jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bernd Strehmel | Christian Schmitz | Kevin Cremanns | J. Göttert
Source: Chemistry - A European Journal (article, 2019-07-03)
Field: Chemistry — Organic Chemistry

Photochemistry with Cyanines in the Near Infrared: A Step to Chemistry 4.0 Technologies Cyanines covering the absorption in the near infrared (NIR) are attractive for distinct applications. They can interact either with lasers exhibiting line-shaped focus emitting at both 808 and 980 nm or bright high intensity NIR-LEDs with 805 nm emission, respectively. This is drawing attention to Industry 4.0 applications. The major deactivation occurs through a non-radiative process resulting in the release of heat into the surrounding, although a small fraction of radiative deactivation also takes place. Most of these NIR-sensitive systems possess an internal activation barrier to react in a photonic process with initiators resulting in the generation of reactive radicals and acidic cations. Thus, the heat released by the NIR absorber helps to bring the system, consisting of an NIR sensitizer and initiator, above such internal barriers. Molecular design strategies making these systems more compatible with distinct applications in a certain oleophilic surrounding are considered as a big challenge. This includes variations of the molecular pattern and counter ions derived from super acids exhibiting low coordinating properties. Further discussion focusses on the use of such systems in Chemistry 4.0 related applications. Intelligent software tools help to improve and optimize these systems combining chemistry, engineering based on high-throughput formulation screening (HTFS) technologies, and machine learning algorithms to open up novel solutions in material sciences. Near-infrared spectroscopy ; Absorption (acoustics) ; Chemistry ; Radical ; Photonics ; Radiative transfer ; Nanotechnology ; Photochemistry ; Materials science ; Optoelectronics ; Organic chemistry ; Physics Radical Photochemical Reactions ; Photochromic and Fluorescence Chemistry ; Oxidative Organic Chemistry Reactions
Authors: Atsuto Seko | Atsushi Togo | Isao Tanaka
Source: Physical review. B./Physical review. B (article, 2019-06-26)
Field: Materials Science — Materials Chemistry

Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomic potential (MLIP) is one of the applications of rotational invariants, which provides the relationship between the energy and the crystal structure. Therefore, the enumeration of rotational invariants should be useful for constructing MLIPs with the desired accuracy. In this study, we introduce high-order linearly independent rotational invariants up to the sixth order based on spherical harmonics and apply them to linearized MLIPs for elemental aluminum. A set of rotational invariants is derived by the general process of reducing the Kronecker products of irreducible representations for the SO(3) group using a group-theoretical projector method. A high predictive power for a wide range of structures is accomplished by using high-order invariants with low-order invariants equivalent to pair and angular structural features. Spherical harmonics ; Group (periodic table) ; Rotational invariance ; Order (exchange) ; Mathematics ; Algebraic structure ; Pure mathematics ; Physics ; Mathematical analysis ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Kaname Matsumoto | Tomoya Horide
Source: Applied Physics Express (article, 2019-06-12)
Field: Materials Science — Materials Chemistry

An acceleration search method of higher <i>T</i> <sub> <i>c</i> </sub> superconductors by a machine learning algorithm We propose a method to efficiently search for superconductors with higher critical temperature Tc by machine learning based on a superconductor database. The Tc prediction and the search for new superconductors are still difficult problems. With the progress of computer power and calculation algorithms, the possibility of finding new materials with higher Tc at high throughput is emerging. Using the obtained Tc prediction model, the scope is expanded to the search space of multielement materials that have never been searched, and candidates for superconductors with higher Tc that can be synthesized are proposed. Superconductivity ; Scope (computer science) ; Acceleration ; Algorithm ; Computer science ; Space (punctuation) ; High-temperature superconductivity ; Physics ; Artificial intelligence ; Machine learning ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Fluorides and Related Compounds
Authors: Esra Kaya | İsmail Sarıtaş
Source: Computers and Electronics in Agriculture (article, 2019-09-26)
Field: Chemistry — Analytical Chemistry

Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Ingredient ; Artificial neural network ; Wavelet ; Feature (linguistics) ; Computer vision ; Computer science ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Xinyu Wang | Yang Hong | Man Wang | Gongming Xin | Yanan Yue | Jingchao Zhang
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Mechanical properties of molybdenum diselenide revealed by molecular dynamics simulation and support vector machine Despite the spurring interests in two-dimensional transition metal dichalcogenide (TMDC) materials, knowledge on the mechanical properties of one of their important member, i.e., molybdenum diselenide (MoSe2) is scarce and remains an open topic. In this work, the mechanical properties of h-MoSe2 and t-MoSe2 were systematically investigated using classical molecular dynamics (MD) simulations combined with machine learning (ML) techniques. The effects of chirality, temperature and strain rate on fracture strain, fracture strength and Young's modulus were characterized in both armchair and zigzag directions. For h-MoSe2, the fracture strengths were 13.6 and 13.0 GPa for armchair and zigzag chiralities, respectively, at 1 K and strain rate of 5 × 10-4 ps-1; the corresponding fracture strains were 0.23 and 0.27. The Young's moduli in armchair and zigzag directions exhibited similar values of 100.9 and 99.5 GPa, respectively. For t-MoSe2, much lower fracture strengths of 6.1 and 6.3 GPa, fracture strains of 0.13 and 0.15, and Young's moduli of 83.7 and 83.0 GPa were predicted under the same conditions. A total of 700 MD simulation cases were calculated under different impact factors and initial conditions, which were subsequently fed into the support vector machine (SVM) algorithm for ML modeling. After training, the ML model could predict the mechanical properties of both MoSe2 types given only the input features such as chirality, temperature and strain rate. Zigzag ; Materials science ; Molecular dynamics ; Molybdenum ; Modulus ; Chirality (physics) ; Strain rate ; Fracture (geology) ; Crystallography ; Composite material ; Computational chemistry ; Metallurgy ; Chemistry ; Geometry ; Mathematics ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Leo Lebanov | Laura Tedone | Alireza Ghiasvand | Brett Paull
Source: Talanta (article, 2019-10-14)
Field: Chemistry — Analytical Chemistry

Random Forests machine learning applied to gas chromatography – Mass spectrometry derived average mass spectrum data sets for classification and characterisation of essential oils Random forest ; Chemistry ; Mass spectrum ; Mass spectrometry ; Data set ; Gas chromatography ; Chromatography ; Kovats retention index ; Analytical Chemistry (journal) ; Biological system ; Pattern recognition (psychology) ; Artificial intelligence ; Algorithm ; Statistics ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Borna Zandkarimi | Anastassia N. Alexandrova
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Surface‐supported cluster catalysis: Ensembles of metastable states run the show Abstract It has recently been shown that the dynamic behavior of surface‐supported nanocluster catalysts in realistic reaction conditions defies conventional models used in catalysis. This opens new doors in catalysis by giving more leverage in catalyst design, but also requires a major revision of the understanding of how dynamic heterogeneous catalytic interfaces operate, as well as of the computational approaches of catalyst modeling, and experimental methods of catalyst characterization. Major aspects of the new paradigm include the collective action of many catalyst states that form a statistical ensemble in reaction conditions, the catalytic activity and selectivity being driven by rare and metastable catalyst states, reaction thermodynamics and kinetics being controlled by different states of the catalyst, broken scaling relationships, non‐Arrhenius behaviors, and catalyst dynamic restructuring being an essential part of the reaction mechanism. For computation, this complexity means the departure from the standard density functional theory calculations of reaction mechanisms on a single catalyst structure. For experiment, it calls for the development of operando characterization tools with the per‐site resolution and the ability to find the minority sites that govern the catalytic activity. For catalyst design, the goal becomes the creation of the catalyst state (geometric and electronic) that might not be present in the as‐prepared catalyst, but would develop in the reaction conditions and would have the desired activity then. While cluster catalysts are the most dramatic in their dynamic fluxionality, other amorphous interfaces also exhibit some of it, and thus are also subject to similar paradigm revision. This article is categorized under: Structure and Mechanism &gt; Reaction Mechanisms and Catalysis Catalysis ; Heterogeneous catalysis ; Metastability ; Chemistry ; Chemical physics ; Materials science ; Nanotechnology ; Computational chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Theoretical and Computational Physics
Authors: Benson Chen | Tianxiao Shen | Tommi Jaakkola | Regina Barzilay
Source: arXiv (Cornell University) (preprint, 2019-10-21)
Field: Materials Science — Materials Chemistry

