jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chirag Sedani | Maulik Panchal | Vipul Tanna | Paritosh Chaudhuri | Manoj Kumar Gupta
Source: Case Studies in Thermal Engineering (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Analysis of effective thermal conductivity of pebble bed by artificial neural network and its computational and experimental verification While dealing with a solid-type ceramic breeding blanket for a fusion reactor, it is critical to determine the basic and thermal properties of the functional material in the form of a pebble bed. In the form of pebbles, lithium ceramics serve as the tritium breeder material in the fusion blanket. Effective thermal conductivity (keff) is one of the important thermal properties for the design and useful parameter to determine the performance of the blanket component. Artificial Neural Networks (ANN) are a popular machine learning technique for tuning between input and output parameters. These networks can learn from examples (data set) and apply them when a homogeneous event arises, making them able to work through genuine-time events. Hence, it can save a lot of time and money for doing repetitive experiments and high-end simulations. This will aid in the creation of a huge database on the pebble bed's keff, which will be useful in the design and development of fusion blankets. The findings of simulations and experiments are compared to those predicted by the ANN model for the pebble bed's thermal conductivity. At IPR, a test setup for experiments has been developed using the steady-state and axial heat flow approach. keff of Li2TiO3 has been measured for the pebbles of diameter of 0.8–1.2 mm having packing fraction of ∼62% and using helium environment at different temperatures ranging from 100 °C to 600 °C at constant atmospheric pressure. keff has been compared with pebble bed of stainless steel pebbles of different diameters (1, 2, 3, 1&3, & 2&3 mm) as well. DDPM-DEM model has been used to generate the pebble bed and ANSYS-CFD simulations using FLUENT have been performed to validate the results. The projected values using ANN are within 5% of the results obtained from simulations and experiments. The details of the DDPM-DEM and ANN models, FLUENT simulations, and experimental results will be discussed in this paper. Blanket ; Pebble ; Thermal conductivity ; Nuclear engineering ; Breeder (animal) ; Materials science ; Ceramic ; Helium ; Fusion power ; Thermal ; Process engineering ; Mechanical engineering ; Environmental science ; Composite material ; Thermodynamics ; Nuclear physics ; Physics ; Engineering ; Geology Fusion materials and technologies ; Nuclear reactor physics and engineering ; Nuclear Materials and Properties
Authors: Sk. Mashfiqur Rahman | H. Vahedi Tafreshi | Behnam Pourdeyhimi
Source: Journal of Applied Polymer Science (article, 2022-09-20)
Field: Materials Science — Biomaterials

<scp>Physics‐based</scp> deep neural network model to guide electrospinning polyurethane fibers Abstract Electrospinning is an inexpensive room‐temperature method of producing nanofibers from polymer granules. While it is quite easy to produce electrospun nanofibers, it is very difficult to control properties of the resulting fibers without conducting laborious trial‐and‐error experiments. In this work, a mass‐spring‐damper (MSD) model was used to simulate formation of electrospun fibers for different combinations of electrospinning conditions such as voltage, needle‐to‐collector distance, and polymer concentration. The sparse data from the CPU‐intensive MSD model were then used in developing a deep neural network (DNN) model that could guide the electrospinning experiment toward producing fibers with a desired diameter. The accuracy of the MSD‐DNN hybrid model was examined via comparison with experimental data obtained by electrospinning polyurethane fibers. Electrospinning ; Materials science ; Nanofiber ; Polyurethane ; Polymer ; Composite material ; Artificial neural network ; Computer science ; Artificial intelligence Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Lei Fan | Jiongjiong Xu | Yutao Niu | Zhongwei Liu | Kun Meng | Xiaohua Yu | Ju Rong
Source: Diamond and Related Materials (article, 2022-10-26)
Field: Materials Science — Materials Chemistry

PO-C32: A two-dimensional graphene-based membrane for efficient seawater desalination Desalination ; Membrane ; Materials science ; Adsorption ; Graphene ; Chemical engineering ; Density functional theory ; Nanotechnology ; Nanomaterials ; Seawater ; Chemical physics ; Chemistry ; Computational chemistry ; Geology ; Physical chemistry ; Engineering Graphene research and applications ; Membrane Separation Technologies ; Nanopore and Nanochannel Transport Studies
Authors: Juan Iván Gómez-Peralta | Xim Bokhimi | Nidia G. García-Peña | P. Quintana | Geonel Rodríguez‐Gattorno
Source: Journal of Applied Crystallography (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

Band-gap assessment from X-ray powder diffraction using artificial intelligence X-ray diffraction is a phenomenon that stems from the interaction of the electron density of a crystalline material and the electric field of the X-ray waves. The product of this interaction, the diffraction pattern, provides a picture of the reciprocal space of the atomic distribution in terms of intensities of certain scattering wavevectors. In this manner, a correlation between those intensities seen in a diffraction pattern and the electronic properties of a material is suggested. This correlation, if it exists, may not be directly proposed using analytical expressions. This article shows for the first time the feasibility of assessing the band gap of metal–organic frameworks (MOFs) and organic and inorganic materials from their X-ray powder diffraction pattern. The band gaps were assessed with convolutional neural networks (CNNs). These CNNs were developed using simulated X-ray powder diffraction patterns and the band gaps calculated with density functional theory. The diffraction patterns were simulated with different crystal sizes, from 10 nm to the macrocrystalline size. In addition, the reported band gaps of MOFs and organic compounds in the Quantum MOF Database and the Organic Materials Database data sets were used, which were calculated with the PBE functional. Furthermore, the band gaps calculated by Kim et al. [ Sci. Data (2020), 7 , 387] for inorganic compounds with the HSE functional were used. The developed CNNs were tested with simulated diffraction patterns of compounds different from those used to train the CNNs, as well as with experimentally recorded diffraction patterns. The developed CNNs allowed the assessment of the band gap of the compounds with a root-mean-square error as low as 0.492 eV after training with over 64 000 diffraction patterns. Diffraction ; Reciprocal lattice ; Powder diffraction ; Band gap ; Materials science ; X-ray crystallography ; Scattering ; Crystallography ; Chemistry ; Optics ; Physics ; Optoelectronics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: J. Harry Moore | Matthias R. Bauer | Jeff Guo | Atanas Patronov | Ola Engkvist | Christian Margreitter
Source: ChemRxiv (preprint, 2022-01-31)
Field: Materials Science — Materials Chemistry

Icolos: A workflow manager for structure based post-processing of de novo generated small molecules We present Icolos, a workflow manager written in Python as a tool for automating complex structure-based workflows. Icolos can be used as a standalone tool, for example in virtual screening campaigns, or can be used in conjunction with deep learning-based molecular generation facilitated for example by REINVENT, a previously published de novo design package. In this publication, we focus on the internal structure and general capabilities of Icolos, using docking experiments as an illustration. The source code is freely available at https://github.com/MolecularAI/Icolos under the Apache 2.0 licence. Tutorial notebooks containing minimal working examples can be found at https://github.com/MolecularAI/IcolosCommunity. Workflow ; Python (programming language) ; Computer science ; Source code ; Programming language ; Software engineering ; Focus (optics) ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: An‐Qi Chen | Qiang Cai | Tong Wang | Xiaozhi Wang | Hai-Bo Sun | Ru‐Qin Yu
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Intelligent Analysis of Excitation-Emission Matrix Fluorescence Fingerprint to Identify and Quantify Adulteration in Camellia Oil Based on Machine Learning Fingerprint (computing) ; Fluorescence ; Excitation ; Artificial intelligence ; Matrix (chemical analysis) ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Engineering ; Chromatography ; Physics ; Optics ; Electrical engineering Spectroscopy and Chemometric Analyses
Authors: Kousuke Nishikiori | Kentaro Tanaka | Yoshihiro Uesawa
Source: Molecular Diversity (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis. Quantitative structure–activity relationship ; Drug ; Partial least squares regression ; Artificial neural network ; Regression analysis ; Hemodialysis ; Regression ; Set (abstract data type) ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Medicine ; Statistics ; Pharmacology ; Internal medicine Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Seyed Oveis Mirabootalebi | Gholam Hosien Akbari Fakhrabadi | Reza Mirahmadi Babaheydari
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-01-30)
Field: Materials Science — Materials Chemistry

