jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sampsa Vierros | Maria Sammalkorpi
Source: ACS Omega (article, 2019-09-13)
Field: Chemistry — Organic Chemistry

Hybrid Atomistic and Coarse-Grained Model for Surfactants in Apolar Solvents Here, we develop and verify the performance of a hybrid molecular modeling approach that combines coarse-grained apolar solvent and atomistic solute or polar solvent description, for example, for description of reverse micellar systems. The coarse-grained solvent model is directly applicable to organic solvents encompassing alkane, alkene, and fatty acid ester functional groups and connects directly to both standard united-atom GROMOS 53A6 and all-atom CHARMM27 force fields, as well as the atomistic detail water models compatible with these force fields. The different levels of description are coupled via explicit, unscaled electrostatics, and scaled mixing rules for dispersive interactions. The hybrid model is in near-quantitative agreement with fully atomistic simulations when combined with the CHARMM27 model but underestimates modestly surfactant aggregation when using GROMOS 53A6 united-atom description. The use of truncated electrostatics affords up to a 9-fold increase in computational speed without significant loss of accuracy. However, long-range electrostatic calculations and load imbalance at high core counts can significantly degrade the performance. We demonstrate the usability of the hybrid model by assessing the reverse micelle formation of a homologous series of nonionic glycerolipids via large-scale self-assembly simulations. The presented model is demonstrated here for accurate description of surfactant systems in apolar solvents, with and without also polar solvent (water) in the system. The formulation can be expected to describe well also other solute species or interfaces with an apolar solvent in an apolar environment. Electrostatics ; Polar ; Molecular dynamics ; Solvent ; Solvent models ; Chemistry ; Water model ; Alkane ; Pulmonary surfactant ; Micelle ; Atom (system on chip) ; Chemical physics ; Solvation ; Thermodynamics ; Computational chemistry ; Hydrocarbon ; Aqueous solution ; Organic chemistry ; Physical chemistry ; Computer science ; Physics Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Lipid Membrane Structure and Behavior
Authors: Diego Pasquier | Oleg V. Yazyev
Source: Physical review. B./Physical review. B (article, 2019-11-15)
Field: Materials Science — Materials Chemistry

Unified picture of lattice instabilities in metallic transition metal dichalcogenides Transition metal dichalcogenides (TMDs) in the $1T$ polymorph are subject to a rich variety of periodic lattice distortions, often referred to as charge-density waves (CDWs) when not too strong. We study from first principles the fermiology and phonon dispersion of three representative single-layer transition metal disulfides with different occupation of the ${t}_{2g}$ subshell: ${\mathrm{TaS}}_{2}\phantom{\rule{4pt}{0ex}}({t}_{2g}^{1}),{\mathrm{WS}}_{2}\phantom{\rule{4pt}{0ex}}({t}_{2g}^{2})$, and ${\mathrm{ReS}}_{2}\phantom{\rule{4pt}{0ex}}({t}_{2g}^{3})$ across a broad range of doping levels. While strong electron-phonon interactions are at the heart of these instabilities, we argue that away from half-filling of the ${t}_{2g}$ subshell, the doping dependence of the calculated CDW wave vector can be explained from simple fermiology arguments, so that a weak-coupling nesting picture is a useful starting point for understanding. On the other hand, when the ${t}_{2g}$ subshell is closer to half-filling, we show that nesting is irrelevant, while a real-space strong-coupling picture of bonding Wannier functions is more appropriate and simple bond-counting arguments apply. Our study thus provides a unifying picture of lattice distortions in $1T$ TMDs that bridges the two regimes, while the crossover between these regimes can be attained by tuning the filling of the ${t}_{2g}$ orbitals. Transition metal ; Condensed matter physics ; Lattice (music) ; Materials science ; Metal ; Physics ; Chemistry ; Metallurgy 2D Materials and Applications ; Organic and Molecular Conductors Research ; Graphene research and applications
Authors: Subrat Kumar Jena | Snehashish Chakraverty
Source: Curved and Layered Structures (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Dynamic Analysis of Single-Layered Graphene Nano-Ribbons (SLGNRs) with Variable Cross-Section Resting on Elastic Foundation Abstract This article deals with free vibration of the variable cross-section (non-uniform) single-layered graphene nano-ribbons (SLGNRs) resting on Winkler elastic foundation using the Differential Quadrature Method (DQM). Here characteristic width of the cross-section is varied exponentially along the length of the nano-ribbon while the thickness of the cross section is kept constant. Euler–Bernoulli beam theory in conjunction with Eringen nonlocal elasticity theory is considered in this study. The numerical as well as graphical results are reported by using MATLAB codes developed by authors. Convergence of present method is explored and our results are compared with known results available in literature showing excellent agreement. Further, effects various parameters on frequency parameters are studied comprehensively. Vibration ; Ribbon ; Graphene ; Nyström method ; Elasticity (physics) ; Mathematical analysis ; Materials science ; Cross section (physics) ; Nano- ; Timoshenko beam theory ; Euler's formula ; Deflection (physics) ; Mathematics ; Structural engineering ; Physics ; Composite material ; Classical mechanics ; Boundary value problem ; Nanotechnology ; Acoustics ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Sihua Wang | Youlong Wu
Source: Chinese Journal of Electrical Engineering (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Effect of salty fog on flashover characteristics of OCS composite insulators To study the flashover characteristics of the fog water in saline-alkali areas for high-speed railway OSC insulators, the spatial distributions of both the potential and electric field of soiled insulators in the presence of salty fog are analyzed using COMSOL Multiphysics, a multi-physics coupling software. Furthermore, to analyze the effect of the contamination of insulators due to salty fog water , a saline solution is used for staining an insulator sample by using the solid coating method. In an artificial climate chamber, the salty fog environment is simulated, and the flashover voltage for different salty fog water conductivities and surface staining are obtained. The salt-density correction coefficient K is also proposed. It provides a strong basis for the selection of railway insulators in saline-alkali areas. The results show that the salty fog water changes the original surface equivalent salt density, ρESDD, by wetting the existing fouling layer on the surface of the insulator, so that the original surface contamination layer becomes equivalent to Kρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESDD</sub> ; the smaller is the value of ρESDD of the insulator surface, the larger is the value of K; the surface of the same insulator is dirty, and as the concentration of salty fog-water increases, K also increases. Insulator (electricity) ; Arc flash ; Wetting ; Materials science ; Contamination ; Environmental science ; Composite material ; Fouling ; Saline water ; Salinity ; Chemistry ; Geology High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Mojtaba Haghighatlari | Johannes Hachmann
Source: Current Opinion in Chemical Engineering (preprint, 2019-03-01)
Field: Materials Science — Materials Chemistry

Advances of machine learning in molecular modeling and simulation Workflow ; Computer science ; Mainstream ; Context (archaeology) ; Artificial intelligence ; Software deployment ; Data science ; Machine learning ; Algorithmic learning theory ; Active learning (machine learning) ; Management science ; Software engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Trevor A. Profitt | Jason K. Pearson
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A shared-weight neural network architecture for predicting molecular properties Quantum chemical methods scale poorly with increasing molecular size and machine learning models have emerged as a promising, computationally-efficient alternative. We present a shared-weight neural network architecture based on modified atom-centered symmetry functions (ACSFs) and show that it performs similarly to the more computationally expensive per-element neural networks of previous work with ACSFs. The model achieves chemically accurate predictions, with a mean absolute error as low as 0.63 kcal mol-1 on energy predictions in the QM9 data set. Additionally, we show that it can reliably predict atomic forces. Artificial neural network ; Architecture ; Computer science ; Computer architecture ; Artificial intelligence ; Biological system ; Chemistry ; Biology ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Dipta B. Ghosh | Bijaya B. Karki | Jianwei Wang
Source: Journal of Nuclear Materials (article, 2019-12-18)
Field: Materials Science — Materials Chemistry

Utilization of Artificial Neural Network to explore the compositional space of hollandite-structured materials for radionuclide Cs incorporation Hollandite ; Chemistry ; Artificial neural network ; Mineralogy ; Computer science ; Artificial intelligence Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Radioactive element chemistry and processing
Authors: Ping Yuan | Syu-Fang Liu
Source: International Journal of Hydrogen Energy (article, 2019-11-17)
Field: Materials Science — Materials Chemistry

Transient analysis of a solid oxide fuel cell unit with reforming and water-shift reaction and the building of neural network model for rapid prediction in electrical and thermal performance Transient (computer programming) ; Materials science ; Volumetric flow rate ; Water-gas shift reaction ; Current (fluid) ; Current density ; Nuclear engineering ; Solid oxide fuel cell ; Operating temperature ; Steady state (chemistry) ; Thermodynamics ; Reaction rate ; Flow (mathematics) ; Mechanics ; Chemical engineering ; Catalysis ; Chemistry ; Computer science ; Physical chemistry ; Anode ; Engineering ; Physics Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Xinchen Ni | Nathan K. Fritz | Brian L. Wardle
Source: MRS Advances (article, 2019-09-16)
Field: Materials Science — Surfaces, Coatings and Films

In Situ Testing Using Synchrotron Radiation Computed Tomography in Materials Research Materials science ; In situ ; Synchrotron radiation ; Synchrotron ; Microstructure ; Visualization ; Nuclear engineering ; Medical physics ; Metallurgy ; Optics ; Computer science ; Artificial intelligence Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Shogo Fukushima | Eisaku Ushijima | Hiroyuki Kumazoe | Akihide Koura | Fuyuki Shimojo | Kohei Shimamura | Masaaki Misawa | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: Physical review. B./Physical review. B (article, 2019-12-09)
Field: Materials Science — Materials Chemistry

Thermodynamic integration by neural network potentials based on first-principles dynamic calculations Simulation-size effect in evaluating the melting temperature of material is studied systematically by combining thermodynamic integration (TI) based on first-principles molecular-dynamics (FPMD) simulations and machine learning. Since the numerical integration to determine the free energies of two different phases as a function of temperature is very time consuming, the FPMD-based TI method has only been applied to small systems, i.e., less than 100 atoms. To accelerate the numerical integration, we here construct an interatomic potential based on the artificial neural-network (ANN) method, which retains the first-principles accuracy at a significantly lower computational cost. The free energies of the solid and liquid phases of rubidium are accurately obtained by the ANN potential, where its weight parameters are optimized to reproduce FPMD results. The ANN results reveal a significant size dependence up to 500 atoms. Thermodynamic integration ; Artificial neural network ; Molecular dynamics ; Rubidium ; Materials science ; Statistical physics ; Function (biology) ; Numerical integration ; Interatomic potential ; Computer science ; Thermodynamics ; Physics ; Machine learning ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Parvaneh Shabanzadeh | Rubiyah Yusof | Kamyar Shameli | Abdollah Hajalilou | Shidrokh Goudarzi
Source: Journal of Nanomaterials (article, 2019-01-14)
Field: Materials Science — Materials Chemistry

Computational Modeling of Biosynthesized Gold Nanoparticles in Black <i>Camellia sinensis</i> Leaf Extract In this research, gold nanoparticles (Au-NPs) are biosynthesized from tetrachloroaurate (AuCl 4 − ) aqueous solution through a simple and ecofriendly route using water extract of black Camellia sinensis leaf ( C. sinensis L.) which acted as a reductant and stabilizer simultaneously. The prepared gold nanoparticles are characterized using UV-visible spectroscopy, X-ray diffraction (XRD), and transmission electron microscopy (TEM). Also, determination of the accurate predictor model for chemical reactions is particularly important because of high cost of the chemical materials and measurement devices. While the artificial neural networks (ANNs) are one of the appropriate tools to forecast any phenomena, due to the low number of data set related to chemical experimental was caused to provide appropriate model is a time-consuming iterative process. With the aim to improve the accuracy of the ANN model and overcome the local convergence of this problem, a global search technique, biogeography-based optimization (BBO) method which integrated by chaotic map is employed. The improved model showed minimum mean squared error (MSE) of 0.0134 and maximum coefficient of determination (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>) equal to 0.9822 compared with several other famous ANN training algorithm, utilizing output experimental data obtained from biosynthesis proceeding. Materials science ; Camellia sinensis ; Artificial neural network ; Nanoparticle ; Convergence (economics) ; Aqueous solution ; Mean squared error ; Biological system ; Algorithm ; Computer science ; Artificial intelligence ; Nanotechnology ; Physical chemistry ; Chemistry ; Mathematics ; Statistics Nanoparticles: synthesis and applications
Authors: Jannis van Kersbergen | Farhad Ghazvinian Zanjani | Svitlana Zinger | Fons van der Sommen | Benjamin Balluff | Naomi R. N. Vos | Shane R. Ellis | Ron M.A. Heeran | Marit Lucas | Henk A. Marquering | Ilaria Jansen | C. Dilara Savci‐Heijink | Daniël M. de Bruin | Peter H. N. de With
Source:  (article, 2019-03-18)
Field: Chemistry — Spectroscopy

Cancer detection in mass spectrometry imaging data by dilated convolutional neural networks Imaging mass spectrometry (IMS) is a novel molecular imaging technique to investigate how molecules are distributed between tumors and within tumor region in order to shed light into tumor biology or find potential biomarkers. Convolutional neural networks (CNNs) have proven to be very potent classifiers often outperforming other machine learning algorithms, especially in computational pathology. To overcome the challenge of complexity and high-dimensionality of the IMS data, the proposed CNNs are either very deep or use large kernels, which results in large amount of parameters and therefore a high computational complexity. An alternative is down-sampling the data, which inherently leads to a loss of information. In this paper, we propose using dilated CNNs as a possible solution to this challenge, since it allows for an increase of the receptive field size, neither by increasing the network parameters nor by decreasing the input signal resolution. Since the mass signature of cancer biomarkers are distributed over the whole mass spectrum, both locally- and globally-distributed patterns need to be captured to correctly classify the spectrum. By experiment, we show that employing dilated convolutions in the architecture of a CNN leads to a higher performance in tumor classification. Our proposed model outperforms the state-of-the-art for tumor classification in both clinical lung and bladder datasets by 1-3%. Computer science ; Convolutional neural network ; Artificial intelligence ; Mass spectrometry imaging ; Pattern recognition (psychology) ; Computational complexity theory ; Curse of dimensionality ; Deep learning ; Mass spectrometry ; Algorithm Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Anca-Narcisa Neagu
Source: Advances in experimental medicine and biology (review, 2019-01-01)
Field: Chemistry — Spectroscopy

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology Mass spectrometry imaging ; Biology ; Proteomics ; Proteome ; Computational biology ; Transcriptome ; Molecular imaging ; Carcinogenesis ; Cell biology ; Bioinformatics ; Chemistry ; Biochemistry ; Gene expression ; Mass spectrometry ; Genetics ; Gene Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Bholanath Maity | Yury Minenkov | Luigi Cavallo
Source: The Journal of Chemical Physics (article, 2019-07-02)
Field: Materials Science — Materials Chemistry

Evaluation of experimental alkali metal ion–ligand noncovalent bond strengths with DLPNO-CCSD(T) method We applied the domain based local pair natural orbital coupled cluster approach with single, double, and perturbative triple excitations, DLPNO-CCSD(T), to rationalize more than 130 experimental bond dissociation enthalpies collected in the work of Rodgers and Armentrout [Chem. Rev. 116, 5642-5687 (2016)] and involving alkali metal cations and versatile neutral organic and inorganic ligands ranging from common solvents to amino acids. In general, a remarkable agreement has been obtained between predicted and experimental alkali metal ion-ligand noncovalent bond strengths, highlighting a high degree of reliability of data assembled by Rodgers and Armentrout. In the case of some inconsistent experimental data given for some species, we pointed to a number for which best agreement with DLPNO-CCSD(T) calculations has been achieved. In addition, we refined a couple of ΔH<sub>0</sub> for which DLPNO-CCSD(T) values turned out to be significantly different from their experimental counterparts. We suggest an application of the DLPNO-CCSD(T) to derive the reference values to train/validate force field and neural network methods to be further applied in molecular dynamic simulations to unravel the mechanisms in biological systems and alkali metal ion batteries. Alkali metal ; Coupled cluster ; Chemistry ; Dissociation (chemistry) ; Natural bond orbital ; Computational chemistry ; Cluster (spacecraft) ; Ion ; Chemical physics ; Atomic physics ; Physical chemistry ; Density functional theory ; Molecule ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Zayyana Nurthohari | Muhammad Ary Murti | Casi Setianingsih
Source:  (article, 2019-11-01)
Field: Chemistry — Organic Chemistry

Wood Quality Classification Based on Texture and Fiber Pattern Recognition using HOG Feature and SVM Classifier Wood as a material for household appliance needs to be considered of quality. Quality of wood can be classified according to colours, texture, and wood fibre pattern differences. In general, wood industries have been doing the wood quality classified process using a conventional method with a sense of vision in which the results are subjective in terms of accuracy and time efficiency. Machine Learning is a solution to this problem of predicting and classifying data of wood quality. In this paper, the wood will be recognized using Histogram of Oriented Gradient to know the pattern and texture. Meanwhile, the classification method uses Support Vector Machine which will be compared to find the best accuracy and time computation. This system is given image input with five types of cedar classification such as Class A, Class B, Class C, Class D, and Class E took using Logitech C930e HD which is integrated with Arduino Uno for object detection process and conveyor. The Experiment achieve 90% of accuracy with time computation 1,40 s. Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Histogram ; Computer science ; Classifier (UML) ; Computation ; Contextual image classification ; Histogram of oriented gradients ; Texture (cosmology) ; Feature extraction ; Class (philosophy) ; Computer vision ; Machine learning ; Image (mathematics) Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Gaoqiang Lv | Du Changwen | Fei Ma | Yazhen Shen | Jianmin Zhou
Source: Plant Disease (article, 2019-09-25)
Field: Chemistry — Analytical Chemistry

Responses of Leaf Cuticles to Rice Blast: Detection and Identification Using Depth-Profiling Fourier Transform Mid-Infrared Photoacoustic Spectroscopy Cuticle is the first barrier for rice to resist blast fungus on the surface of the leaf. Studies on how the rice leaf cuticle responds to rice blast and attempts to perform early detection of rice blast are limited, and these two issues were explored in this study via depth-profiling Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS). Rice leaves with four different scales of injury (healthy leaves as CK, asymptomatic leaves from mildly diseased seedlings as S1, infected leaves with fewer than five lesions as S2, and infected leaves with more than 10 lesions as S3) were scanned by three moving mirror velocities 0.32, 0.47, and 0.63 cm/s for the depth profiling of the rice leaf surface. The response patterns were acquired via chemometrics to analyze the variations of the chemical group absorptions in the different layers of a sample and in the same layer between different samples. Results showed that the leaf cuticle tended to be thicker and the relative content of fatty alcohols and cutin, unsaturated compounds, and aromatics in the cuticle increased when rice seedlings were infected by blast fungus. Together with the principal component analysis, the probabilistic neural network was applied to identify the samples in early stages (CK and S1), which reached an accuracy of 90% for the samples in the greenhouse and 82% for the samples in the field. Thus, depth-profiling FTIR-PAS was good at analyzing the variation in cuticle layers and showed great potential in the early detection of rice blast or other diseases in different species. Cutin ; Plant cuticle ; Cuticle (hair) ; Biology ; Fourier transform infrared spectroscopy ; Botany ; Photoacoustic spectroscopy ; Fungus ; Lignin ; Horticulture ; Photoacoustic imaging in biomedicine ; Biochemistry ; Anatomy ; Optics Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Plant Surface Properties and Treatments