jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriele Scalia | Colin A. Grambow | Barbara Pernici | Yi‐Pei Li | William H. Green
Source: arXiv (Cornell University) (preprint, 2019-10-07)
Field: Materials Science — Materials Chemistry

Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolution neural networks (GCNNs) reporting state-of-the-art performance for this task. However, some challenges remain and one of the most important that needs to be fully addressed concerns uncertainty quantification. DNN performance is affected by the volume and the quality of the training samples. Therefore, establishing when and to what extent a prediction can be considered reliable is just as important as outputting accurate predictions, especially when out-of-domain molecules are targeted. Recently, several methods to account for uncertainty in DNNs have been proposed, most of which are based on approximate Bayesian inference. Among these, only a few scale to the large datasets required in applications. Evaluating and comparing these methods has recently attracted great interest, but results are generally fragmented and absent for molecular property prediction. In this paper, we aim to quantitatively compare scalable techniques for uncertainty estimation in GCNNs. We introduce a set of quantitative criteria to capture different uncertainty aspects, and then use these criteria to compare MC-Dropout, deep ensembles, and bootstrapping, both theoretically in a unified framework that separates aleatoric/epistemic uncertainty and experimentally on the QM9 dataset. Our experiments quantify the performance of the different uncertainty estimation methods and their impact on uncertainty-related error reduction. Our findings indicate that ensembling and bootstrapping consistently outperform MC-Dropout, with different context-specific pros and cons. Our analysis also leads to a better understanding of the role of aleatoric/epistemic uncertainty and highlights the challenge posed by out-of-domain uncertainty. Computer science ; Bootstrapping (finance) ; Uncertainty quantification ; Scalability ; Inference ; Machine learning ; Artificial intelligence ; Generalization ; Dropout (neural networks) ; Context (archaeology) ; Data mining ; Bayesian probability ; Econometrics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jacob Søgaard Larsen | Line Katrine Harder Clemmensen
Source: arXiv (Cornell University) (preprint, 2019-10-01)
Field: Chemistry — Analytical Chemistry

Deep learning for Chemometric and non-translational data We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in chemometrics where individual measurements represent exact chemical compounds and thus signals cannot be translated or resized without disturbing their interpretation. Our approach show superior performance compared to transfer learning when a medium sized and a small data set are trained together. While we observe a small improvement compared to individual training when two medium sized data sets are trained together, in particular through a reduction in the variance. Artificial intelligence ; Computer science ; Deep learning ; Data science ; Machine learning Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: In Hyuk Choi | Ju Am Son | Ja Bin Koo | Young-Geun Yoon | Tae Keun Oh
Source: Applied Sciences (article, 2019-08-02)
Field: Materials Science — Materials Chemistry

Damage Assessment of Porcelain Insulators through Principal Component Analysis Associated with Frequency Response Signals More than 55% of porcelain insulators installed throughout Korea have exceeded their service life. Hence, utilities are extremely interested in determining the robustness of insulators in their systems. In this study, the identification of the peak ranges in the main natural modes by frequency response analysis, the principal component analysis (PCA) method by feature extraction in the time and frequency domains for the damage detection of porcelain insulators are investigated; among these, the PCA method, which utilizes frequency response data, is proposed for defect classification. The 67 porcelain insulators are secured as specimens from 154 kV transmission towers installed in various parts of Korea; their main materials are cristobalite and alumina. In these specimens, it is observed that the three types of damage, such as porcelain damage, cap damage, and internal damage, are those that are typically found in actual sites. Accordingly, the use of two eigenvectors (moments of real value and moments of imaginary value) considerably aids in the analysis of principal components. With the frequency response data, the material and damage types are found to be distinguishable. The classification accuracy is increased by including the third largest eigenvector (area of real value) in three-dimensional analysis. By employing frequency response data, the PCA method provides useful information for assessing the integrity of porcelain insulators; it may be used as basis for future machine learning applications. Principal component analysis ; Robustness (evolution) ; Eigenvalues and eigenvectors ; Frequency response ; Structural engineering ; Materials science ; Computer science ; Forensic engineering ; Acoustics ; Engineering ; Artificial intelligence ; Electrical engineering ; Physics High voltage insulation and dielectric phenomena ; Structural Health Monitoring Techniques ; Non-Destructive Testing Techniques
Authors: Salem O. Baarimah | Naziha Al-Aidroos | Khaled Saeed Ba-Jaalah
Source:  (article, 2019-12-01)
Field: Chemistry — Analytical Chemistry

Using Chemical Composition of Crude Oil and Artificial Intelligence Techniques to Predict the Reservoir Fluid Properties This study presents Artificial Neural Network (ANN) and Fuzzy Logic (FL) techniques to predict of some important reservoir fluid properties, like, bubble point pressure (Pb), oil formation volume factor at bubble point pressure (βo at Pb), and solution gas oil ratio at bubble point pressure (Rs at Pb) using chemical composition of crude oil. The presented models here were established using 1500 data points, collected from mainly unpublished sources. Statistical analysis was conducted to see which of the Artificial Intelligence techniques (AI) were more reliable and accurate for predicting the reservoir fluid properties. The new (FL) models outperformed most of the (ANN) models. The presented models provide good estimation for (Pb), βo at Pb, and Rs at Pb with correlation coefficient (R2) of 0.995, 0.991, and 0.998, respectively. Bubble point ; Artificial neural network ; Petroleum engineering ; Bubble ; Crude oil ; Correlation coefficient ; Compositional data ; Computer science ; Artificial intelligence ; Geology ; Machine learning Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Fault Detection and Control Systems
Authors: Fariba Safa | Asghar Alizadehdakhel | Shahab Shariati
Source: Russian Journal of Physical Chemistry A (article, 2019-12-01)
Field: Chemistry — Organic Chemistry

Predictive Artificial Neural Network Model for Solvation Enthalpy of Organic Compounds in N,N-Dimethylformamide A quantitative structure-property relationship (QSPR) strategy was followed for prediction of the solvation enthalpy (ΔHsolv) values for 116 organic compounds in N,N-dimethylformamide. At first, a three-parameter multiple linear regression (MLR) model including the hydrophilic factor (Hy), sum of Kier–Hall electrotopological states (Ss) and 3D-MoRSE signal 15 weighted by atomic van der waals volumes (Mor15v) was generated for the enthalpy data. The model showed a standard error of 5.49 and R2 = 0.9220. The descriptors were then employed to develop an artificial neural network (ANN) model for estimating the solvation enthalpies. The developed ANN with 3-6-1 topology resulted in the R2 values of 0.9914, 0.9765, and 0.9796 for the training, validation and test sets, respectively. Relative importances of Ss, Mor15v and Hy were found to be 48.86, 32.08, and 19.06%, respectively. The findings proved the significant role of molecular topology, electron density and hydrophilicity as the structural features determining ΔHsolv values of the organic compounds in N,N-dimethylformamide. Solvation ; Enthalpy ; Artificial neural network ; Thermodynamics ; Chemistry ; Dimethylformamide ; Materials science ; Organic chemistry ; Computer science ; Artificial intelligence ; Physics ; Solvent Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Zhaocheng Liu | Dayu Zhu | Kyu‐Tae Lee | Andrew S. Kim | Lakshmi Raju | Wenshan Cai
Source: arXiv (Cornell University) (preprint, 2019-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compounding meta-atoms into meta-molecules with hybrid artificial intelligence techniques Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, meta-molecules consisting of multiple meta-atoms possess emerging features that the meta-atoms themselves do not possess. Metasurfaces composed of meta-molecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multi-element systems impede an effective strategy for the design and optimization of meta-molecules. Here, we propose a hybrid artificial intelligence-based framework consolidating compositional pattern-producing networks and cooperative coevolution to resolve the inverse design of meta-molecules in metasurfaces. The framework breaks the design of the meta-molecules into separate designs of meta-atoms, and independently solves the smaller design tasks of the meta-atoms through deep learning and evolutionary algorithms. We leverage the proposed framework to design metallic meta-molecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large-scale metasurfaces in a labor-saving, systematic manner. Computer science ; Leverage (statistics) ; Artificial intelligence ; Nanotechnology ; Materials science Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Photonic Crystals and Applications
Authors: William John Thrift | Cuong Q. Nguyen | Junlin Wang | Jason E. Kahn | Ruijun Dong | Andrew Benjamin Laird | Regina Ragan
Source:  (article, 2019-09-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Improved regressions with convolutional neural networks for surface enhanced Raman scattering sensing of metabolite biomarkers Surface enhanced Raman scattering (SERS) is a vibrational spectroscopy method that enables the quantification of the concentration of small molecules. SERS sensing has been demonstrated in a wide variety of applications, from explosive and drug detection, to monitoring of bacteria growth. Underpinning SERS sensing are the sensor surfaces that are composed of vast quantities of metal nanostructures which confine light into small gaps called “hotspots”, enhancing Raman scattering. While these surfaces are essential for increasing Raman scattering intensity so that analyte signal may be observed in small concentrations, they introduce signal variations due to spatial distributions of Raman enhancement and hotspot volume. In this work, we introduce a convolutional neural network model that improves concentration regressions in SERS sensors by learning the distributions of sensor surface dependent latent variables. We demonstrate that this model significantly improves predictions compared to a traditional multilayer perceptron approach, and that the model uses analyte spectral information and is capable of reasonable interpolations. Raman scattering ; Analyte ; Materials science ; Convolutional neural network ; Scattering ; Raman spectroscopy ; Biological system ; Computer science ; Optics ; Chemistry ; Physics ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Xianfei Huang | Lin‐Qiang Mo | Guan Huang | Guang‐Ping Yuan
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

How Did the Response Surface Methodology Optimized Reaction Conditions Influence and Enhance the Catalytic Performance of Nanoprous Chitosan Immobilized Cobalt Porphyrinate How was the catalytic performance of the title catalyst influenced and enhanced by the Response Surface Methodology optimized-reaction conditions? Based on our experimental data, the effects of the Response Surface Methodology optimized reaction conditions on the catalytic performance were investigated in this paper. The experimental results show that, the micro alterations of various reaction parameter values resulted in the micro changes of cyclohexane conversion, further causing the positive and negative effects, and resulting in the synergy or the antagonism to cyclohexane conversion. The statistic study via Response Surface Methodology showed that, (1) the size order of the effects of the parameters on cyclohexane conversion was that, the quadratic terms > the mutual interaction terms ≈ the single variable terms; (2) compared to Traditional Optimization Method, Response Surface Methodology could more quickly offer the precise optimum values of reaction conditions. In the five recycle applications of the title catalyst, on average, the catalytic activity and the catalytic efficiency were respectively increased 50% and 25% than those obtained from the Traditional Optimization Method-optimized reaction conditions; (3) compared to the other similar catalysts reported in literatures, the present catalyst was a biomimetic catalyst with the highest Turnover Frequency value (6.5 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ). Response surface methodology ; Catalysis ; Cyclohexane ; Materials science ; Chemistry ; Chitosan ; Chemical engineering ; Biological system ; Computer science ; Organic chemistry ; Chromatography ; Engineering Nanomaterials for catalytic reactions ; Surface Chemistry and Catalysis ; Dendrimers and Hyperbranched Polymers
Authors: Chad M. Folkman | Seo Hyoung Chang | Hyoungjeen Jeen | Edith Perret | P. M. Baldo | Carol Thompson | J. A. Eastman | Ho Nyung Lee | Dillon D. Fong
Source: APL Materials (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

<i>In operando</i> studies of CO oxidation on epitaxial SrCoO2.5+δ thin films The high abundance and fast kinetics of select transition metal oxide catalysts are attractive features for many chemical and electrochemical device applications. However, the activity of such catalysts can be accompanied by phase instabilities that prevent their widespread usage. Furthermore, complexities associated with variations in phase behavior and oxygen stoichiometry have hindered studies on the true origins of catalytic activity. Here, we explore the interactions between activity, phase stability, and microstructure using in operando synchrotron X-ray techniques and gas chromatography/mass spectroscopy (GCMS) to probe the behavior of model SrCoO2.5+δ (SCO) catalysts. Pulsed laser deposition was used to prepare SCO thin films on (001) SrTiO3, (111) SrTiO3, and pseudocubic (001) DyScO3 substrates. The GCMS catalytic measurements were performed with a custom-built microreactor compatible with a synchrotron X-ray diffractometer at the Advanced Photon Source. The activity for carbon monoxide oxidation was determined as a function of temperature from 500 °C to 800 °C. We show that the SrCoO2.5+δ films are active for CO oxidation, most likely by direct reaction with lattice oxygen; consequently, the activity was observed to increase as the films become less stable, with the most active film being the one exhibiting the lowest surface and crystal quality. All films decompose at high temperatures, with in operando diffraction indicating the gradual formation of Sr-rich hexagonal and CoO phases. We find that real-time studies of model oxide systems with synchrotron X-rays is a powerful means of gaining insight into the varied processes taking place at catalytic surfaces. Materials science ; Catalysis ; Carbon monoxide ; Synchrotron ; Oxide ; Thin film ; Diffractometer ; Stoichiometry ; Epitaxy ; Chemical engineering ; Analytical Chemistry (journal) ; Crystal structure ; Crystallography ; Nanotechnology ; Physical chemistry ; Chemistry ; Optics ; Organic chemistry Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides ; Electrocatalysts for Energy Conversion
Authors: Irmak Sargin | Scott P. Beckman
Source: Scientific Reports (article, 2019-02-07)
Field: Materials Science — Materials Chemistry

A data-informatics method to quantitatively represent ternary eutectic microstructures Many of the useful properties of modern engineering materials are determined by the material's microstructure. Controlling the microstructure requires an understanding of the complex dynamics underlying its evolution during processing. Investigating the thermal and mass transport phenomena responsible for a structure requires establishing a common language to quantitatively represent the microstructures being examined. Although such a common language exists for some of the simple structures, which has allowed these materials to be engineered, there has yet to be a method to represent complex systems, such as the ternary microstructures, which are important for many technologies. Here we show how stereological and data science methods can be combined to quantitatively represent ternary eutectic microstructures relative to a set of exemplars that span the stereological attribute space. Our method uniquely describes ternary eutectic microstructures, allowing images from different studies, with different compositions and processing histories, to be quantitatively compared. By overcoming this long-standing challenge, it becomes possible to begin to make progress toward a quantitatively predictive theory of ternary eutectic growth. We anticipate that the method of quantifying instances of an object relative to a set of exemplars spanning attribute-space will be broadly applied to classify materials structures, and may also find uses in other fields. Computer science ; Informatics ; Ternary operation ; Eutectic system ; Microstructure ; Data mining ; Materials science ; Metallurgy ; Engineering Machine Learning in Materials Science ; Geochemistry and Geologic Mapping
Authors: Omer Al Haiqi | Abdurahman Hamid Nour | Rushdi Bargaa | Bamidele Victor Ayodele
Source: BULLETIN OF CHEMICAL REACTION ENGINEERING AND CATALYSIS (article, 2019-11-05)
Field: Chemistry — Analytical Chemistry

Effect of Process Parameters on the Photocatalytic Degradation of Phenol in Oilfield Produced Wastewater using ZnO/Fe2O3 Nanocomposites. The upstream processing of crude oil is often associated with the presence of phenolic compounds when not properly treated could result in adverse effects on human health. The objective of the study was to investigate the effect of process parameters on the photocatalytic degradation of phenol. The ZnO/Fe2O3 nanocomposite photocatalyst was prepared by sol-gel method and characterized using various instrument techniques. The characterized ZnO/Fe2O3 nanocomposite displayed suitable physicochemical properties for the photocatalytic reaction. The ZnO/Fe2O3 nanocomposite was employed for the phenol degradation in a cylindrical batch reactor under solar radiation. The photocatalytic runs show that calcination temperature of the ZnO/Fe2O3 nanocomposite, catalyst loading, initial phenol concentration and pH of the wastewater significantly influence the photocatalytic degradation of phenol. After 180 min of solar radiation, the highest phenol degradation of 92.7% was obtained using the ZnO/Fe2O3 photocatalyst calcined at 400 ºC. This study has demonstrated that phenol degradation is significantly influenced by parameters such as calcination temperature of the ZnO/Fe2O3 nanocomposite, catalyst loading, initial phenol concentration and pH of the wastewater resulting in highest phenol degradation using the ZnO/Fe2O3 nanocomposite calcined at 400 ºC, initial phenol concentration of 0.5 mg/L, catalyst loading of 3 mg/L and pH of 3. Copyright © 2020 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0). Calcination ; Photocatalysis ; Phenol ; Nanocomposite ; Degradation (telecommunications) ; Catalysis ; Wastewater ; Nuclear chemistry ; Chemical engineering ; Materials science ; Chemistry ; Waste management ; Organic chemistry ; Nanotechnology Petroleum Processing and Analysis ; Water Quality Monitoring and Analysis ; Enhanced Oil Recovery Techniques
Authors: Amir Samimi | Soroush Zarinabadi | Amir Hossein Shahbazi Kotanaei | Alireza Azimi | Masoumeh Mirzaei
Source: Eurasian Chemical Communications (article, 2019-12-29)
Field: Chemistry — Analytical Chemistry

Use of data mining in the corrosion classification of pipelines in catalytic reforming units (CRU) Nowadays, computational tools for analyzing and collecting data in the operation of petroleum units are essential. One of the methods is the classification or regression is to step in the overall process of knowledge extraction. In this article, one of specific type of decision called the conditional contract arrangement, is used to extract the relevant knowledge in Catalytic Reforming Units (CRU) for 4 factors: Density, PH, total iron ions in vessels (S.FE) and H2S. All of these factors are related to corrosion in CRU units and this paper aims to optimize some conditions to eliminate it. In this regard, using ammonium water with a specific range and pH can be helpful. According to the obtained results the best range of density (in Feed) is less than 0.515 kg/m3, PH (water in vessels) is more than 6.7, S.FE is less than 1.5 ppm and H2S in recycle gas is less than 700 ppm. The outcomes also show how this approach can be used to gain insight into some refineries and how to deliver results in a comprehensible and user-friendly way. Cru ; Corrosion ; Process (computing) ; Oil refinery ; Pipeline transport ; Catalysis ; Range (aeronautics) ; Process engineering ; Computer science ; Environmental science ; Chemistry ; Engineering ; Waste management ; Environmental engineering ; Physics ; Organic chemistry ; Meteorology Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques