jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aderval S. Luna | Alexandre Rodrigues Tôrres | Camilla L. Cunha | Igor Campos de Almeida Lima | Luís Gustavo Nonato
Source: Brazilian Journal of Analytical Chemistry (article, 2022-08-31)
Field: Chemistry — Analytical Chemistry

Employing Auto-Machine Learning Algorithms for Predicting the Cold Filter Plugging and Kinematic Viscosity at 40 ºC in Biodiesel Blends using Vibrational Spectroscopy Data This work aims to develop an auto-machine learning method using Mid-Infrared (MIR) spectroscopy data to determine the cold filter plugging point (CFPP) and kinematic viscosity at 40 ºC of biodiesel, diesel, and mixtures samples. The biodiesel was obtained by the transesterification reaction and later purified. The first dataset was composed of 108 blends (biodiesel obtained from different biomass such as soy, corn, sunflower, and canola) with binary, ternary and quaternary mixtures. The second dataset was composed of 227 blends of diesel-biodiesel and diesel-biodiesel-ethanol, respectively. The physical properties of the samples were obtained according to ABNT NBR 14747 and ABNT NBR 10441, respectively. The MIR Spectroscopy data were acquired from 7,800 to 450 cm-1, with a 4 cm-1 resolution and 20 scans. The spectra' baseline alignment was carried out using the asymmetric least squares method. A Savitzky–Golay filter was applied to a set of digital data points to smooth the data. This work used a first-order polynomial and a zero derivative function to smooth the spectra. The dataset was split into training and test sets using the function CreateDataPartition from the caret package. It was adopted 70% for training and 30% for test sets. In this work, the model training process was carried out using the open-source Python library LazyPredict. The LazyPredict returns the trained models and their performance metrics. The kinematic viscosity at 40 ºC of the biodiesel samples and their blends could be modeled using the MIR Spectroscopy dataset using different auto-machine learning algorithms. The RMSEP (Root Mean Square Error of Prediction) (≤ 0.02 mm2 s-1) was similar to the experimental error obtained after log transformation. The CFPP of the biodiesel samples and their blends could be modeled using the MIR Spectroscopy dataset by different auto-machine learning algorithms with an RMSEP (≤ 1.6 ºC) similar to the experimental error obtained by traditional methodology. Based on the lower computational time and the same performance observed by the RMSEP and R2 (coefficient of determination) values from different algorithms, it is recommended to use Ridge or Ridge Cross-Validation Regression models for both physical properties using MIR Spectroscopy data. Biodiesel ; Diesel fuel ; Algorithm ; Transesterification ; Machine learning ; Mathematics ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Methanol ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Mohamed Azouz Mrad | Kristóf Csorba | Dorián László Galata | Zsombor Kristóf Nagy | Brigitta Nagy
Source:  (preprint, 2022-09-17)
Field: Chemistry — Analytical Chemistry

Comparing Spectroscopy Measurements in the Prediction of in Vitro Dissolution Profile using Artificial Neural Networks Dissolution testing is part of the target product quality that is essential in approving new products in the pharmaceutical industry. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near-infrared (NIR) spectroscopies are two fast and complementary methods that provide information on the tablets' physical and chemical properties and can help predict their dissolution profiles. This work aims to compare the information collected by these spectroscopy methods to support the decision of which measurements should be used so that the accuracy requirement of the industry is met. Artificial neural network models were created, in which the spectroscopy data and the measured compression curves were used as an input individually and in different combinations in order to estimate the dissolution profiles. Results showed that using only the NIR transmission method along with the compression force data or the Raman and NIR reflection methods, the dissolution profile was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy measurements increased the prediction accuracy. Dissolution ; Raman spectroscopy ; Artificial neural network ; Biological system ; Spectroscopy ; Chemometrics ; Calibration ; Principal component analysis ; Materials science ; Near-infrared spectroscopy ; Computer science ; Process engineering ; Artificial intelligence ; Data mining ; Machine learning ; Chemistry ; Optics ; Mathematics ; Statistics ; Engineering Spectroscopy and Chemometric Analyses ; Drug Solubulity and Delivery Systems ; Crystallization and Solubility Studies
Authors: Alireza Aghajamali | Amir Karton
Source: Journal of Applied Physics (article, 2022-08-11)
Field: Chemistry — Organic Chemistry

Comprehensive theoretical study of the correlation between the energetic and thermal stabilities for the entire set of 1812 C60 isomers The thermal stability of fullerenes plays a fundamental role in their synthesis and in their thermodynamic and kinetic properties. Here, we perform extensive molecular dynamics (MD) simulations using an accurate machine-learning-based Gaussian Approximation Potential (GAP-20) force field to investigate the energetic and thermal properties of the entire set of 1812 C60 isomers. Our MD simulations predict a comprehensive and quantitative correlation between the relative isomerization energy distribution of the C60 isomers and their thermal fragmentation temperatures. We find that the 1812 C60 isomers span over an energetic range of over 400 kcal mol−1, where the majority of isomers (∼85%) lie in the range between 90 and 210 kcal mol−1 above the most stable C60-Ih buckminsterfullerene. Notably, the MD simulations show a clear statistical correlation between the relative energies of the C60 isomers and their fragmentation temperature. The maximum fragmentation temperature is 4800 K for the C60-Ih isomer and 3700 K for the energetically least stable isomer, where nearly 80% of isomers lie in a temperature window of 4000–4500 K. In addition, an Arrhenius-based approach is used to map the timescale gap between simulation and experiment and establish a connection between the MD simulations and fragmentation temperatures. Isomerization ; Fullerene ; Chemistry ; Molecular dynamics ; Arrhenius equation ; Fragmentation (computing) ; Kinetic energy ; Computational chemistry ; Thermodynamics ; Buckminsterfullerene ; Physical chemistry ; Chemical physics ; Activation energy ; Physics ; Organic chemistry Fullerene Chemistry and Applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: KSPEU Kazan, Russian Federation | A. M. Gataullin | Д. Ф. Губаев
Source: Problems of the Regional Energetics (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Integrated Method of Assessment the Type of Damages, their Localization and Predicting the Destruction of High-Voltage Electrical Equipment Insulators by the Partial Discharges Characteristics The aim of this study is to improve the accuracy of localization of defects in insulators and determine their type. This goal is achieved by solving the problem of combining contact and remote methods for polymer and porcelain insulators using model partial discharges. The most significant results are the regularities of the dynamics of the characteristics of partial discharges up to the prebreakdown situation for porcelain insulators, the study of the statistical distributions of partial discharges depending on their intensity, and the identification of the features of the statistical distribution of surface discharges. Part of the work is devoted to the study of the characteristics of partial discharges and their sources by spectra, polarity, statistical distributions, oscillograms, which is important from the point of view of automating the recognition of corona and internal partial discharges, as well as for the recognition of porcelain insulators destroyed by partial discharges. Regularities of changes in the statistical distribution of partial discharges up to the pre-breakdown situation were established. At the same time, the breakdown signs of the model discharge gap, the breakdown voltage values for defective and operable porcelain insulators are determined, which can be used to train models of artificial neural networks and recognize the pre-breakdown situation based on them. The most significant results were: assessment of the ohmic resistance of porcelain insulators by the characteristics of partial discharges, recognition of corona, internal and surface partial discharges of polymer insulators, localization of defects, using electromagnetic radiation sensors. Partial discharge ; Partial pressure ; Ohmic contact ; Materials science ; Corona (planetary geology) ; Insulator (electricity) ; Polarity symbols ; High voltage ; Voltage ; Acoustics ; Mechanics ; Electrical engineering ; Breakdown voltage ; Composite material ; Engineering ; Chemistry ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Jun Liao | Alexander Mace | Guoqiang Wang | Jesse S. Fisher
Source:  (article, 2022-08-08)
Field: Materials Science — Materials Chemistry

