference Series (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Effect of temperature cycling on performance of epoxy resin and evaluation of state Abstract Epoxy resin samples were prepared according to the most common types of epoxy resins and curing agents in the market. The material was temperature-circulated at -40 to 150°C to investigate the effect of temperature cycling on the dielectric properties and bending properties of the material. The results show that with the increase of the number of temperature cycles, the relative dielectric constant, dielectric loss, flexural strength and flexural modulus of epoxy resin increase first, then decrease and then increase, and the electrical conductivity increases. Based on the samples of various performance values of epoxy resin in different times, an evaluation model based on BP neural network was established to evaluate the state of epoxy resin. Epoxy ; Composite material ; Materials science ; Flexural strength ; Dielectric ; Curing (chemistry) ; Flexural modulus ; Modulus ; Conductivity ; Chemistry High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Loı̈c M. Roch | Semion K. Saikin | Florian Häse | Pascal Friederich | Randall H. Goldsmith | Salvador León | Alán Aspuru‐Guzik
Source: arXiv (Cornell University) (preprint, 2019-09-24)
Field: Materials Science — Polymers and Plastics

From absorption spectra to charge transfer in PEDOT nanoaggregates with machine learning Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider PEDOT:PSS, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions, and that the electronic couplings can be predicted from the simulated spectra with an 88 % accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multi-scale computational workflow, they were able to leverage leverage experimental data. Leverage (statistics) ; PEDOT:PSS ; Spectral line ; Materials science ; Absorption spectroscopy ; Workflow ; Characterization (materials science) ; Electronic structure ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Nanotechnology ; Physics ; Polymer ; Computational chemistry ; Database ; Composite material ; Optics ; Quantum mechanics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Electrochemical Analysis and Applications
Authors: Guang-He Lee | Wengong Jin | David Alvarez-Melis | Tommi Jaakkola
Source: arXiv (Cornell University) (article, 2019-02-26)
Field: Materials Science — Materials Chemistry

Functional Transparency for Structured Data: a Game-Theoretic Approach We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an unrestricted predictor such as a neural network, and a set of witnesses chosen from the desired transparent family. The goal of the witnesses is to highlight, locally, how well the predictor conforms to the chosen family of functions, while the predictor is trained to minimize the highlighted discrepancy. We emphasize that the predictor remains globally powerful as it is only encouraged to agree locally with locally adapted witnesses. We analyze the effect of the proposed approach, provide example formulations in the context of deep graph and sequence models, and empirically illustrate the idea in chemical property prediction, temporal modeling, and molecule representation learning. Transparency (behavior) ; Computer science ; Representation (politics) ; Artificial neural network ; Context (archaeology) ; Artificial intelligence ; Property (philosophy) ; Set (abstract data type) ; Machine learning ; Theoretical computer science ; Training set ; Graph ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Cunfu He | CAI Yanchao | Xiucheng Liu | Wu Bin
Source: Journal of Mechanical Engineering (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comparative of Models for Quantitative Prediction of Surface Hardness in S136 Steel Based on Magnetic Barkhausen Noise Abstract：The surface hardness is important index for the manufacturing quality evaluation of ferromagnetic materials and the non-destructively quantitative testing of surface hardness is one of the hot topics in the field.Magnetic Barkhausen noise (MBN) measurement technique is applied for quantitative evaluation of surface hardness in S136 steel.A self-developed device is used to conduct MBN measurements in sixty specimens of different surface hardness.The coefficient of variation of the repeated measured magnetic parameters is analyzed to show that the device has good repeated accuracy for MBN measurements.To achieve quantitative characterization of the surface hardness using magnetic parameters, the dependency of the surface hardness on six parameters extracted from the measured MBN and tangential magnetic field signals is individually analyzed.More importantly, comparative study is performed among the simple linear regression model, multiple linear regression model and BP neural network model to evaluate their accuracy in surface hardness prediction.The results show that the BP neural network model employing six magnetic parameters as inputs has highest accuracy in surface hardness prediction.A total of 300 cases are investigated and it is found that the average prediction error is only around 2.14% and the maximum error is about 11.74%, and the prediction errors out of 274 of 300 cases are less than 5.00%.A reference is provided for realizing the nondestructive quantitative detection of steel plate surface hardness based on magnetic Buckhausen noise method. Barkhausen effect ; Materials science ; Hardness ; Metallurgy ; Noise (video) ; Computer science ; Artificial intelligence ; Magnetic field ; Magnetization ; Physics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Florian Seefried | Tobias Schmidt | Maria Reinecke | Stephanie Heinzlmeir | Bernhard Küster | Mathias Wilhelm
Source: Journal of Proteome Research (article, 2019-02-25)
Field: Chemistry — Spectroscopy

CiRCus: A Framework to Enable Classification of Complex High-Throughput Experiments Despite the increasing use of high-throughput experiments in molecular biology, methods for evaluating and classifying the acquired results have not kept pace, requiring significant manual efforts to do so. Here, we present CiRCus, a framework to generate custom machine learning models to classify results from high-throughput proteomics binding experiments. We show the experimental procedure that guided us to the layout of this framework as well as the usage of the framework on an example data set consisting of 557 166 protein/drug binding curves achieving an AUC of 0.9987. By applying our classifier to the data, only 6% of the data might require manual investigation. CiRCus bundles two applications, a minimal interface to label a training data set (CindeR) and an interface for the generation of random forest classifiers with optional optimization of pretrained models (CurveClassification). CiRCus is available on https://github.com/kusterlab accompanied by an in-depth user manual and video tutorial. Computer science ; Throughput ; Random forest ; Classifier (UML) ; Interface (matter) ; Set (abstract data type) ; Machine learning ; Data set ; Artificial intelligence ; Data mining ; Programming language ; Parallel computing ; Operating system Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Protein Structure and Dynamics