jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingyuan Xu | Tong Zhu | John Z. H. Zhang
Source: The Journal of Physical Chemistry A (article, 2019-07-11)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Simulation of Zinc Ion in Water with an ab Initio Based Neural Network Potential An artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. Here, we develop an ab initio based neural network potential (NN/MM-RESP) to perform molecular dynamics study of zinc ion in liquid water. In this approach, the interaction energy, atomic forces, and atomic charges of zinc ion and water molecules in the first solvent shell are described by a neural network potential trained with energies and forces generated from density functional calculations. The predicted energies and forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we carried out molecular dynamics simulation to study the hydration of zinc ion in water. The experimentally observed zinc-water radial distribution function, as well as the X-ray absorption near edge structure spectrum, is well-reproduced by the MD simulation. Comparison of the results with other theoretical calculations is provided, and important features of the present approach are discussed. The neural network approach used in this study can be applied to construct potentials to study solvation of other metal ions, and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other environments such as proteins. Molecular dynamics ; Solvation ; Chemistry ; Ab initio ; Ion ; Solvation shell ; Chemical physics ; Radial distribution function ; Zinc ; Metal ions in aqueous solution ; Molecule ; Computational chemistry ; Atomic physics ; Physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Biruk Abreha | Snigdha Agarwal | Ian Foster | Ben Blaiszik | Steven A. Lopez
Source: The Journal of Physical Chemistry Letters (article, 2019-10-23)
Field: Materials Science — Materials Chemistry

Virtual Excited State Reference for the Discovery of Electronic Materials Database: An Open-Access Resource for Ground and Excited State Properties of Organic Molecules This letter announces the Virtual Excited State Reference for the Discovery of Electronic Materials Database (<i>VERDE materials DB</i>), the first database to include downloadable excited-state structures (S<sub>0</sub>, S<sub>1</sub>, T<sub>1</sub>) and photophysical properties. <i>VERDE materials DB</i> is searchable, open-access via www.verdedb.org , and focused on light-responsive π-conjugated organic molecules with applications in green chemistry, organic solar cells, and organic redox flow batteries. It includes results of our active and past virtual screening studies; to date, more than 13 000 density functional theory (DFT) calculations have been performed on 1 500 molecules to obtain frontier molecular orbitals and photophysical properties, including excitation energies, dipole moments, and redox potentials. To improve community access, we have made <i>VERDE materials DB</i> available via an integration with the Materials Data Facility. We are leveraging <i>VERDE materials DB</i> to train machine learning algorithms to identify new materials and structure-property relationships between molecular ground- and excited-states. We present a case-study involving photoaffinity labels, including predictions of new diazirine-based photoaffinity labels anticipated to have high photostabilities. Excited state ; Resource (disambiguation) ; State (computer science) ; Organic molecules ; Computer science ; Materials science ; Ground state ; Molecule ; Database ; Atomic physics ; Physics ; Quantum mechanics ; Computer network Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Dominik Steinberger | Hengxu Song | Stefan Sandfeld
Source: Frontiers in Materials (article, 2019-06-18)
Field: Materials Science — Metals and Alloys

Machine Learning-Based Classification of Dislocation Microstructures Dislocations -- the carrier of plastic deformation -- are responsible for a wide range of mechanical properties of metals or semiconductors. Those line-like objects tend to form complex networks that are very difficult to characterize or to link to macroscopic properties on the specimen scale. In this work a machine learning based approach for classification of coarse-grained dislocation microstructures in terms of different dislocation density field variables is used. The performance of the learning algorithm combined with domain knowledge from the underlying physics helps to shed light on the interplay between coarse-graining voxel size and the set of suitable or even required density variables for a faithful microstructure characterization. Dislocation ; Microstructure ; Materials science ; Deformation (meteorology) ; Characterization (materials science) ; Granularity ; Semiconductor ; Artificial intelligence ; Domain (mathematical analysis) ; Computer science ; Statistical physics ; Machine learning ; Nanotechnology ; Physics ; Composite material ; Mathematics ; Optoelectronics ; Mathematical analysis Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: Sangwon Lee | Baekjun Kim | Jihan Kim
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Predicting performance limits of methane gas storage in zeolites with an artificial neural network Crystalline nanoporous materials (<italic>i.e.</italic> shapes) were generated in the energy space using an artificial neural network. Methane ; Artificial neural network ; Environmental science ; Process engineering ; Computer science ; Artificial intelligence ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Covalent Organic Framework Applications
Authors: Stefan Heisel | Janine Ernst | Arne Emshoff | Gerhard Schembecker | Kerstin Wohlgemuth
Source: Powder Technology (article, 2019-01-08)
Field: Materials Science — Materials Chemistry

Shape-independent particle classification for discrimination of single crystals and agglomerates Agglomerate ; Economies of agglomeration ; Classifier (UML) ; Biological system ; Artificial intelligence ; Materials science ; Particle (ecology) ; Particle size ; Mineralogy ; Pattern recognition (psychology) ; Computer science ; Chemical physics ; Chemistry ; Chemical engineering ; Engineering ; Composite material ; Geology Crystallization and Solubility Studies ; Mineral Processing and Grinding ; Minerals Flotation and Separation Techniques
Authors: Mazaher Ahmadi | Janis Timoshenko | Farzad Behafarid | Beatriz Roldán Cuenya
Source: The Journal of Physical Chemistry C (article, 2019-03-27)
Field: Materials Science — Surfaces, Coatings and Films

Tuning the Structure of Pt Nanoparticles through Support Interactions: An in Situ Polarized X-ray Absorption Study Coupled with Atomistic Simulations Interactions of nanoparticles (NPs) with their environment may have a pronounced effect on their structure and shape as well as on their functionality in applications such as catalysis. It is therefore crucial to disentangle the particle-adsorbate and particle-support interaction effects on the particle shape, its local structure, atomic dynamics, and its possible anisotropies. In order to gain insight into the support effect, we carried out an X-ray absorption fine-structure spectroscopy (XAFS) investigation of adsorbate- and ligand-free size-selected Pt NPs deposited on two different supports in ultrahigh vacuum. Polarization-dependent XAFS measurements, neural network-based analysis of X-ray absorption near-edge structure data, and reverse Monte Carlo (RMC) simulations of extended X-ray absorption fine structure (EXAFS) were used to resolve the 3D shape of the NPs and details of their local structure. A synergetic combination of advanced in situ XAFS analysis with atomic force microscopy and scanning tunneling microscopy (STM) imaging provides uniquely detailed information about the particle-support interactions and the NP/support buried interface, not accessible to any experimental technique, when considered alone. In particular, our combined approach reveals differences in the structure of Pt NPs deposited on TiO<sub>2</sub>(110) and SiO<sub>2</sub>/Si(111). Pt NPs on SiO<sub>2</sub> assume a spherical-like 3D shape and weakly interact with the support. In contrast, the effective shape of analogously synthesized Pt NPs on TiO<sub>2</sub>(110) after annealing at 600 °C is found to be a truncated octahedron with (100) top and interfacial facets that are encapsulated by the TiO<sub>2</sub> support. Modeling disorder effects in these NPs using an RMC approach reveals differences in bond-length distributions for NPs on different supports and allows us to analyze their anisotropy, which may be crucial for the interpretation of support-dependent atomic dynamics and can have an impact on the understanding of the catalytic properties of these NPs. X-ray absorption fine structure ; Extended X-ray absorption fine structure ; Nanoparticle ; Reverse Monte Carlo ; Chemical physics ; Materials science ; X-ray absorption spectroscopy ; Anisotropy ; Crystallography ; Absorption (acoustics) ; Particle (ecology) ; Scanning tunneling microscope ; Nanotechnology ; Spectroscopy ; Absorption spectroscopy ; Chemistry ; Crystal structure ; Optics ; Physics ; Composite material Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Iron oxide chemistry and applications
Authors: Xiang Tian | Genwei Zhang | Zhu Zou | Zhibo Yang
Source: Analytical Chemistry (article, 2019-04-05)
Field: Chemistry — Spectroscopy

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields. Metabolomics ; Chemistry ; Spheroid ; Mass spectrometry imaging ; Computational biology ; Mass spectrometry ; Drug discovery ; In vitro ; Chromatography ; Biochemistry ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Rebecca Han | Krista S. Walton | David S. Sholl
Source: Annual Review of Chemical and Biomolecular Engineering (review, 2019-03-27)
Field: Materials Science — Materials Chemistry

Does Chemical Engineering Research Have a Reproducibility Problem? Concerns have been raised in multiple scientific fields in recent years about the reproducibility of published results. Systematic efforts to examine this issue have been undertaken in biomedicine and psychology, but less is known about this important issue in the materials-oriented research that underpins much of modern chemical engineering. Here, we relate a dramatic historical episode from our own institution to illustrate the implications of performing reproducible research and describe two case studies based on literature analysis to provide concrete information on the reproducibility of modern materials-oriented research. The two case studies deal with the properties of metal-organic frameworks (MOFs), a class of materials that have generated tens of thousands of papers. We do not claim that research on MOFs is less (or more) reproducible than other subfields; rather, we argue that the characteristics of this subfield are common to many areas of materials-oriented research. We conclude with specific recommendations for action by individual researchers, journal editors, publishers, and research communities. Biomedicine ; Data science ; Engineering ethics ; Computer science ; Institution ; Management science ; Nanotechnology ; Social science ; Sociology ; Engineering ; Materials science ; Biology ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Meta-analysis and systematic reviews
Authors: Abdullah F. Al Naim | Huda Alfannakh | S. S. Arafat | S. S. Ibrahim
Source: Science and Engineering of Composite Materials (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

Characterization of PVC/MWCNTs Nanocomposite: Solvent Blend Abstract Polyvinyl Vinyl Chloride (PVC) multiwall carbon nanotubes (MWCNTs) nanocomposite flexible films were prepared using the solvent blend technique. Chloroform (CHCl 3 ) and tetrahydrofuran ((CH 2 ) 4 O) were used as solvents for MWCNTs and PVC, respectively. The effect of the solvents’ blend on electrical, optical and thermal properties of PVC/MWCNTs were investigated. The results of the Raman spectrum showed that all the characteristic bands of PVC polymer have a slight shift due to addition of MWCNTs. Electrical results showed that the nanocomposite samples with chloroform volume ratios of 10% and 25% had nearly the same conductivity. This is attributed to the formation of the MWCNTs network, which assisted in electrical conductivity. The I-V hysteresis curve decreases as the temperature increases and as it approaches the glass transition temperature. The non-isothermal kinetics analysis for PVC and PVC/MWCNTs were investigated by Thermogravimetry Analysis (TGA) using the model-free kinetic method. The non-isothermal measurements were carried out at five heating rates of 5 to 40 ∘ C/min. The results show that the main decomposition process has constant apparent activation energies for all samples. The use of the bi-solvent method has improved the dispersion of untreated MWCNTs, and this has been reflected on the stability of both electrical and thermal properties. Materials science ; Nanocomposite ; Carbon nanotube ; Polyvinyl chloride ; Thermal stability ; Thermogravimetric analysis ; Thermogravimetry ; Thermal decomposition ; Composite material ; Glass transition ; Solvent ; Vinyl chloride ; Isothermal process ; Raman spectroscopy ; Tetrahydrofuran ; Polymer ; Chemical engineering ; Organic chemistry ; Chemistry ; Copolymer Conducting polymers and applications ; Polymer Nanocomposite Synthesis and Irradiation ; Carbon Nanotubes in Composites
Authors: Kamyar Mehrabi | Detlef Günther | Alexander Gundlach‐Graham
Source: Environmental Science Nano (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Single-particle ICP-TOFMS with online microdroplet calibration for the simultaneous quantification of diverse nanoparticles in complex matrices Online microdroplet calibration is used to determine the mass and particle number concentration of inorganic nanoparticles (NPs) without the use of NP standards. This approach can be applied directly to quantify NPs in environmental matrices. Nanoparticle ; Calibration ; Particle (ecology) ; Materials science ; Nanotechnology ; Biological system ; Physics Nanoparticles: synthesis and applications ; Analytical chemistry methods development ; Electrochemical Analysis and Applications
Authors: Ruiqing Zhou | Juanjuan Jin | Qing-Mian Li | Zhen-Zhu Su | Xinjie Yu | Yu Tang | Shaoming Luo | Yong He | Xiaoli Li
Source: Frontiers in Plant Science (article, 2019-01-15)
Field: Chemistry — Analytical Chemistry

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of <i>Magnaporthe oryzae</i>-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host-pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management. Hyperspectral imaging ; Principal component analysis ; Magnaporthe ; Biology ; Lesion ; Visualization ; Linear discriminant analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Pathology ; Oryza sativa ; Medicine Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Linsheng Huang | Zhaochuan Wu | Wenjiang Huang | Huiqin Ma | Jinling Zhao
Source: Applied Sciences (article, 2019-09-17)
Field: Chemistry — Analytical Chemistry

Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine Fusarium head blight (FHB) is one of the diseases caused by fungal infection of winter wheat (Triticum aestivum), which is an important cause of wheat yield loss. It produces the deoxynivalenol (DON) toxin, which is harmful to human and animal health. In this paper, a total of 89 samples were collected from FHB endemic areas. The occurrence of FHB is completely natural in experimental fields. Non-imaging hyperspectral data were first processed by spectral standardization. Spectral features of the first-order derivatives, the spectral absorption features of the continuum removal, and vegetation indices were used to evaluate the ability to identify FHB. Then, the spectral feature sets, which were sensitive to FHB and have significant differences between classes, were extracted from the front, side, and erect angles of winter wheat ear, respectively. Finally, Fisher’s linear discriminant analysis (FLDA) for dimensionality reduction and a support vector machine (SVM) based on radical basis function (RBF) were used to construct an effective identification model for disease severity at front, side, and erect angles. Among selected features, the first-order derivative features of SDg/SDb and (SDg-SDb)/(SDg+SDb) were most dominant in the model produced for the three angles. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB; (2) the accuracy of the FLDA model for the side, front, and erect angles was 77.1%, 85.7%, and 62.9%. The overall accuracy of the SVM (80.0%, 82.9%, 65.7%) was slightly better than FLDA, but the effect was not obvious; (3) LDA combined with SVM can effectively improve the overall accuracy, user’s accuracy, and producer’s accuracy of the model for the three angles. The over accuracy of the side (88.6%) was better than the front (85.7%), while the over accuracy of the erect angle was the lowest (68.6%). Linear discriminant analysis ; Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Fusarium ; Dimensionality reduction ; Computer science ; Biology ; Horticulture Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Plant Pathogens and Fungal Diseases
Authors: Swarnava Ghosh | Amartya Banerjee | Phanish Suryanarayana
Source: Physical review. B./Physical review. B (article, 2019-09-20)
Field: Materials Science — Materials Chemistry

Symmetry-adapted real-space density functional theory for cylindrical geometries: Application to large group-IV nanotubes We present a symmetry-adapted real-space formulation of Kohn-Sham density functional theory for cylindrical geometries and apply it to the study of large X (X=C, Si, Ge, Sn) nanotubes. Specifically, starting from the Kohn-Sham equations posed on all of space, we reduce the problem to the fundamental domain by incorporating cyclic and periodic symmetries present in the angular and axial directions of the cylinder, respectively. We develop a high-order finite-difference parallel implementation of this formulation, and verify its accuracy against established plane-wave and real-space codes. Using this implementation, we study the band structure and bending properties of X nanotubes and Xene sheets, respectively. Specifically, we first show that zigzag and armchair X nanotubes with radii in the range 1 to $5\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$ are semiconducting, other than the armchair and zigzag type III carbon variants, for which we find a vanishingly small bandgap, indicative of metallic behavior. In particular, we find an inverse linear dependence of the bandgap with respect to the radius for all nanotubes, other than the armchair and zigzag type III carbon variants, for which we find an inverse quadratic dependence. Next, we exploit the connection between cyclic symmetry and uniform bending deformations to calculate the bending moduli of Xene sheets in both zigzag and armchair directions, while considering radii of curvature up to $5\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$. We find Kirchhoff-Love type bending behavior for all sheets, with graphene and stanene possessing the largest and smallest moduli, respectively. In addition, other than graphene, the sheets demonstrate significant anisotropy, with larger bending moduli along the armchair direction. Finally, we demonstrate that the proposed approach has very good parallel scaling and is highly efficient, enabling ab initio simulations of unprecedented size for systems with a high degree of cyclic symmetry. In particular, we show that even micron-sized nanotubes can be simulated with modest computational effort. Overall, the current work opens an avenue for the efficient ab initio study of 1D nanostructures with large radii as well as 1D/2D nanostructures under uniform bending. Zigzag ; Graphene ; Density functional theory ; Bending ; Carbon nanotube ; Symmetry (geometry) ; Physics ; Materials science ; Anisotropy ; Inverse ; Moduli space ; Condensed matter physics ; Geometry ; Quantum mechanics ; Nanotechnology ; Mathematics ; Composite material Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites