jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hiroshi Ohno
Source: Soft Computing (article, 2019-05-30)
Field: Materials Science — Materials Chemistry

Auto-encoder-based generative models for data augmentation on regression problems Computer science ; Artificial intelligence ; Benchmark (surveying) ; Machine learning ; Encoder ; Generative model ; Regression ; Generalization ; Artificial neural network ; Regression analysis ; Realization (probability) ; Linear regression ; Generative grammar ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Neural Networks and Applications ; Reservoir Engineering and Simulation Methods
Authors: Alexander W. Kohn | Zhou Lin | Troy Van Voorhis
Source: The Journal of Physical Chemistry C (article, 2019-06-05)
Field: Chemistry — Physical and Theoretical Chemistry

Toward Prediction of Nonradiative Decay Pathways in Organic Compounds I: The Case of Naphthalene Quantum Yields Many emerging technologies depend on our ability to control and manipulate the excited-state properties of molecular systems. These technologies include fluorescent labeling in biomedical imaging, light harvesting in photovoltaics, and electroluminescence in light-emitting devices. All of these systems suffer from nonradiative loss pathways that dissipate electronic energy as heat, which causes the overall system efficiency to be directly linked to the quantum yield (Φ) of the molecular excited state. Unfortunately, Φ is very difficult to predict from the first principles because the description of a slow nonradiative decay mechanism requires an accurate description of long-timescale excited-state quantum dynamics. In the present study, we introduce an efficient semi-empirical method of calculating the fluorescence quantum yield (Φfl) for molecular chromophores, which converts simple electronic energies computed using time-dependent density functional theory into an estimate of Φfl. As with all machine learning strategies, the algorithm needs to be trained on fluorescent dyes for which Φfl’s are known, so as to provide a black-box method which can later predict Φ’s for chemically similar chromophores that have not been studied experimentally. As a first illustration of how our proposed algorithm can be trained, we examine a family of 25 naphthalene derivatives. The simplest application of the energy gap law is found to be inadequate to explain the rates of internal conversion (IC) or intersystem crossing (ISC)—the electronic properties of at least one higher lying electronic state (Sn or Tn) or one far-from-equilibrium geometry are typically needed to obtain accurate results. Indeed, the key descriptors turn out to be the transition state between the Franck–Condon minimum and a distorted local minimum near an S1/S0 conical intersection (which governs IC) and the magnitude of the spin–orbit coupling (which governs ISC). The resulting Φfl’s are predicted with reasonable accuracy (±0.22), making our approach a promising ingredient for high-throughput screening and rational design of the molecular excited states with desired Φ’s. We thus conclude that our model, while semi-empirical in nature, does in fact extract sound physical insight into the challenge of describing nonradiative relaxations. Naphthalene ; Quantum ; Chemistry ; Environmental chemistry ; Photochemistry ; Computational chemistry ; Environmental science ; Organic chemistry ; Physics ; Quantum mechanics Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; Luminescence and Fluorescent Materials
Authors: Shaolong Zhu | Maoni Chao | Jinyu Zhang | Xinjuan Xu | Puwen Song | Jinlong Zhang | Zhongwen Huang
Source: Sensors (article, 2019-11-28)
Field: Chemistry — Analytical Chemistry

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Smoothing ; Principal component analysis ; Support vector machine ; Feature selection ; Mathematics ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sergio Borraz‐Martínez | Joan Simó | Anna Gras | Mariàngela Mestre | Ricard Boqué
Source: Scientific Reports (article, 2019-12-24)
Field: Chemistry — Analytical Chemistry

Multivariate Classification of Prunus dulcis Varieties using Leaves of Nursery Plants and Near-Infrared Spectroscopy The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used. The most important variables to discriminate between these varieties were studied through of three scientifically accepted indicators (Variable importance in projection¸ selectivity ratio and vector of the regression coefficients). The results showed that the 7000 to 4000 cm<sup>-1</sup> range contains the most useful variables, which allowed to decrease the complexity of the data set. Concerning to the classification models, a high percentage of correct classifications (90-100%) was obtained, where dried-powdered leaves showed better results than fresh leaves. However, the classification rate of both kinds of leaves evidences the capacity of the near-infrared spectroscopy to discriminate Prunus dulcis varieties. We demonstrate with these results the capability of the NIRS technology as a quality control tool in nursery plant industry. Prunus dulcis ; Prunus ; Multivariate statistics ; Horticulture ; Mathematics ; Botany ; Biology ; Cultivar ; Statistics Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Michael Adams | Mariana Kozłowska | Nicolò Baroni | Michael Oldenburg | Rui Ma | Dmitry Busko | Andrey Turshatov | Ganapathi Emandi | Mathias O. Senge | Ritesh Haldar | Christof Wöll | G. Ulrich Nienhaus | Bryce S. Richards | Ian A. Howard
Source: ACS Applied Materials & Interfaces (article, 2019-04-02)
Field: Chemistry — Inorganic Chemistry

Highly Efficient One-Dimensional Triplet Exciton Transport in a Palladium–Porphyrin-Based Surface-Anchored Metal–Organic Framework Efficient photon-harvesting materials require easy-to-deposit materials exhibiting good absorption and excited-state transport properties. We demonstrate an organic thin-film material system, a palladium-porphyrin-based surface-anchored metal-organic framework (SURMOF) thin film that meets these requirements. Systematic investigations using transient absorption spectroscopy confirm that triplets are very mobile within single crystalline domains; a detailed analysis reveals a triplet transfer rate on the order of 10<sup>10</sup> s<sup>-1</sup>. The crystalline nature of the SURMOFs also allows a thorough theoretical analysis using the density functional theory. The theoretical results reveal that the intermolecular exciton transfer can be described by a Dexter electron exchange mechanism that is considerably enhanced by virtual charge-transfer exciton intermediates. On the basis of the photophysical results, we predict exciton diffusion lengths on the order of several micrometers in perfectly ordered, single-crystalline SURMOFs. In the presently available samples, strong interactions of excitons with domain boundaries present in these metal-organic thin films limit the diffusion length to the diameter of these two-dimensional grains, which amount to about 100 nm. Our results demonstrate high potential of SURMOFs for light-harvesting applications. Porphyrin ; Palladium ; Materials science ; Exciton ; Surface (topology) ; Metal ; Photochemistry ; Metal-organic framework ; Chemical physics ; Nanotechnology ; Physical chemistry ; Condensed matter physics ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications ; Lanthanide and Transition Metal Complexes
Authors: Christophe Tournassat | Carl I. Steefel
Source: Reviews in Mineralogy and Geochemistry (article, 2019-09-01)
Field: Chemistry — Inorganic Chemistry

Reactive Transport Modeling of Coupled Processes in Nanoporous Media International audience Nanoporous ; Materials science ; Nanotechnology Radioactive element chemistry and processing ; CO2 Sequestration and Geologic Interactions ; Concrete and Cement Materials Research
Authors: Junqing Shi | M. Ángeles Izquierdo | Sangyoon Oh | Soo Young Park | Begoña Milián‐Medina | Daniel Roca‐Sanjuán | Johannes Gierschner
Source: Organic Chemistry Frontiers (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Inverted energy gap law for the nonradiative decay in fluorescent floppy molecules: larger fluorescence quantum yields for smaller energy gaps The non-radiative decay of substituted dicyano-distyrylbenzenes in solution increase with the Franck–Condon energy, being opposite to the conventional energy gap law. Fluorescence ; Chemistry ; Radiative transfer ; Molecule ; Energy (signal processing) ; Atomic physics ; Quantum ; Band gap ; Photochemistry ; Optoelectronics ; Physics ; Optics ; Quantum mechanics Photochemistry and Electron Transfer Studies ; Luminescence and Fluorescent Materials ; Strong Light-Matter Interactions
Authors: Bowen Zheng | Grace X. Gu
Source: Carbon (article, 2019-09-03)
Field: Materials Science — Materials Chemistry

Tuning the graphene mechanical anisotropy via defect engineering Graphene ; Materials science ; Anisotropy ; Nanotechnology ; Electronics ; Monolayer ; Strain engineering ; Optoelectronics ; Electrical engineering ; Silicon ; Optics ; Physics ; Engineering Graphene research and applications ; Graphene and Nanomaterials Applications ; Advanced Materials and Mechanics
Authors: Guru Prakash Neupane | Wendi Ma | Tanju Yildirim | Yilin Tang | Linglong Zhang | Yuerui Lu
Source: Nano Materials Science (article, 2019-10-12)
Field: Materials Science — Polymers and Plastics

2D organic semiconductors, the future of green nanotechnology The discovery of 2D organic semiconductors of atomically thin structures has attracted great attention due to their emerging optical, electronic, optoelectronic and mechatronic properties. Recent progress in such organic nanostructures has opened new opportunities for engineering material properties in many ways, such as, 0D/1D/2D nanoparticles hybridization, strain engineering, atomic doping etc. Moreover, 2D organic nanostructures exhibit a unique feature of bio–functionality and are highly sensitive to bio-analytes. Such peculiar behavior in 2D organics can be utilized to design highly-efficient bio-sensors. Also, a bio-molecular integrated electronic/optoelectronic device with enhanced performance can be attained. Furthermore, the bio-degradable, biocompatible, bio-metabolizable, non-toxic behaviour and natural origin of organic nanomaterials can address the current ecological concerns of increasing inorganic material based electronic waste. This review highlights the benefits of 2D organic semiconductors. Considering the importance of strategic techniques for growing thin 2D organic layers, this review summarizes progress towards this direction. The possible challenges for long-time stability and future research directions in 2D organic nano electronics/optoelectronics are also discussed. We believe that this review article provides immense research interests in organic 2D nanotechnology for exploiting green technologies in the future. Nanotechnology ; Materials science ; Nanomaterials ; Electronics ; Organic electronics ; Organic semiconductor ; Semiconductor ; Transistor ; Engineering ; Optoelectronics ; Electrical engineering Conducting polymers and applications ; Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques
Authors: Marialuisa Piccolo | Gabriella Misso | Maria Grazia Ferraro | Claudia Riccardi | Antonella Capuozzo | Mayra Rachele Zarone | Francesco Μaione | Marco Trifuoggi | Paola Stiuso | Gerardino D’Errico | Michele Caraglia | Luigi Paduano | Daniela Montesarchio | Carlo Irace | Rita Santamaria
Source: Scientific Reports (article, 2019-05-07)
Field: Materials Science — Materials Chemistry

Exploring cellular uptake, accumulation and mechanism of action of a cationic Ru-based nanosystem in human preclinical models of breast cancer According to WHO, breast cancer incidence is increasing so that the search for novel chemotherapeutic options is nowadays an essential requirement to fight neoplasm subtypes. By exploring new effective metal-based chemotherapeutic strategies, many ruthenium complexes have been recently proposed as antitumour drugs, showing ability to impact on diverse cellular targets. In the framework of different molecular pathways leading to cell death in human models of breast cancer, here we demonstrate autophagy involvement behind the antiproliferative action of a ruthenium(III)-complex incorporated into a cationic nanosystem (HoThyRu/DOTAP), proved to be hitherto one of the most effective within the suite of nucleolipidic formulations we have developed for the in vivo transport of anticancer ruthenium(III)-based drugs. Indeed, evidences are implicating autophagy in both cancer development and therapy, and anticancer interventions endowed with the ability to trigger this biological response are currently considered attractive oncotherapeutic approaches. Moreover, crosstalk between apoptosis and autophagy, regulated by finely tuned metallo-chemotherapeutics, may provide novel opportunities for future improvement of cancer treatment. Following this line, our in vitro and in vivo preclinical investigations suggest that an original strategy based on suitable formulations of ruthenium(III)-complexes, inducing sustained cell death, could open new opportunities for breast cancer treatment, including the highly aggressive triple-negative subtype. Mechanism (biology) ; Mechanism of action ; Cancer ; Action (physics) ; Breast cancer ; Human breast ; Cancer research ; Computational biology ; Computer science ; Bioinformatics ; Medicine ; Chemistry ; Biology ; Biochemistry ; Internal medicine ; In vitro Nanoparticles: synthesis and applications ; RNA Interference and Gene Delivery ; Nanoparticle-Based Drug Delivery
Authors: Mohamed M. Habashy | Amr M. Abd‐Elhady | R. A. Elsad | Mohamed A. Izzularab
Source: Applied Nanoscience (article, 2019-01-03)
Field: Materials Science — Materials Chemistry

Performance of PVC/SiO2 nanocomposites under thermal ageing Abstract Accelerated thermal ageing test on nanocomposites of polyvinyl chloride (PVC/SiO 2 ) is conducted at constant temperatures of 110 °C and 140 °C. Accordingly, different PVC/SiO 2 nanocomposite samples with different silicon dioxide (SiO 2 ) filler loading are prepared. The preparation of the samples was carried out based on the solution casting technique. The loading concentrations of SiO 2 in the investigating samples are 0, 1, 2.5, 5 and 7.5 wt%. The breakdown strength of the prepared PVC/SiO 2 nanocomposite samples is evaluated before and after the thermal ageing test. The evaluation of breakdown strength is carried out based on American Society for Testing and Materials (ASTM) standard. Also, dielectric spectroscopy, which includes dielectric constant ( έ ) and tangent loss (tan δ ) of nanocomposite, is performed before and after the thermal ageing test. Also, the mass losses during thermal ageing of all samples are evaluated. The results show that the breakdown strength, tan δ , and the dielectric constant ( έ ) of pure PVC and PVC/SiO 2 nanocomposites are significantly affected by the thermal ageing test. Adding a small amount of SiO 2 nanoparticles to PVC leads to improvement in its dielectric properties before and after the thermal ageing process as compared to the pure PVC. The mass loss of PVC/SiO 2 nanocomposite during thermal ageing is less than that of pure PVC and confirms the obtained result from breakdown strength. Materials science ; Nanocomposite ; Composite material ; Polyvinyl chloride ; Dielectric ; Dissipation factor ; Casting ; Ageing ; Dielectric loss ; Dielectric strength High voltage insulation and dielectric phenomena ; Polymer Nanocomposites and Properties ; Dielectric materials and actuators
Authors: Junmian Zhu | Van‐Quan Vuong | Bobby G. Sumpter | Stephan Irle
Source: MRS Communications (article, 2019-06-28)
Field: Materials Science — Materials Chemistry

Artificial neural network correction for density-functional tight-binding molecular dynamics simulations Tight binding ; Intramolecular force ; Density functional theory ; Molecular dynamics ; Artificial neural network ; Ab initio ; Quantum chemical ; Quantum ; Hydrogen bond ; Materials science ; Computational chemistry ; Energetics ; Binding energy ; Chemical physics ; Molecule ; Electronic structure ; Computer science ; Artificial intelligence ; Physics ; Chemistry ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Michelle J. S. Spencer | David A. Winkler
Source: Advanced Theory and Simulations (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

Predicting Thermal Properties of Crystals Using Machine Learning Abstract Calculating vibrational properties of crystals using quantum mechanical (QM) methods is a challenging problem in computational material science. This problem is solved using complementary machine learning methods that rapidly and reliably recapitulate entropy, specific heat, effective polycrystalline dielectric function, and a non‐vibrational property (band gap) for materials calculated by accurate but lengthy QM methods. The materials are described mathematically using property‐labeled materials fragment descriptors. The machine learning models predict the QM properties with root mean square errors of 0.31 meV per atom per K for entropy, 0.18 meV per atom per K for specific heat, 4.41 for the trace of the dielectric tensor, and 0.5 eV for band gap. These models are sufficiently accurate to allow rapid screening of large numbers of crystal structures to accelerate material discovery. Band gap ; Dielectric ; Atom (system on chip) ; Materials science ; Entropy (arrow of time) ; Thermal ; Crystal (programming language) ; Quantum ; Crystallite ; Crystal structure ; Statistical physics ; Algorithm ; Computational physics ; Condensed matter physics ; Computer science ; Thermodynamics ; Physics ; Chemistry ; Quantum mechanics ; Crystallography ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Atsushi Ishikawa | Keitaro Sodeyama | Yasuhiko Igarashi | Tomofumi Nakayama | Yoshitaka Tateyama | Masato Okada
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents We combined a data science-driven method with quantum chemistry calculations, and applied it to the battery electrolyte problem. We performed quantum chemistry calculations on the coordination energy (Ecoord) of five alkali metal ions (Li, Na, K, Rb, and Cs) to electrolyte solvent, which is intimately related to ion transfer at the electrolyte/electrode interface. Three regression methods, namely, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), were employed to find the relationship between Ecoord and descriptors. Descriptors include both ion and solvent properties, such as the radius of metal ions or the atomic charge of solvent molecules. Our results clearly indicate that the ionic radius and atomic charge of the oxygen atom that is connected to the metal ion are the most important descriptors. Good prediction accuracy for Ecoord of 0.127 eV was obtained using ES-LiR, meaning that we can predict Ecoord for any alkali ion without performing quantum chemistry calculations for ion-solvent pairs. Further improvement in the prediction accuracy was made by applying the exhaustive search with Gaussian process, which yields 0.016 eV for the prediction accuracy of Ecoord. Chemistry ; Electrolyte ; Alkali metal ; Ion ; Ionic radius ; Quantum chemistry ; Atom (system on chip) ; Battery (electricity) ; Computational chemistry ; Physical chemistry ; Thermodynamics ; Electrochemistry ; Electrode ; Physics ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials