jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. Takenaka | Daigo Asai | Ryoichi Kaizu | Yosuke Mizuno | Yasunori Yokoyama | Yoshihiko Okamoto | Naoyuki Katayama | Hiroyuki Suzuki | Y. Imanaka
Source: Scientific Reports (article, 2019-01-09)
Field: Materials Science — Materials Chemistry

Giant isotropic negative thermal expansion in Y-doped samarium monosulfides by intra-atomic charge transfer Stimulated by strong demand for thermal expansion control from advanced modern industries, various giant negative thermal expansion (NTE) materials have been developed during the last decade. Nevertheless, most such materials exhibit anisotropic thermal expansion in the crystal lattice. Therefore, strains and cracks induced during repeated thermal cycling degrade their performance as thermal-expansion compensators. Here we achieved giant isotropic NTE with volume change exceeding 3%, up to 4.1%, via control of the electronic configuration in Sm atoms of SmS, (4 f)<sup>6</sup> or (4 f)<sup>5</sup>(5d)<sup>1</sup>, by partial replacement of Sm with Y. Contrary to NTE originating from cooperative phenomena such as magnetism, the present NTE attributable to the intra-atomic phenomenon avoids the size effect of NTE and therefore provides us with fine-grained thermal-expansion compensators, which are strongly desired to control thermal expansion of microregions such as underfill of a three-dimensional integrated circuit. Volume control of lanthanide monosulfides via tuning of the 4 f electronic configuration presents avenues for novel mechanical functions of a material, such as a volume-change driven actuator by an electrical field, which has a different drive principle from those of conventional strain-driven actuators such as piezostrictive or magnetostrictive materials. Samarium ; Isotropy ; Charge (physics) ; Doping ; Negative thermal expansion ; Thermal expansion ; Thermal ; Materials science ; Thermal transfer ; Physics ; Condensed matter physics ; Atomic physics ; Nanotechnology ; Nuclear physics ; Quantum mechanics ; Thermodynamics Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Ferroelectric and Piezoelectric Materials
Authors: Sascha Buchegger | Andrej Kamenac | Sven Fuchs | R. Herrmann | Pia Houdek | Christian Gorzelanny | Andreas Obermeier | Stephan Heller | Rainer Burgkart | B. Stritzker | A. Wixforth | Christoph Westerhausen
Source: Scientific Reports (article, 2019-11-21)
Field: Materials Science — Materials Chemistry

Smart antimicrobial efficacy employing pH-sensitive ZnO-doped diamond-like carbon coatings One of the main challenges in endoprosthesis surgeries are implant-associated infections and aseptic-loosenings, caused by wear debris. To combat these problems, the requirements to surfaces of endoprostheses are wear-resistance, low cytotoxicity and antimicrobial efficacy. We here present antimicrobial coatings with a smart, adaptive release of metal ions in case of infection, based on ZnO-nanoparticles embedded in diamond-like carbon (DLC). The Zn<sup>2+</sup> ion release of these coatings in aqueous environments reacts and adapts smartly on inflammations accompanied by acidosis. Moreover, we show that this increased ion release comes along with an increased toxicity to fibroblastic cells (L929) and bacteria (Staphylococcus aureus subsp. aureus, resistant to methicillin and oxacillin. (ATCC 43300, MRSA) and Staphylococcus epidermidis (ATCC 35984, S. epidermidis). Interestingly, the antimicrobial effect and the cytotoxicity of the coatings increase with a reduction of the pH value from 7.4 to 6.4, but not further to pH 5.4. Staphylococcus epidermidis ; Antimicrobial ; Staphylococcus aureus ; Cytotoxicity ; Microbiology ; Carbon fibers ; Chemistry ; Methicillin-resistant Staphylococcus aureus ; Bacteria ; Nuclear chemistry ; Materials science ; In vitro ; Biology ; Biochemistry ; Composite material Diamond and Carbon-based Materials Research ; Graphene and Nanomaterials Applications ; Polymer Surface Interaction Studies
Authors: Joanna L. Donnelly | Daniel Offenbartl‐Stiegert | José Manuel Marín‐Beloqui | Loris Rizzello | Guiseppe Battaglia | Tracey M. Clarke | Stefan Howorka | Jonathan D. Wilden
Source: Chemistry - A European Journal (article, 2019-10-29)
Field: Materials Science — Materials Chemistry

Exploring the Relationship between BODIPY Structure and Spectroscopic Properties to Design Fluorophores for Bioimaging Designing chromophores for biological applications requires a fundamental understanding of how the chemical structure of a chromophore influences its photophysical properties. We here describe the synthesis of a library of BODIPY dyes, exploring diversity at various positions around the BODIPY core. The results show that the nature and position of substituents have a dramatic effect on the spectroscopic properties. Substituting in a heavy atom or adjusting the size and orientation of a conjugated system provides a means of altering the spectroscopic profiles with high precision. The insight from the structure-activity relationship was applied to devise a new BODIPY dye with rationally designed photochemical properties including absorption towards the near-infrared region. The dye also exhibited switch-on fluorescence to enable visualisation of cells with high signal-to-noise ratio without washing-out of unbound dye. The BODIPY-based probe is non-cytotoxic and compatible with staining procedures including cell fixation and immunofluorescence microscopy. BODIPY ; Chromophore ; Fluorescence ; Photochemistry ; Conjugated system ; Fluorescence microscope ; Microscopy ; Chemistry ; Materials science ; Nanotechnology ; Biophysics ; Organic chemistry ; Optics ; Polymer Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Molecular Sensors and Ion Detection
Authors: Paavo Auvinen | Niko Kinnunen | Janne T. Hirvi | Teuvo Maunula | Kauko Kallinen | Matthew Keenan | R.S.G. Baert | Erik van den Tillaart | Mika Suvanto
Source: Applied Catalysis B: Environmental (article, 2019-07-24)
Field: Materials Science — Materials Chemistry

Development of a rapid ageing technique for modern methane combustion catalysts in the laboratory: Why does SO2 concentration play an essential role? In pursuance to decrease emissions of transportation sector, the durability and longevity of modern catalysts has become a topical issue. Understanding the deactivation of catalysts is especially urgent. Our aim in this study was to clarify the roles of reaction temperature and SO2 concentration in methane combustion catalyst poisoning. Information about this process can help the research community perform more realistic laboratory simulations and provide new insights for catalyst development. With the collected experimental data, the likelihood that poisoning would be influenced by different sulphur species and mechanisms was evaluated. Our results suggested that both reaction temperature and SO2 concentration influenced the stability of the resulting sulphates. Low SO2 concentrations lead to formation of stabler sulphates and lower total amount of sulphur in the catalyst. In turn, high SO2 concentrations formed less stable sulphates but accumulated more sulphur. Lesser stability was attributed to formation of Al2(SO4)3 through spillover. Catalysis ; Sulfur ; Combustion ; Methane ; Chemistry ; Durability ; Anaerobic oxidation of methane ; Environmental chemistry ; Chemical engineering ; Environmental science ; Materials science ; Organic chemistry ; Engineering ; Composite material Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Catalysis and Oxidation Reactions
Authors: Mareike Picklum | Michael Beetz
Source: Computational Materials Science (article, 2019-03-16)
Field: Materials Science — Materials Chemistry

MatCALO: Knowledge-enabled machine learning in materials science Industrial and technological innovations constantly call for the development of materials that meet specific and novel requirement profiles. However, the engineering of materials with the desired properties is a time-, cost-, and labor-intensive process as changing the composition and the parameterization of processing steps yields an unimaginably large search space. It is therefore crucial for the future engineering of materials to be conducted in a more goal-directed and more efficient way to generate informative hypotheses revealing particularly promising experiments and thus guiding the exploration process in a more informed fashion. In this paper, we present MatCALO, an intelligent, cognitive assistant system that supports materials scientists in developing novel materials. MatCALO combines modern machine learning techniques with machine-interpretable semantic knowledge in order to model representations of relationships between materials, processes and properties and allow reasoning about them. We showcase a prototypical approach for generating such hypotheses by reverse-querying regression models learned from experimental data. However, a workable set of experiments alone generally does not suffice as it will never cover an adequately large area of the search space, which led us to the conclusion that additional background knowledge is required to build more reliable models. The readers can assure themselves of the feasibility of this approach by testing the web-based interface using the MatCALO system which will be made available to the broad community. Given a requirement profile, the system generates hypotheses on how these criteria can be achieved. Computer science ; Process (computing) ; Interface (matter) ; Set (abstract data type) ; Artificial intelligence ; Machine learning ; Space (punctuation) ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Mário R. G. Marques | Jakob Wolff | Conrad Steigemann | Miguel A. L. Marques
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures We present a practical procedure to obtain reliable and unbiased neural network based force fields for solids. Training and test sets are efficiently generated from global structural prediction runs, at the same time assuring the structural variety and importance of sampling the relevant regions of phase space. The neural networks are trained to yield not only good formation energies, but also accurate forces and stresses, which are the quantities of interest for molecular dynamics simulations. Finally, we construct, as an example, several force fields for both semiconducting and metallic elements, and prove their accuracy for a variety of structural and dynamical properties. These are then used to study the melting of bulk copper and gold. Phonon ; Simple (philosophy) ; Semiconductor ; Melting temperature ; Materials science ; Artificial neural network ; Condensed matter physics ; Nanotechnology ; Chemical physics ; Physics ; Optoelectronics ; Computer science ; Composite material ; Artificial intelligence Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Nuclear Physics and Applications
Authors: Wenwen Li | Yasunobu Ando
Source: The Journal of Chemical Physics (article, 2019-09-16)
Field: Materials Science — Materials Chemistry

Dependence of a cooling rate on structural and vibrational properties of amorphous silicon: A neural network potential-based molecular dynamics study Amorphous materials have variable structural order, which has a significant influence on their electronic, transport, and thermal properties. However, this difference in structure has rarely been investigated by atomistic modeling. In this study, a high-quality machine-learning-based interatomic potential was used to generate a series of atomic structures of amorphous silicon with different degrees of disorder by simulated cooling from the melt with different cooling rates (10<sup>11</sup>-10<sup>15</sup> K/s). We found that the short- and intermediate-range orders are enhanced with decreasing cooling rate, and the influence of the structural order change is in excellent agreement with the experimental annealing process in terms of the structural, energetic, and vibrational properties. In addition, by comparing the excess energies, structure factors, radial distribution functions, phonon densities of states, and Raman spectra, it is possible to determine the corresponding theoretical model for experimental samples prepared with a certain method and thermal history. Amorphous solid ; Molecular dynamics ; Materials science ; Interatomic potential ; Silicon ; Amorphous silicon ; Raman spectroscopy ; Phonon ; Annealing (glass) ; Thermal ; Thermodynamics ; Crystalline silicon ; Condensed matter physics ; Computational chemistry ; Chemistry ; Crystallography ; Optics ; Physics ; Optoelectronics ; Composite material Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Thin-Film Transistor Technologies
Authors: Giuseppe Marco Randazzo | Andrea Bileck | Andrea Danani | Bruno Vogt | Michael Groessl
Source: Journal of Chromatography A (article, 2019-10-28)
Field: Chemistry — Spectroscopy

Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry Chemistry ; Partial least squares regression ; Artificial intelligence ; Convolutional neural network ; Support vector machine ; Mass spectrometry ; Analyte ; Pattern recognition (psychology) ; Resolution (logic) ; Chemometrics ; Artificial neural network ; Chromatography ; Kovats retention index ; Gas chromatography ; Random forest ; Mass ; Machine learning ; Mass spectrum ; Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Takashi Kamachi | Toshinobu Tatsumi | Takashi Toyao | Yoyo Hinuma | Zen Maeno | Satoru Takakusagi | Shinya Furukawa | Ichigaku Takigawa | Ken‐ichi Shimizu
Source: The Journal of Physical Chemistry C (article, 2019-08-05)
Field: Materials Science — Materials Chemistry

Linear Correlations between Adsorption Energies and HOMO Levels for the Adsorption of Small Molecules on TiO<sub>2</sub> Surfaces Adsorption is a fundamental step in catalysis on a solid surface, and therefore, its understanding is important for explaining its behavior. This work investigated the adsorption of various small molecules, including H2, N2, CO, CO2, CH4, NH3, H2O, H2S, dimethyl sulfoxide, alkanes, alkenes, alkynes, aromatic compounds, alcohols, aldehydes, ketones, nitriles, carboxylic acids, amides, and amines, on the anatase (101) and rutile (110) surfaces of TiO2, using periodic density functional theory calculations and statistical methods. Adsorption energies were computed at the same level of theory to obtain a clean and consistent data set. A linear relationship was observed between the adsorption energies of these molecules and their highest occupied molecular orbital (HOMO) levels, whereas no obvious correlation was evident for the lowest unoccupied molecular orbital levels. Improved correlations between the adsorption energies and HOMO levels were generated by dividing these molecules into two subgroups: hydrocarbons and heteroatom-containing compounds. Interactions between frontier molecular orbitals and the surfaces were considered, to gain a better understanding of the significant correlations that were identified. The data show that these relationships can be primarily ascribed to the interactions between the HOMO of the small molecule and conduction state of the TiO2 surface. The statistical analysis using machine learning demonstrated that the HOMO and dipole moment are the first and second most important properties, respectively, in terms of rationalizing and predicting the adsorption energies. Adsorption ; Molecule ; Chemistry ; Molecular orbital ; HOMO/LUMO ; Heteroatom ; Density functional theory ; Sulfoxide ; Computational chemistry ; Anatase ; Physisorption ; Dipole ; Atomic orbital ; Physical chemistry ; Chemical physics ; Catalysis ; Organic chemistry ; Ring (chemistry) ; Physics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Henry Chan | Kiran Sasikumar | Srilok Srinivasan | Mathew Cherukara | Badri Narayanan | Subramanian K. R. S. Sankaranarayanan
Source: Nanoscale (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine learning a bond order potential model to study thermal transport in WSe<sub>2</sub>nanostructures Nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics. The thermal properties of these nanoscale TMDCs are of particular interest for thermoelectric applications. Thermal transport studies on nanotubes and nanoribbons remain intractable to first principles calculations whereas existing classical molecular models treat the two chalcogen layers in a monolayer with different atom types; this imposes serious limitations in studying multi-layered TMDCs and dynamical phenomena such as nucleation and growth. Here, we overcome these limitations using machine learning (ML) and introduce a bond order potential (BOP) trained against first principles training data to capture the structure, dynamics, and thermal transport properties of a model TMDC such as WSe2. The training is performed using a hierarchical objective genetic algorithm workflow to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet. As a representative case study, we perform molecular dynamics simulations using the ML-BOP model to study the structure and temperature-dependent thermal conductivity of WSe2 tubes and ribbons of different chiralities. We observe slightly higher thermal conductivities along the armchair direction than zigzag for WSe2 monolayers but the opposite effect for nanotubes, especially of smaller diameters. We trace the origin of these differences to the anisotropy in thermal transport and the restricted momentum selection rules for phonon-phonon Umpklapp scattering. The developed ML-BOP model is of broad interest and will facilitate studies on nucleation and growth of low dimensional WSe2 structures as well as their transport properties for thermoelectric and thermal management applications. Nanostructure ; Order (exchange) ; Thermal ; Materials science ; Nanotechnology ; Physics ; Thermodynamics ; Business Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Arkadiusz Dobrzycki | Stanisław Mikulski | W. Opydo
Source: Applied Sciences (article, 2019-04-12)
Field: Materials Science — Materials Chemistry

Using ANN and SVM for the Detection of Acoustic Emission Signals Accompanying Epoxy Resin Electrical Treeing Electrical treeing is one of the effects of partial discharges in the solid insulation of high-voltage electrical insulating systems. The process involves the formation of conductive channels inside the dielectric. Acoustic emission (AE) is a method of partial discharge detection and measurement, which belongs to the group of non-destructive methods. If electrical treeing is detected, the measurement, recording, and analysis of signals, which accompany the phenomenon, become difficult due to the low signal-to-noise ratio and possible multiple signal reflections from the boundaries of the object. That is why only selected signal parameters are used for the detection and analysis of the phenomenon. A detailed analysis of various acoustic emission signals is a complex and time-consuming process. It has inspired the search for new methods of identifying the symptoms related to partial discharge in the recorded signal. Bearing in mind that a similar signal is searched, denoting a signal with similar characteristics, the use of artificial neural networks seems pertinent. The paper presents an effort to automate the process of insulation material condition identification based on neural classifiers. An attempt was made to develop a neural classifier that enables the detection of the symptoms in the recorded acoustic emission signals, which are evidence of treeing. The performed studies assessed the efficiency with which different artificial neural networks (ANN) are able to detect treeing-related signals and the appropriate selection of such input parameters as statistical indicators or analysis windows. The feedforward network revealed the highest classification efficiency among all analyzed networks. Moreover, the use of primary component analysis helps to reduce the teaching data to one variable at a classification efficiency of up to 1%. Electrical treeing ; Acoustic emission ; Partial discharge ; Artificial neural network ; SIGNAL (programming language) ; Acoustics ; Computer science ; Materials science ; Pattern recognition (psychology) ; Voltage ; Biological system ; Electronic engineering ; Artificial intelligence ; Electrical engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Cuong Ly | Adam M. Olsen | Ian J. Schwerdt | Reid Porter | Kari Sentz | Luther W. McDonald | Tolga Taşdizen
Source: Journal of Nuclear Materials (article, 2019-02-09)
Field: Materials Science — Materials Chemistry

A new approach for quantifying morphological features of U3O8 for nuclear forensics using a deep learning model Segmentation ; Artificial intelligence ; Computer science ; Intersection (aeronautics) ; Software ; Sample (material) ; Pattern recognition (psychology) ; Metric (unit) ; Deep learning ; Process (computing) ; Image segmentation ; Computer vision ; Physics ; Cartography ; Engineering ; Geography Nuclear Materials and Properties ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Xiaoyu Gong | Dong Chaofang | Jiajin Xu | Li Wang | Xiaogang Li
Source: Materials and Corrosion (article, 2019-10-07)
Field: Materials Science — Materials Chemistry

Machine learning assistance for electrochemical curve simulation of corrosion and its application Abstract In this paper, we used machine learning algorithms such as k‐nearest neighbour, decision tree, gradient boosting decision tree, random forest, and support vector machine in a scikit‐learn module of Python to construct polarization curves and electrochemical impedance spectra. After application of this method to a high‐level nuclear waste disposal tank material of pure copper, the polarization curves, and electrochemical impedance spectra of pure copper under different chloride ion concentrations, sulfide concentrations, and temperature environments were used as training sets. The combination of the cross‐validation and experimental validation results revealed that the RF algorithm had the best effect on predicting the polarization curve and EIS. Through the input weight analysis, it was found that the sulfide concentration had the greatest influence on the polarization curve, followed by the chloride ion concentration, and the temperature influence was the smallest. For electrochemical impedance spectroscopy, sulfide and temperature had a large effect, while chloride ions had little effect. The result of the weight analysis was consistent with traditional electrochemical results. Dielectric spectroscopy ; Polarization (electrochemistry) ; Decision tree ; Materials science ; Analytical Chemistry (journal) ; Sulfide ; Chloride ; Support vector machine ; Python (programming language) ; Electrical impedance ; Electrochemistry ; Copper ; Machine learning ; Computer science ; Chemistry ; Metallurgy ; Electrode ; Electrical engineering ; Engineering ; Chromatography ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Non-Destructive Testing Techniques