jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Roman Mysiuk | Iryna Mysiuk | Volodymyr Yuzevych | Grzegorz Pawlowski | Zaklad Handlowo-Uslugowy BHP
Source: Path of Science (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Determining the Place of Depressurization of Underground Pipelines (Gas Pipelines): New Solutions in Industry based on Thermal Image Analysis Using Computer Vision An analysis of the analytical ratios of the mathematical model, which characterizes the development processes of a corrosion cavern on the surface of an underground metal pipeline, which is placed in the environment of moist soil with an electrolyte solution, is performed. A neural network method for estimating the main informative parameters for determining the place of gas depressurization on the surface of an underground pipe and an expression for calculating the change in gas pressure around a crack after its formation have been developed. The principles of determining the limit values of the parameters of the “pipe-cathodic protection” system are formulated, considering the metal's quality and strength criteria at the top of the cavern. Depressurization causes fluid to flow from the pipeline to the surface. Thermal imaging devices make it possible to detect the place of damage to the pipeline based on the temperature properties of the surrounding objects. Thermal imaging can be used to analyze the location of a fluid leak or warn of it using computer vision. Thus, preventing an accident or even a catastrophe in the pipeline. In the work, the colour gamuts of the thermal image in the places of depressurization are considered, and the regularities of detecting damaged sections of the pipeline are established. Cabin pressurization ; Pipeline transport ; Thermal ; Petroleum engineering ; Image (mathematics) ; Geology ; Computer science ; Mechanical engineering ; Engineering ; Computer vision ; Thermodynamics ; Physics Material Properties and Failure Mechanisms ; Industrial Engineering and Technologies ; Engineering Diagnostics and Reliability
Authors: Elisabeth Moser | Simon Jobst | Rudolf Bierl | F. Jenko
Source: Vibrational Spectroscopy (article, 2022-07-18)
Field: Chemistry — Spectroscopy

A deep learning system to transform cross-section spectra to varying environmental conditions Cross section (physics) ; Absorption (acoustics) ; Computer science ; Absorption cross section ; Atom (system on chip) ; Line (geometry) ; Transfer (computing) ; Transfer line ; Deep learning ; Cross-validation ; Artificial intelligence ; Optics ; Mechanical engineering ; Physics ; Mathematics ; Engineering Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: S. Verga | M. Compare | Enrico Zio | G. Carra | M. Farina | I. Righetto | Valerio Della Sala
Source:  (preprint, 2022-09-13)
Field: Chemistry — Analytical Chemistry

Estimation Of The Lower Heating Value Of Solid Recovered Fuel Based On Swir Hyper-Spectral Images And Machine Learning In this work, we apply Machine Learning techniques to Hyper-Spectral Images acquired by a Short Wave Infra-Red (SWIR) Camera, to classify the materials composing the Solid Recovered Fuel (SRF). This classification, enabled by data pre-processing techniques, is used to estimate the Lower Heat Value (LHV) of SRF samples, building on models of the literature. The accurate and timely estimates of SRF LHVs yield significant benefits to SRF consumers. Heat of combustion ; Computer science ; Artificial intelligence ; Wood processing ; Work (physics) ; Machine learning ; Process engineering ; Remote sensing ; Computer vision ; Pattern recognition (psychology) ; Environmental science ; Engineering ; Mechanical engineering ; Chemistry ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Shriram Chennakesavalu | David J. Toomer | Grant M. Rotskoff
Source: arXiv (Cornell University) (preprint, 2022-10-14)
Field: Materials Science — Materials Chemistry

Ensuring thermodynamic consistency with invertible coarse-graining Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insight by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared to atomistic models. Designing ``good'' coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We show that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide. Granularity ; Computer science ; Invertible matrix ; Observable ; Representation (politics) ; Phase space ; Statistical physics ; Degrees of freedom (physics and chemistry) ; Theoretical computer science ; Algorithm ; Mathematics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Surface Chemistry and Catalysis
Authors: Nabin Giri | Jianlin Cheng
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-05-29)
Field: Materials Science — Materials Chemistry

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge Abstract Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline ( DeepProLigand ) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps.This results demonstrate that the deep learning bioinformatics approach is a promising direction to model protein-ligand interaction on cryo-EM data using prior structural information. The source code, data, and instruction to reproduce the results are available on GitHub repository : https://github.com/jianlin-cheng/DeepProLigand Protein ligand ; Leverage (statistics) ; Computer science ; Template ; Ligand (biochemistry) ; Deep learning ; Computational biology ; Protein–ligand docking ; Docking (animal) ; Artificial intelligence ; Drug discovery ; Function (biology) ; Structural bioinformatics ; Protein structure ; Bioinformatics ; Chemistry ; Biology ; Virtual screening ; Biochemistry ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Ignatius Ebo-Quansah | Ahmed H. Hassanin | Tadaharu Adachi | Mohsen A. Hassan
Source: International Journal of Materials Technology and Innovation (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

An overview on penetration mechanism and optimization techniques of steel sandwich structure under ballistic testing This review reports on the current trends in steel-based sandwich structures subjected under high velocity impact. The review begins with a brief introduction on sandwich structures in general. It then delves into detail on some structural configuration parameters that affect sandwich performance. Light has been thrown on the energy absorption mechanisms of sandwich panels. The current global demand on energy necessitates the design of light weight structures. Therefore, the review also elucidates on sandwich design optimization techniques mostly employed by researchers in meeting design constraints and objectives at the minimum weight and cost. Among these optimization techniques are, artificial neural network, fuzzy logic, Taguchi based method, response surface method, particle swarm optimization, genetic algorithm among others. Promising potential of auxetic materials with their negative poison’s ratio in resisting impact penetration has been discussed. Comprehensive explanation on failure mechanisms that are encountered during projectile penetration has also been elaborated upon. Parameters such as projectile geometry, core design, core material and thickness of face sheets noted to have enormous influence on penetration resistance have also been addressed. Mechanism (biology) ; Penetration (warfare) ; Materials science ; Structural engineering ; Engineering ; Physics ; Operations research High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Electromagnetic Launch and Propulsion Technology
Authors: M. Mathumathi | T. Vetriselvi | P. Matheswaran | R. Nandhini | A Arul Selvi
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Silver nano-particles' size prediction using deep learning model with green method Convolutional neural network ; Deep learning ; Particle size ; Computer science ; Nano- ; Materials science ; Artificial intelligence ; Artificial neural network ; Process (computing) ; Silver Nano ; Nanotechnology ; Process engineering ; Biological system ; Engineering ; Chemical engineering ; Composite material Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles ; Healthcare and Environmental Waste Management
Authors: Matthew Beauregard Smith | Zack B. Simpson | Edward M. Marcotte
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-09-26)
Field: Chemistry — Spectroscopy

Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier Abstract We present a machine learning-based interpretive framework ( whatprot ) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion [1] [2]. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot’s hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates. Classifier (UML) ; Hidden Markov model ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Bayesian probability ; Machine learning ; Naive Bayes classifier ; Data mining ; Support vector machine Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; RNA and protein synthesis mechanisms
Authors: Viktor C. Birschitzky | Florian Ellinger | Ulrike Diebold | Michele Reticcioli | Cesare Franchini
Source: arXiv (Cornell University) (preprint, 2022-02-02)
Field: Materials Science — Materials Chemistry

Machine Learning for Exploring Small Polaron Configurational Space Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining the spatial distribution of small polarons is essential to understand materials properties and functionalities. This requires an exploration of the configurational space, which is computationally demanding when using standard first principles methods, and technically prohibitive for many-polaron systems. Here, we propose a machine-learning (ML) accelerated search that compares the energy stability of different polaron patterns and determines the ground state configuration. The kernel-regression based ML model is trained on databases generated by density functional theory (DFT) calculations on a minimal set of initial polaron patterns, obtained by using either molecular dynamics simulations or a random sampling approach. To establish an efficient mapping between training data and configuration stability we designed simple descriptors that model the interactions among polarons and charged point defects. The proposed DFT+ML protocol is used here to explore millions of polaron configurations for two different systems, oxygen defective rutile TiO$_2$(110) and Nb-doped SrTiO$_3$(001). Our data shows that the ML-aided search correctly individuates the ground-state polaron patterns, proposes polaronic configurations not visited in the training and can be used to efficiently determine the optimal distribution of polarons at any charge concentration. Polaron ; Stability (learning theory) ; Computer science ; Kernel (algebra) ; Charge (physics) ; Density functional theory ; Materials science ; Physics ; Statistical physics ; Computational chemistry ; Machine learning ; Chemistry ; Mathematics ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Geochemistry and Geologic Mapping
Authors: Qi Zhang | Yangtianze Tao | Bin Tang | Jin Zhou | Haiyan Wang | Jianmei Wang | Ying Gao | Jianxin Yang | Linhong Ji | Shuangshou Li
Source: ACS Sustainable Chemistry & Engineering (article, 2022-06-22)
Field: Chemistry — Inorganic Chemistry

Enhanced Aggregation-Induced Emission Activity of Metal–Organic Frameworks by Using Machine Learning Technology Metal–organic frameworks (MOFs) with aggregation-induced emission (AIE) activity show a high emission intensity, high sensitivity, and high resolution in biological imaging and identification technologies. However, their AIE activity is controlled by various Eu precursors’ components and synthesis process parameters, and traditional research methods are hard to deal with these complex multiple parameter systems. In this work, we utilize two machine learning technologies to optimize the synthesis process parameters of Eu-MOFs and analyze their synthesis mechanism. First, we choose gradient boosting decision tree (GBDT) regression as the best fitting model. Second, on the basis of the SHapley Additive exPlanation (SHAP) calculation method with the PL/UV intensity ratio regarded as the evaluation standard, we demonstrate that the Eu-precursor concentration (1.91 × 107) and synthesis time (1.73 × 107) dominate in the synthesis systems. Meanwhile, these two parameters show synergic and antagonistic effects on the PL/UV intensity ratio, respectively. Finally, we employ a greedy random walk method to work out that “142-0.83-4.1” should be the best optimization process parameters, and the corresponding sample shows a high photoluminescence quantum yield (PLQY) with a value of 7.65% in the solid state. More importantly, the screen-printed pattern exhibits bright red fluorescence under UV light. Aggregation-induced emission ; Metal-organic framework ; Nanotechnology ; Chemistry ; Materials science ; Computer science ; Chemical engineering ; Engineering ; Organic chemistry ; Fluorescence ; Adsorption ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Electrochemical Analysis and Applications ; Machine Learning in Materials Science
Authors: Bohayra Mortazavi | Fazel Shojaei | Masoud Shahrokhi
Source: Applied Physics Letters (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Comment on “Erratum: ‘Two-dimensional porous graphitic carbon nitride C6N7 monolayer: First-principles calculations’ [Appl. Phys. Lett. <b>119</b>, 142102 (2021)]” [Appl. Phys. Lett. <b>120</b>, 189901 (2022)] Recently, Bafekry et al. [Appl. Phys. Lett. 120, 189901 (2022)] reported their density functional theory (DFT) results on the elastic constants of C6N7 monolayer. They predicted non-zero elastic constants along the out-of-plane direction for a single-layered material, which contradicts with basic physics of the stiffness tensor for plane stress condition. Moreover, in their work Young's modulus is erroneously calculated. On the basis of DFT calculations, herein we predicted the C11, C12 and C66 of the C6N7 monolayer to be 286, 73 and 107 GPa, respectively, equivalent with an in-plane Youngs modulus of 267 GPa. Using DFT calculations and a machine learning interatomic potential, we also show that C6N7 monolayer shows isotropic elasticity. Monolayer ; Graphitic carbon nitride ; Materials science ; Condensed matter physics ; Porosity ; Physics ; Chemistry ; Nanotechnology ; Composite material ; Organic chemistry ; Catalysis Graphene research and applications ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Iman Peivaste | Nima H. Siboni | Ghasem Alahyarizadeh | Reza Ghaderi | Bob Svendsen | Dierk Raabe | Jaber Rezaei Mianroodi
Source: arXiv (Cornell University) (preprint, 2022-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerating phase-field-based simulation via machine learning Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they suffer from the drawback of being computationally very costly when applied to large, complex systems. To reduce such computational costs, a Unet-based artificial neural network is developed as a surrogate model in the current work. Training input for this network is obtained from the results of the numerical solution of initial-boundary-value problems (IBVPs) based on the Fan-Chen model for grain microstructure evolution. In particular, about 250 different simulations with varying initial order parameters are carried out and 200 frames of the time evolution of the phase fields are stored for each simulation. The network is trained with 90% of this data, taking the $i$-th frame of a simulation, i.e. order parameter field, as input, and producing the $(i+1)$-th frame as the output. Evaluation of the network is carried out with a test dataset consisting of 2200 microstructures based on different configurations than originally used for training. The trained network is applied recursively on initial order parameters to calculate the time evolution of the phase fields. The results are compared to the ones obtained from the conventional numerical solution in terms of the errors in order parameters and the system's free energy. The resulting order parameter error averaged over all points and all simulation cases is 0.005 and the relative error in the total free energy in all simulation boxes does not exceed 1%. Artificial neural network ; Computer science ; Computer simulation ; Field (mathematics) ; Phase (matter) ; Algorithm ; Approximation error ; Frame (networking) ; Energy (signal processing) ; Applied mathematics ; Statistical physics ; Simulation ; Artificial intelligence ; Mathematics ; Physics ; Statistics Magnetic Properties and Applications ; Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties
Authors: Ran Tao | Qi Guo
Source: Journal of Environmental and Public Health (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft The application of artificial intelligence (AI) technology in the field of clothes can provide a good development mode and system under the social context of AI technology development. AI provides help for the development of intelligent clothing. Intelligent clothing is a high-tech product that integrates intelligent technology and clothing. It combines cutting-edge technologies in electronic information technology, sensor technology, textile science, and material science. In the extraction and analysis of environmental factors in clothing handicraft, AI technology has a considerable application prospect and a certain development potential. In order to improve the accuracy of environmental factors extraction in clothing handicraft, this paper uses convolutional neural network (CNN) to extract and analyze environmental factors in traditional clothing handicraft. We carried out experiments on the extraction of environmental factors in clothing handicrafts with pure color, few patterns, patterns, and complex background. The experimental results show that the CNN has a good effect on the extraction of environmental factors in clothing handicraft under different backgrounds. In addition, the model in this paper has good stability, accuracy, and feature extraction speed, which has high practical value and research significance. Clothing ; Handicraft ; Computer science ; Convolutional neural network ; Context (archaeology) ; Artificial intelligence ; Architectural engineering ; Engineering ; Geography Textile materials and evaluations