Learning to Make Generalizable and Diverse Predictions for Retrosynthesis We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions. On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse. Retrosynthetic analysis ; Generative grammar ; Transformer ; Benchmark (surveying) ; Task (project management) ; Computer science ; Artificial intelligence ; Machine learning ; Sequence (biology) ; Set (abstract data type) ; Construct (python library) ; Architecture ; Generative model ; Sequence learning ; Engineering ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Salma Bilal | Ayesha Akbar | Anwar‐ul‐Haq Ali Shah
Source: Polymers (article, 2019-08-13)
Field: Materials Science — Polymers and Plastics

Highly Selective and Reproducible Electrochemical Sensing of Ascorbic Acid Through a Conductive Polymer Coated Electrode The surface of an Au-disc electrode was modified through electro polymerization of aniline, in the presence of dodecyl benzene sulphonic acid (DBSA) and sulphuric acid (H<sub>2</sub>SO<sub>4</sub>) solution. The polymerization conditions were pre-optimized so that micelle formation and solution coagulation could be minimized and surfactant doped polyaniline film could be obtained through a quick, simple and one step polymerization route. The synthesized material was characterized via Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), scanning electron microscopy (SEM) and cyclic voltammetry (CV). The effective surface area of the Au-disc, calculated through cyclic voltammetry, was immensely increased through a polyaniline (PANI) coating (0.04 and 0.11 cm<sup>2</sup> for bare and PANI coated gold respectively). The modified electrode was utilized for ascorbic acid (AA) sensing. The changing pH of electrolyte and scan rate influenced the PANI electrode response towards AA. The modified electrode was highly selective towards AA oxidation and showed a very low limit of detection i.e. 0.0267 μmol·L<sup>-1</sup>. Moreover, the PANI coating greatly reduced the sensing potential for AA by a value of around 140 mV when compared to that on a bare gold electrode. Polyaniline ; Cyclic voltammetry ; Ascorbic acid ; Materials science ; Polymerization ; Electrode ; Conductive polymer ; Thermogravimetric analysis ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Chemical engineering ; Electrochemistry ; Inorganic chemistry ; Polymer ; Chemistry ; Organic chemistry ; Composite material Conducting polymers and applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Xiaohu Huang
Source: Modern Concepts in Material Science (article, 2019-11-06)
Field: Materials Science — Biomaterials

Plasma Electrolytic Oxidation Coatings on Aluminum Alloys: Microstructures, Properties, and Applications We present a future concept in material science, which we believe will lead to solve many problems researchers have experienced in the past. Our new method is a step forward to conceptualizing key experiments in material science and will provide researchers among the world an important tool towards realizing their goals. Key (lock) ; Aluminium ; Plasma electrolytic oxidation ; Materials science ; Nanotechnology ; Microstructure ; Computer science ; Electrolyte ; Metallurgy ; Chemistry Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Anodic Oxide Films and Nanostructures
Authors: Yu Wang | Yiming Ding | Wei Wei | Yi Cao | David F. Davidson | Ronald K. Hanson
Source: Fuel (article, 2019-07-04)
Field: Chemistry — Analytical Chemistry

On estimating physical and chemical properties of hydrocarbon fuels using mid-infrared FTIR spectra and regularized linear models Hydrocarbon ; Fourier transform infrared spectroscopy ; Boiling point ; Cetane number ; Analytical Chemistry (journal) ; Chemistry ; Infrared spectroscopy ; Infrared ; Absorption (acoustics) ; Materials science ; Organic chemistry ; Physics ; Optics ; Composite material Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Chemical Thermodynamics and Molecular Structure
Authors: Susu Zhu | Lei Zhou | Chu Zhang | Yidan Bao | Baohua Wu | Hangjian Chu | Yue Yu | Yong He | Lei Feng
Source: Sensors (article, 2019-09-20)
Field: Chemistry — Analytical Chemistry

Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra. Hyperspectral imaging ; Pixel ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Identification (biology) ; Spectral line ; Artificial neural network ; Mathematics ; Computer science ; Botany ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Vera Novy | Kevin Aïssa | Fredrik Nielsen | Suzana K. Straus | Peter N. Ciesielski | Christopher G. Hunt | Jack Saddler
Source: Proceedings of the National Academy of Sciences (article, 2019-10-21)
Field: Materials Science — Biomaterials

Quantifying cellulose accessibility during enzyme-mediated deconstruction using 2 fluorescence-tagged carbohydrate-binding modules Two fluorescence-tagged carbohydrate-binding modules (CBMs), which specifically bind to crystalline (CBM2a-RRedX) and paracrystalline (CBM17-FITC) cellulose, were used to differentiate the supramolecular cellulose structures in bleached softwood Kraft fibers during enzyme-mediated hydrolysis. Differences in CBM adsorption were elucidated using confocal laser scanning microscopy (CLSM), and the structural changes occurring during enzyme-mediated deconstruction were quantified via the relative fluorescence intensities of the respective probes. It was apparent that a high degree of order (i.e., crystalline cellulose) occurred at the cellulose fiber surface, which was interspersed by zones of lower structural organization and increased cellulose accessibility. Quantitative image analysis, supported by <sup>13</sup>C NMR, scanning electron microscopy (SEM) imaging, and fiber length distribution analysis, showed that enzymatic degradation predominates at these zones during the initial phase of the reaction, resulting in rapid fiber fragmentation and an increase in cellulose surface crystallinity. By applying this method to elucidate the differences in the enzyme-mediated deconstruction mechanisms, this work further demonstrated that drying decreased the accessibility of enzymes to these disorganized zones, resulting in a delayed onset of degradation and fragmentation. The use of fluorescence-tagged CBMs with specific recognition sites provided a quantitative way to elucidate supramolecular substructures of cellulose and their impact on enzyme accessibility. By designing a quantitative method to analyze the cellulose ultrastructure and accessibility, this study gives insights into the degradation mechanism of cellulosic substrates. Cellulose ; Hydrolysis ; Enzyme ; Chemistry ; Substrate (aquarium) ; Enzymatic hydrolysis ; Biochemistry ; Cellulase ; Polysaccharide ; Fluorescence ; Carbohydrate ; Biology Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Polysaccharides and Plant Cell Walls
Authors: Sergio Royuela | Joaquín Almarza | María J. Mancheño | Juan Carlos Pérez‐Flores | E. G. Michel | Mar Ramos | Félix Zamora | P. Ocón | José L. Segura
Source: Chemistry - A European Journal (article, 2019-07-02)
Field: Materials Science — Materials Chemistry

Synergistic Effect of Covalent Bonding and Physical Encapsulation of Sulfur in the Pores of a Microporous COF to Improve Cycling Performance in Li‐S Batteries Lithium-sulfur batteries stands out as a promising technology for energy storage owing to a combination of favorable characteristics including a high theoretical gravimetric capacity, energy density, inexpensive character, and environmental benignity. Covalent organic frameworks (COFs) are a rapidly developing family of functional nanostructures which combine porosity and crystallinity, and which have been already used in these kinds of batteries to build sulfur electrodes, by embedding sulfur into porous COFs in order to enhance cycle lifetimes. In this contribution, this is taken one step forward and a COF endowed with vinyl groups is used, in order to graft sulfur to the COF skeleton through inverse vulcanization. The main aim of the article is to show the synergistic effect of covalent bonding and physical encapsulation of sulfur in the pores of the COF in order to alleviate the fatal redox shuttling process, to improve the cycling performance, and to provide faster ion diffusion pathways. In addition, it is shown how the material with covalently-bound S provides better electrochemical performance under demanding and/or changeable charge conditions than a parent analogue material with sulfur physically confined, but without covalent linkage. Covalent bond ; Sulfur ; Pseudocapacitor ; Materials science ; Microporous material ; Chemical engineering ; Porosity ; Vulcanization ; Nanotechnology ; Energy storage ; Electrochemistry ; Chemistry ; Electrode ; Organic chemistry ; Composite material ; Natural rubber ; Supercapacitor ; Metallurgy Covalent Organic Framework Applications ; Advanced Battery Materials and Technologies ; Advanced battery technologies research
Authors: Mingfei Zhang | Louis G. Hector | Yang Guo | Ming Liu | Liang Qi
Source: Computational Materials Science (article, 2019-05-02)
Field: Materials Science — Biomaterials

First-principles search for alloying elements that increase corrosion resistance of Mg with second-phase particles of transition metal impurities Impurity ; Corrosion ; Transition metal ; Materials science ; Metallurgy ; Phase (matter) ; Metal ; Phase transition ; Inorganic chemistry ; Chemical engineering ; Chemistry ; Thermodynamics ; Catalysis ; Organic chemistry Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Corrosion Behavior and Inhibition