jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bin Jia | A. Rusinek | Raphaël Pesci | Slim Bahi | R. Bernier
Source: International Journal of Mechanical Sciences (article, 2019-12-04)
Field: Materials Science — Materials Chemistry

Thermo-viscoplastic behavior of 304 austenitic stainless steel at various strain rates and temperatures: Testing, modeling and validation Materials science ; Viscoplasticity ; Strain rate ; Flow stress ; Portevin–Le Chatelier effect ; Hardening (computing) ; Strain hardening exponent ; Composite material ; Austenite ; Sensitivity (control systems) ; Austenitic stainless steel ; Diffusionless transformation ; Dynamic strain aging ; Deformation (meteorology) ; Metallurgy ; Constitutive equation ; Thermodynamics ; Finite element method ; Microstructure High-Velocity Impact and Material Behavior ; Laser-Plasma Interactions and Diagnostics ; Metal and Thin Film Mechanics
Authors: Jonas Anderegg | Andreas Hund | Petteri Karisto | Alexey Mikaberidze
Source: Frontiers in Plant Science (article, 2019-10-25)
Field: Chemistry — Analytical Chemistry

In-Field Detection and Quantification of Septoria Tritici Blotch in Diverse Wheat Germplasm Using Spectral–Temporal Features Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of <i>Septoria tritici</i> blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding. Septoria ; Germplasm ; Biology ; Mycosphaerella graminicola ; Agronomy Spectroscopy and Chemometric Analyses ; Wheat and Barley Genetics and Pathology ; Smart Agriculture and AI
Authors: Yangqiu Liu | Yue Xin | Chenguang Luo | Lin Zhang | Ming Lei
Source: Energy & environment materials (article, 2019-10-27)
Field: Chemistry — Inorganic Chemistry

Mechanisms of Ketone/Imine Hydrogenation Catalyzed by Transition‐Metal Complexes Alcohols and amines are important in pharmaceutical, perfume, and agrochemical industries. Catalytic asymmetric synthesis is one of the major ways to produce chiral alcohols/amines from prochiral ketones/imines via hydrogenation. Meanwhile, the alcohol/amine dehydrogenation with high hydrogen energy density is paid more and more attention as promising hydrogen‐storage media. In this review, we summarize classifications of mechanisms of ketone/imine hydrogenation and alcohol/amine dehydrogenation catalyzed by transition‐metal (TM) complexes, the H 2 activation modes, and the nature of asymmetric ketone/imine hydrogenation (AKH/AIH). This will elaborate our understanding on the nature of the TM‐catalyzed ketone/imine hydrogenation and alcohol/amine dehydrogenation reactions. Imine ; Dehydrogenation ; Ketone ; Catalysis ; Chemistry ; Amine gas treating ; Organic chemistry ; Alcohol ; Noyori asymmetric hydrogenation ; Transition metal ; Enantioselective synthesis Asymmetric Hydrogenation and Catalysis ; Catalysis for Biomass Conversion ; Carbon dioxide utilization in catalysis
Authors: Hidetoshi Somekawa
Source: MATERIALS TRANSACTIONS (review, 2019-09-29)
Field: Materials Science — Biomaterials

Effect of Alloying Elements on Fracture Toughness and Ductility in Magnesium Binary Alloys; A Review The development of magnesium alloys, which exhibit high strength and high ductility (fracture toughness), is critical for ensuring the safety and reliability in structural applications. It is well-known that grain refinement and/or alloying are impressive strategies to attain such properties in metallic materials. In the former case, grain boundaries of magnesium and its alloys have unique characteristics, e.g., sites for non-basal dislocation activity and occurrence of partial grain boundary sliding. As a result, strength as well as ductility (fracture toughness) tend to increase and improve with grain refinement. In the latter case, 29 types of solid solution elements, which have a maximum solubility of more than 0.1 at%, can dissolve in magnesium. Several elements are generally added to magnesium simultaneously to achieve good mechanical properties via a synergistic effect. In industrial fields, ternary magnesium alloys such as Mg–Al–Zn and Mg–Zn–Zr alloys, which have fine-grained structures, have been widely used; however, there is no still clear and systematic understanding of the impact of various alloying elements on properties for magnesium. In this paper, we review recent studies on the effect of solid solution alloying elements on ductility (fracture toughness), with focusing on polycrystalline binary magnesium alloys. With regard to the toughness, the crack-propagation behavior and/or fracture behavior are quite sensitive to the alloying element, regardless of grain size. Twin boundaries in particular are recognized as harmful defects, because they act as crack-propagation sites. Nevertheless, changing the electric bonding behavior through alloying has the potential to increase the toughness. As for the ductility, the alloying elements also dramatically affect the room-temperature plastic deformation. In addition to the activation of the non-basal dislocation slip, grain boundary sliding also plays a notable role in enhancing the elongation-to-failure in tension. Materials science ; Ductility (Earth science) ; Fracture toughness ; Magnesium ; Metallurgy ; Toughness ; Grain boundary ; Crystallite ; Microstructure ; Creep Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Microstructure and mechanical properties
Authors: Alphy Sebastian | Malay Krishna Mahato | Edamana Prasad
Source: Soft Matter (article, 2019-01-01)
Field: Materials Science — Biomaterials

A mixed ligand approach towards lanthanide-based gels using citric acid as assembler ligand: white light emission and environmental sensing Fine tuning the optical properties of lanthanide-based gels using low molecular weight gelators has several advantages over the polymeric gelator analogues. Herein, we have prepared a lanthanide-based gel using low molecular weight citric acid as the assembler ligand and the optical properties of the gel were fine-tuned, utilizing a mixed ligand approach, enabling white light emission and environmental sensing (pH and temperature). The coligand utilized in the study was 4'-(4-bromophenyl)-2,2':6',2''-terpyridine. The resultant mixed-ligand gel exhibited green and red emissions in the presence of Tb(iii) ions and Eu(iii) ions, respectively. White light emission was achieved, with CIE coordinates (0.33, 0.32), in the bimetallic Tb/Eu metallogel formed by the precise control over Tb/Eu ratio. The correlated color temperature (CCT) for white-light-emitting gel was calculated, and the value of 5473 K suggests that the system generates cool white light. While most of the reported low molecular weight gelators exhibit on-off responses to stimulus at a particular value, the present system is capable of gradually monitoring changes for stimuli such as pH and temperature over a wide range (pH from 4-11 and temperature from 20 to 70 °C). The unique design strategy of the gel and the characteristic physicochemical properties of the lanthanide ions resulted in the unprecedented ability of the system to monitor changes in environmental stimuli over a considerable range. Citric acid ; Lanthanide ; Ligand (biochemistry) ; White light ; Chemistry ; Photochemistry ; Organic chemistry ; Materials science ; Biochemistry ; Receptor ; Optoelectronics ; Ion Supramolecular Self-Assembly in Materials ; Luminescence and Fluorescent Materials ; Polyoxometalates: Synthesis and Applications
Authors: Yang Yu | Theodore Tyrikos‐Ergas | Yuntao Zhu | Giulio Fittolani | Vittorio Bordoni | Ankush Singhal | Richard J. Fair | Andrea Grafmüller | Peter H. Seeberger | Martina Delbianco
Source: Angewandte Chemie (article, 2019-07-30)
Field: Materials Science — Biomaterials

Systematic Hydrogen‐Bond Manipulations To Establish Polysaccharide Structure–Property Correlations Abstract A dense hydrogen‐bond network is responsible for the mechanical and structural properties of polysaccharides. Random derivatization alters the properties of the bulk material by disrupting the hydrogen bonds, but obstructs detailed structure–function correlations. We have prepared well‐defined unnatural oligosaccharides including methylated, deoxygenated, deoxyfluorinated, as well as carboxymethylated cellulose and chitin analogues with full control over the degree and pattern of substitution. Molecular dynamics simulations and crystallographic analysis show how distinct hydrogen‐bond modifications drastically affect the solubility, aggregation behavior, and crystallinity of carbohydrate materials. This systematic approach to establishing detailed structure–property correlations will guide the synthesis of novel, tailor‐made carbohydrate materials. Hydrogen bond ; Crystallinity ; Chemistry ; Polysaccharide ; Solubility ; Cellulose ; Derivatization ; Computational chemistry ; Molecule ; Crystallography ; Organic chemistry Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; biodegradable polymer synthesis and properties
Authors: Rubén Cantón-Vitoria | Tobías Scharl | Anastasios Stergiou | Alejandro Cadranel | Raúl Arenal | Dirk M. Guldi | Nikos Tagmatarchis
Source: Angewandte Chemie International Edition (article, 2019-12-11)
Field: Materials Science — Materials Chemistry

Ping‐Pong Energy Transfer in Covalently Linked Porphyrin‐MoS<sub>2</sub> Architectures Molybdenum disulfide nanosheets covalently modified with porphyrin were prepared and fully characterized. Neither the porphyrin absorption nor its fluorescence was notably affected by covalent linkage to MoS<sub>2</sub> . The use of transient absorption spectroscopy showed that a complex ping-pong energy-transfer mechanism, namely from the porphyrin to MoS<sub>2</sub> and back to the porphyrin, operated. This study reveals the potential of transition-metal dichalcogenides in photosensitization processes. Porphyrin ; Covalent bond ; Molybdenum disulfide ; Photochemistry ; Energy transfer ; Absorption (acoustics) ; Förster resonance energy transfer ; Chemistry ; Molybdenum ; Ultrafast laser spectroscopy ; Fluorescence ; Metal ; Spectroscopy ; Materials science ; Inorganic chemistry ; Organic chemistry ; Chemical physics ; Optics 2D Materials and Applications ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Miaomiao Jin | Penghui Cao | Michael P. Short
Source: Journal of Nuclear Materials (article, 2019-06-03)
Field: Materials Science — Materials Chemistry

Predicting the onset of void swelling in irradiated metals with machine learning Swelling ; Void (composites) ; Irradiation ; Materials science ; Computer science ; Radiation damage ; Nuclear engineering ; Biological system ; Composite material ; Physics ; Nuclear physics ; Engineering Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Taro Fukazawa | Yosuke Harashima | Zhufeng Hou | Takashi Miyake
Source: Physical Review Materials (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Bayesian optimization of chemical composition: A comprehensive framework and its application to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>R</mml:mi><mml:msub><mml:mi>Fe</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mrow></mml:math>-type magnet compounds We propose a framework for optimization of the chemical composition of multinary compounds with the aid of machine learning. The scheme is based on first-principles calculation using the Korringa-Kohn-Rostoker method and the coherent potential approximation (KKR-CPA). We introduce a method for integrating datasets to reduce systematic errors in a dataset, where the data are corrected using a smaller and more accurate dataset. We apply this method to values of the formation energy calculated by KKR-CPA for nonstoichiometric systems to improve them using a small dataset for stoichiometric systems obtained by the projector-augmented-wave method. We apply our framework to optimization of $R{\mathrm{Fe}}_{12}$-type magnet compounds $({R}_{1\ensuremath{-}\ensuremath{\alpha}}{Z}_{\ensuremath{\alpha}}){({\mathrm{Fe}}_{1\ensuremath{-}\ensuremath{\beta}}{\mathrm{Co}}_{\ensuremath{\beta}})}_{12\ensuremath{-}\ensuremath{\gamma}}{\mathrm{Ti}}_{\ensuremath{\gamma}}$, and benchmark the efficiency in determination of the optimal choice of elements ($R$ and $Z$) and ratio ($\ensuremath{\alpha}$, $\ensuremath{\beta}$, and $\ensuremath{\gamma}$) with respect to magnetization, Curie temperature, and formation energy. We find that the optimization efficiency depends on descriptors significantly. The variables $\ensuremath{\beta}$, $\ensuremath{\gamma}$, and the number of electrons from the $R$ and $Z$ elements per cell are important in improving the efficiency. When the descriptor is appropriately chosen, the Bayesian optimization becomes much more efficient than random sampling. Bayesian optimization ; Benchmark (surveying) ; Bayesian probability ; Magnet ; Energy (signal processing) ; Stoichiometry Machine Learning in Materials Science ; Magnetic Properties of Alloys ; X-ray Diffraction in Crystallography
Authors: Yuhang Tang | Wibe A. de Jong
Source: The Journal of Chemical Physics (article, 2019-01-25)
Field: Materials Science — Materials Chemistry

Prediction of atomization energy using graph kernel and active learning Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effects of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 ± 0.01 kcal/mol using as few as 2000 training samples on the QM7 dataset. Graph ; Kernel (algebra) ; Computer science ; Kernel method ; Gaussian process ; Artificial intelligence ; Polynomial kernel ; Algorithm ; Covariance ; Gaussian ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Theoretical computer science ; Support vector machine ; Combinatorics ; Statistics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Dmitry Sharapa | Dmitry E. Doronkin | Felix Studt | Jan‐Dierk Grunwaldt | Silke Behrens
Source: Advanced Materials (review, 2019-02-25)
Field: Materials Science — Materials Chemistry

Moving Frontiers in Transition Metal Catalysis: Synthesis, Characterization and Modeling Nanosized transition metal particles are important materials in catalysis with a key role not only in academic research but also in many processes with industrial and societal relevance. Although small improvements in catalytic properties can lead to significant economic and environmental impacts, it is only now that knowledge-based design of such materials is emerging, partly because the understanding of catalytic mechanisms on nanoparticle surfaces is increasingly improving. A knowledge-based design requires bottom-up synthesis of well-defined model catalysts, an understanding of the catalytic nanomaterials "at work" (operando), and both a detailed understanding and a prediction by theoretical methods. This article reports on progress in colloidal synthesis of transition metal nanoparticles for preparation of model catalysts to close the materials gap between the discoveries of fundamental surface science and industrial application. The transition metal particles, however, often undergo extensive transformations when applied to the catalytic process and much progress has recently been achieved operando characterization techniques under relevant reaction conditions. They allow better understanding of size/structure-activity correlations in these systems. Moreover, the growth of computing power and the improvement of theoretical methods uncover mechanisms on nanoparticles and have recently predicted highly active particles for CO/CO<sub>2</sub> hydrogenation or direct H<sub>2</sub> O<sub>2</sub> synthesis. Nanotechnology ; Catalysis ; Characterization (materials science) ; Materials science ; Nanomaterials ; Nanoparticle ; Transition metal ; Biochemical engineering ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysts for Methane Reforming
Authors: Matthias Filez | E. Redekop | Jolien Dendooven | Ranjith K. Ramachandran | Eduardo Solano | Unni Olsbye | Bert M. Weckhuysen | Vladimir Galvita | Hilde Poelman | Christophe Detavernier | Guy Marin
Source: Angewandte Chemie International Edition (review, 2019-04-01)
Field: Materials Science — Materials Chemistry

Formation and Functioning of Bimetallic Nanocatalysts: The Power of X‐ray Probes Bimetallic nanocatalysts are key enablers of current chemical technologies, including car exhaust converters and fuel cells, and play a crucial role in industry to promote a wide range of chemical reactions. However, owing to significant characterization challenges, insights in the dynamic phenomena that shape and change the working state of the catalyst await further refinement. Herein, we discuss the atomic-scale processes leading to mono- and bimetallic nanoparticle formation and highlight the dynamics and kinetics of lifetime changes in bimetallic catalysts with showcase examples for Pt-based systems. We discuss how in situ and operando X-ray spectroscopy, scattering, and diffraction can be used as a complementary toolbox to interrogate the working principles of today's and tomorrow's bimetallic nanocatalysts. Bimetallic strip ; Nanomaterial-based catalyst ; Catalysis ; Nanotechnology ; Materials science ; Nanoparticle ; Characterization (materials science) ; Metal ; Chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Daniel José da Silva | Hélio Wiebeck
Source: Polímeros (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Predicting LDPE/HDPE blend composition by CARS-PLS regression and confocal Raman spectroscopy Industries and the scientific community currently focus on creating new ways to recycle and to reuse polymer waste that leads to serious socio-environmental risks. However, the quality of recycled polyethylenes depends strongly on their purity degree, but the distinction between Low Density Polyethylene (LDPE) and High Density Polyethylene (HDPE) by a fast and consistently good methodology is still an unsolved issue for the current recycling processes. In this study, confocal Raman spectroscopy and Competitive Adaptive Reweighted Sampling - Partial Least Squares (CARS-PLS) linear regression have been successfully applied to quantify the concentration of LDPE/HDPE blends. The effects of several regression parameters (pretreatment method, Monte Carlo sampling runs, k-fold and maximal number of latent variables for cross-validation) on the CARS-PLS model training and prediction performance were analyzed. The CARS-PLS-based models show root-mean-squared prediction error of 4.06 - 8.87 wt% of LDPE for the whole composition range of HDPE/LDPE blend. Low-density polyethylene ; High-density polyethylene ; Partial least squares regression ; Polyethylene ; Materials science ; Linear regression ; Regression analysis ; Statistics ; Mathematics ; Composite material Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Marco Baity-Jesi | Levent Sagun | Mario Geiger | Stefano Spigler | Gérard Ben Arous | Chiara Cammarota | Yann LeCun | Matthieu Wyart | Giulio Biroli
Source: Journal of Statistical Mechanics Theory and Experiment (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Comparing dynamics: deep neural networks versus glassy systems* Abstract We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (1) the complexity of the loss landscape and of the dynamics within it, and (2) to what extent DNNs share similarities with glassy systems. Our findings, obtained for different architectures and datasets, suggest that during the training process the dynamics slows down because of an increasingly large number of flat directions. At large times, when the loss is approaching zero, the system diffuses at the bottom of the landscape. Despite some similarities with the dynamics of mean-field glassy systems, in particular, the absence of barrier crossing, we find distinctive dynamical behaviors in the two cases, showing that the statistical properties of the corresponding loss and energy landscapes are different . In contrast, when the network is under-parametrized we observe a typical glassy behavior, thus suggesting the existence of different phases depending on whether the network is under-parametrized or over-parametrized. Artificial neural network ; Dynamics (music) ; Deep neural networks ; Process (computing) ; Energy landscape ; Statistical learning ; Energy (signal processing) Machine Learning in Materials Science ; Material Dynamics and Properties ; Quantum many-body systems