Applying Machine Learning to Improve BOA Analysis of CIPS/CILC Abstract CRUD depositions on fuel cladding are generated as a result of coolant chemistry and corrosion of primary system components during the normal operation of a nuclear reactor. These depositions can affect both reactor power peaking and fuel element structural integrity through CRUD Induced Power Shifts (CIPS) and CRUD Induced Local Corrosion (CILC) phenomena, respectively. The CIPS/CILC phenomena are typically assessed using the Boron-induced Offset Anomaly (BOA) computer code, which predicts the characterization of the distribution of CRUD within a reactor and the risk for occurrence of CIPS/CILC within a given cycle. Machine learning (ML) algorithms have often been applied towards understanding large data sets and acquiring useable information in many industries, including nuclear. In the nuclear engineering field, ML is increasingly being used as a method to improve nuclear power plant efficiency and reduce engineering costs. The application of machine learning to enhance the prediction of CIPS/CILC increases the possibility of reducing the cost associated with CRUD mitigation. In Westinghouse, exploratory analysis was performed to identify ways in which ML could be utilized to improve CIPS risk assessments. There were two paths identified, the first one was ML applications for CIPS with the standalone BOA code, and the second path was ML applications for CIPS calibration with coupled ANC/FUELUDTYDRV/VIPRE-W/BOA code. The exploratory study demonstrated that ML was capable of accurately predicting the boron mass in the cores of selected nuclear power plants as surrogate models of the BOA code. Cladding (metalworking) ; Nuclear power ; Nuclear power plant ; Computer science ; Coolant ; Offset (computer science) ; Corrosion ; Nuclear engineering ; Reliability engineering ; Materials science ; Engineering ; Mechanical engineering ; Physics ; Nuclear physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Xiaoyan Yin | Robert Spatschek | Norbert H. Menzler | Claas Hüter
Source: Materials (article, 2022-04-14)
Field: Materials Science — Materials Chemistry

A Pragmatic Transfer Learning Approach for Oxygen Vacancy Formation Energies in Oxidic Ceramics Lower oxygen vacancy formation energy is one of the requirements for air electrode materials in solid oxide cells applications. We introduce a transfer learning approach for oxygen vacancy formation energy prediction for some ABO3 perovskites from a two-species-doped system to four-species-doped system. For that, an artificial neural network is used. Considering a two-species-doping training data set, predictive models are trained for the determination of the oxygen vacancy formation energy. To predict the oxygen vacancy formation energy of four-species-doped perovskites, a formally similar feature space is defined. The transferability of predictive models between physically similar but distinct data sets, i.e., training and testing data sets, is validated by further statistical analysis on residual distributions. The proposed approach is a valuable supporting tool for the search for novel energy materials. Vacancy defect ; Transferability ; Doping ; Materials science ; Oxide ; Energy (signal processing) ; Artificial neural network ; Transfer of learning ; Oxygen ; Ceramic ; Chemical physics ; Training set ; Computer science ; Statistical physics ; Machine learning ; Chemistry ; Artificial intelligence ; Physics ; Condensed matter physics ; Optoelectronics ; Mathematics ; Statistics Advancements in Solid Oxide Fuel Cells ; Machine Learning and ELM ; Fault Detection and Control Systems
Authors: Peitao Liu | Jian-Tao Wang | Noah Avargues | Carla Verdi | Andreas Singraber | Ferenc Karsai | Xing‐Qiu Chen | Georg Kresse
Source: arXiv (Cornell University) (preprint, 2022-08-20)
Field: Materials Science — Materials Chemistry

Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111) Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in surface sciences and catalysis. Despite its simplicity, it has posed great challenges to theoretical modeling. Pretty much all existing density functionals fail to accurately describe surface energies, CO adsorption site preference, as well as adsorption energies simultaneously. Although the random phase approximation (RPA) cures these density functional theory failures, its large computational cost makes it prohibitive to study the CO adsorption for any but the simplest ordered cases. Here, we address these challenges by developing a machine-learned force field (MLFF) with near RPA accuracy for the prediction of coverage-dependent adsorption of CO on the Rh(111) surface through an efficient on-the-fly active learning procedure and a $Δ$-machine learning approach. We show that the RPA-derived MLFF is capable to accurately predict the Rh(111) surface energy, CO adsorption site preference as well as adsorption energies at different coverages that are all in good agreement with experiments. Moreover, the coverage-dependent ground-state adsorption patterns and adsorption saturation coverage are identified. Adsorption ; Density functional theory ; Carbon monoxide ; Saturation (graph theory) ; Materials science ; Computer science ; Chemistry ; Chemical physics ; Catalysis ; Computational chemistry ; Physical chemistry ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: R. Sankarganesh | S. Chandrasekar
Source: 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (article, 2022-07-15)
Field: Materials Science — Materials Chemistry

Flashover Identification of Aged Silicone Rubber Insulators Using Partial Discharge Signal Processing and Machine Learning Random Forest Algorithm Due to a variety of factors including light weight, effortlessness of moving and installation, towering automatic power to heaviness percentage, destruction resistance, aesthetic appearance, and better padding piece, silicone rubber outdoor insulators have replaced ceramic units in distribution networks. The flashover analysis of long-term aged silicone rubber insulators is the subject of this research. The investigation is base on the limited release approach, which is a very effective and efficient method that is nearly universally used in high-voltage transmission and distribution systems. On aged polymeric insulators, laboratory experiments are conducted under ac voltage, at various humidity levels, and at various infectivity level with NACL impurity. The insulator was thermally aged for 100 days in the laboratory within the heat cavity.This approach simulated the conditions encountered in real-time field applications. The partial discharge signals are collected using a PD monitoring device that can capture PD waveforms as well as phase resolved patterns.The time domain and frequency domain features of PD pulses are investigated in order to make a comparison between new and old polymeric insulators. To detect insulator flashover due to pollution, a machine learning-based Random forest (RF) classifier technique is used.The results show that the performance of outdoor polymeric insulators degrades with age, and that flashover of silicone rubber insulators may be detected using partial discharge pattern analysis and an RF classifier model. Silicone rubber ; Partial discharge ; Insulator (electricity) ; Arc flash ; Materials science ; Composite material ; Voltage ; Electrical engineering ; Computer science ; Electronic engineering ; Engineering High voltage insulation and dielectric phenomena ; Fire Detection and Safety Systems ; Advanced Computing and Algorithms
Authors: Seunghun Jang | Gyoung S. Na | Jung‐Ho Lee | Jung‐Ho Shin | Hyun Woo Kim | Hyunju Chang
Source: Integrating materials and manufacturing innovation (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

An Easy, Simple, and Accessible Web-based Machine Learning Platform, SimPL-ML Computer science ; Preprocessor ; Simple (philosophy) ; Machine learning ; Artificial intelligence ; Generator (circuit theory) ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Joseph B. Choi | Phong Nguyen | Oishik Sen | H. S. Udaykumar | Stephen Baek
Source: arXiv (Cornell University) (preprint, 2022-11-15)
Field: Materials Science — Materials Chemistry

Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research. Interpretability ; Computer science ; Artificial intelligence ; Machine learning ; Scalability ; Generalizability theory ; Representation (politics) ; Property (philosophy) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Calcium Carbonate Crystallization and Inhibition
Authors: Sara Mantach | Ahmed Ashraf | Puneet Gill | Derek R. Oliver | Behzad Kordi
Source: 2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

Assessing One-vs-All 1D-CNN Classifiers for Multi-Label Classification of Partial Discharge Waveforms in 3D-Printed Dielectric Samples with Different Void Sizes Effective insulation degradation diagnosis is essential for monitoring the reliability of any electrical system. One cause of degradation within insulation materials is the occurrence of partial discharge in voids. The severity of the degradation is related to the size of these voids inside the material. Hence, non-invasive classification of the void size could be important for cost-effective maintenance. However, multiple void sizes can exist concurrently within the insulation material which makes the problem a multi-label classification problem. In this paper, the performance of a collection of one-versus-all one-dimensional convolutional neural network (CNN) was investigated to classify different void sizes inside 3D-printed dielectric samples. Training of the CNN classification algorithm was done on single void-size samples and testing was done on single and multiple void-size samples. The CNN took a set of PD time-series waveforms as the input and investigation was carried out to assess the performance of such a system when multi-labeled signals were presented in the testing phase. In addition, the effect of the number of the classified classes on the performance of the proposed system was considered. Partial discharge ; Void (composites) ; Waveform ; Convolutional neural network ; Dielectric ; Computer science ; Pattern recognition (psychology) ; Porosity ; Artificial neural network ; Artificial intelligence ; Materials science ; Voltage ; Engineering ; Electrical engineering ; Composite material High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Currency Recognition and Detection