High-yield Production of Amorphous Carbon via Ball Milling of Graphite and Prediction of Its Crystallite Size through ANN One of the main methods for the synthesis of amorphous and nanostructured carbon is the mechanical milling of graphite. However, calculation and anticipation of the amorphous phase during the mechanical milling of graphite still is a major challenge due to a lot of important parameters. The main aim of this study is to mass-produce amorphous carbon and predict the crystallite size of graphite. For this purpose, ball-milling of graphite powder was carried out at different times of milling. Then, the destruction of crystal structure and changes in phases were studied by XRD, TEM, AFM, SEM, and Zeta Seizer. The results of theMAUD analysis showed that 91% and 93% of the unmilled graphite were converted to amorphous carbon at 250 and 330 hours of ball-milling, respectively. In order to predict the crystallite size of carbon during the high energy ball-milling, the effective variables in the ball-milling process along with the initial crystallite size of carbon were determined as the input of the artificial neural network (ANN). Moreover, the final crystallite size of carbon was considered as the output of the network. The designed network with a root mean square error (RMSE) of 4% was able to predict the crystallite size of carbon during the process. Finally, by comparing the experimental results and the designed model, it was shown that the predicted results were very close to the experimental outcomes. Accordingly, the presented model can be used for predicting the crystallite size of carbon during the mechanical milling of graphite. Ball mill ; Crystallite ; Graphite ; Materials science ; Ball (mathematics) ; Yield (engineering) ; Amorphous carbon ; Amorphous solid ; Metallurgy ; Chemical engineering ; Chemistry ; Crystallography ; Engineering ; Mathematics Graphite, nuclear technology, radiation studies ; Fiber-reinforced polymer composites ; Iron and Steelmaking Processes
Authors: Ignacio Pérez‐Correa | Pablo Giunta | Javier A. Francesconi | Fernando Mariño
Source: The Canadian Journal of Chemical Engineering (article, 2022-11-30)
Field: Chemistry — Organic Chemistry

Artificial neural network for the prediction of physical properties of organic compounds based on the group contribution method Abstract In the development and optimization of chemical processes involving the selection of organic fluids, knowledge of the physical properties of compounds is vital. In many cases, it is complex to find experimental measurements for all substances, so it becomes necessary to have a tool to predict properties based on the characteristics of the molecule. One of the most extensively used methods in the literature is the estimation by contribution of functional groups, where properties are calculated using the constituent elements of the molecule. There are several models published in the literature, but they fail to represent a wide variety of compounds with high accuracy and simultaneously maintain a low computational complexity. The aim of this work is to develop a prediction model for eight thermodynamic properties (melting temperature, boiling temperature, critical pressure, critical temperature, critical volume, enthalpy of vaporization, enthalpy of fusion, and enthalpy of gas formation) based on the group contribution methodology by implementing a multilayer perceptron. Here, 2736 substances were used to train the neural network, whose prediction capacity was compared with other reference models available in the literature. The proposed model presents errors ranging from 1% to 5% for the different properties (except for the melting point), which improves the reference models with errors in the range of 3%–30%. Nevertheless, a difficulty in the prediction of the melting point is detected, which could represent an inherent hindrance to this methodology. Boiling point ; Enthalpy of vaporization ; Artificial neural network ; Enthalpy ; Group contribution method ; Thermodynamics ; Melting point ; Enthalpy of fusion ; Heat capacity ; Computer science ; Acentric factor ; Multilayer perceptron ; Chemistry ; Biological system ; Machine learning ; Organic chemistry ; Physics ; Phase equilibrium Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Oliviero Carugo
Source: Methods in molecular biology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Uses and Abuses of the Atomic Displacement Parameters in Structural Biology Flexibility (engineering) ; Structural biology ; Macromolecule ; Computational biology ; Nanotechnology ; Chemistry ; Biology ; Materials science ; Mathematics ; Biochemistry ; Statistics Enzyme Structure and Function ; Biochemical and Molecular Research ; RNA and protein synthesis mechanisms
Authors: Markus Stricker | Lars Banko | Nik Sarazin | Niklas Siemer | Janssen, Jan | Zhang, Lei | Neugebauer, Jörg | Ludwig, Alfred
Source: arXiv (Cornell University) (preprint, 2022-12-09)
Field: Materials Science — Materials Chemistry

Computationally accelerated experimental materials characterization -- drawing inspiration from high-throughput simulation workflows Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of `machine learning' in materials discovery campaigns. The obvious benefits which include automation, reproducibility, data provenance, and reusability of managed data, however, is not widely available in the experimental domain. We present an implementation of a Active Learning loop with a direct interface to an experimental measurement device in pyiron, a framework designed for high-throughput simulations, as demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided by the active learning approach, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as composition-property predictions from literature mining using correlations in word embeddings. With data from all domains in the same framework, a heretofore untapped and much-needed potential for the acceleration of materials characterization and materials discovery campaigns becomes available. Workflow ; Throughput ; Computer science ; Automation ; Reusability ; Characterization (materials science) ; Process (computing) ; Experimental data ; Data science ; Distributed computing ; Nanotechnology ; Database ; Engineering ; Materials science ; Mechanical engineering ; Programming language Machine Learning in Materials Science ; Scientific Computing and Data Management ; Gaussian Processes and Bayesian Inference
Authors: Khaled Alrfou | Amir Kordijazi | Tian Zhao
Source: arXiv (Cornell University) (preprint, 2022-07-29)
Field: Materials Science — Materials Chemistry

Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives Finding quantitative descriptors representing the microstructural features of a given material is an ongoing research area in the paradigm of Materials-by-Design. Historically, microstructural analysis mostly relies on qualitative descriptions. However, to build a robust and accurate process-structure-properties relationship, which is required for designing new advanced high-performance materials, the extraction of quantitative and meaningful statistical data from the microstructural analysis is a critical step. In recent years, computer vision (CV) methods, especially those which are centered around convolutional neural network (CNN) algorithms have shown promising results for this purpose. This review paper focuses on the state-of-the-art CNN-based techniques that have been applied to various multi-scale microstructural image analysis tasks, including classification, object detection, segmentation, feature extraction, and reconstruction. Additionally, we identified the main challenges with regard to the application of these methods to materials science research. Finally, we discussed some possible future directions of research in this area. In particular, we emphasized the application of transformer-based models and their capabilities to improve the microstructural analysis of materials. Computer science ; Convolutional neural network ; Segmentation ; Artificial intelligence ; Process (computing) ; Feature extraction ; Machine learning ; Pattern recognition (psychology) ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Industrial Vision Systems and Defect Detection
Authors: Otávio Bianchi | Patrícia Bereta Pereira | Carlos A. Ferreira
Source: Polymers (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Mechanochemical Treatment in High-Shear Thermokinetic Mixer as an Alternative for Tire Recycling This publication highlights the use of a high-speed thermokinetic mixer as an alternative to recycling ground tire rubber (GTR) using mechanochemical treatment. The GTR initially had a gelled fraction of 80% and presented a reduction of up to 50% of gel fraction in the most intensive condition (5145 rpm, n<sub>2</sub>). The processing condition at the lowest speed (2564 rpm, n<sub>1</sub>) resulted in greater selectivity in chain scission (<i>K</i>~1). However, in the most intense processing condition (10 min to n<sub>2</sub>), more significant degradation was observed via random scission, reduction in the glass transition temperature, T<sub>g</sub> (11 °C), increase in the soluble polymeric fraction, and a more significant reduction in the density of bonds occurs. The artificial neural network could describe and correlate the thermal degradation profile with the processing conditions and the physicochemical characteristics of the GTR. The n<sub>2</sub> velocity resulted in the formation of particles with a smoother and more continuous surface, which is related to the increase in the amount of soluble phase. The approach presented here represents an alternative to the mechanochemical treatment since it can reduce the crosslink density with selectivity and in short times (1-3 min). Materials science ; Degradation (telecommunications) ; Natural rubber ; Fraction (chemistry) ; Composite material ; Chain scission ; Selectivity ; Bond cleavage ; Chemical engineering ; Shear (geology) ; Phase (matter) ; Polymer ; Chemistry ; Chromatography ; Organic chemistry ; Catalysis ; Computer science Thermal and Kinetic Analysis ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Kangmo Yeo | Karam Park | Sukmin Jeong
Source: Current Applied Physics (article, 2022-04-16)
Field: Materials Science — Materials Chemistry

Neural network approach to diffusion of B and N adatoms on the Pt(111) surface Diffusion ; Surface diffusion ; Surface (topology) ; Materials science ; Artificial neural network ; Chemical physics ; Chemistry ; Adsorption ; Physical chemistry ; Computer science ; Physics ; Thermodynamics ; Mathematics ; Artificial intelligence ; Geometry Machine Learning in Materials Science ; Graphene research and applications ; Semiconductor materials and devices