jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Krishna Sampathkumar | Valentín Diez‐Cabanes | Petr Kovaříček | Elena del Corro | Milan Bouša | Jan Hošek | Martin Kalbáč | Otakar Frank
Source: The Journal of Physical Chemistry C (article, 2019-08-15)
Field: Materials Science — Materials Chemistry

On the Suitability of Raman Spectroscopy to Monitor the Degree of Graphene Functionalization by Diazonium Salts Raman spectroscopy is undoubtedly the most frequently used technique for universal characterization of graphene and related materials. Quantification of parameters like disorder or strain is possible through analysis of particular Raman bands. However, under certain conditions, such evaluation can be jeopardized by—sometimes hidden—convolution of more overlapping effects. In this work, graphene functionalization by the common nitrobenzenediazonium salt under simultaneous biaxial tensile deformation induced by substrate swelling was investigated by Raman spectroscopy. As expected, the disorder-related D band appeared in the spectra documenting the covalent attack on the graphene lattice. However, the strain-induced shift of the graphene bands exposed additional peaks, masked at exactly the same positions as the unstrained graphene bands. The new bands were assigned to vibrations of the diazonium molecule and its decomposition products adsorbed on top of the functionalized graphene. The external strain thus provided means for more correct quantification of the lattice disorder. Graphene ; Raman spectroscopy ; Materials science ; Surface modification ; Spectroscopy ; Molecule ; Infrared spectroscopy ; Covalent bond ; Chemical physics ; Nanotechnology ; Chemistry ; Physical chemistry ; Optics ; Organic chemistry ; Physics Graphene research and applications ; Molecular Junctions and Nanostructures ; Boron and Carbon Nanomaterials Research
Authors: Vlastimil Jirasko | Nils‐Alexander Lakomek | Susanne Penzel | Marie‐Laure Fogeron | Ralf Bartenschlager | Beat H. Meier | Anja Böckmann
Source: ChemBioChem (article, 2019-12-18)
Field: Chemistry — Spectroscopy

Proton‐Detected Solid‐State NMR of the Cell‐Free Synthesized α‐Helical Transmembrane Protein NS4B from Hepatitis C Virus Proton-detected 100 kHz magic-angle-spinning (MAS) solid-state NMR is an emerging analysis method for proteins with only hundreds of microgram quantities, and thus allows structural investigation of eukaryotic membrane proteins. This is the case for the cell-free synthesized hepatitis C virus (HCV) nonstructural membrane protein 4B (NS4B). We demonstrate NS4B sample optimization using fast reconstitution schemes that enable lipid-environment screening directly by NMR. 2D spectra and relaxation properties guide the choice of the best sample preparation to record 2D <sup>1</sup> H-detected <sup>1</sup> H,<sup>15</sup> N and 3D <sup>1</sup> H,<sup>13</sup> C,<sup>15</sup> N correlation experiments with linewidths and sensitivity suitable to initiate sequential assignments. Amino-acid-selectively labeled NS4B can be readily obtained using cell-free synthesis, opening the door to combinatorial labeling approaches which should enable structural studies. Solid-state nuclear magnetic resonance ; Transmembrane protein ; Magic angle spinning ; Membrane protein ; Chemistry ; Proton ; Crystallography ; Materials science ; Nuclear magnetic resonance spectroscopy ; Membrane ; Nuclear magnetic resonance ; Biochemistry ; Stereochemistry ; Physics Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Mohammad Mahdi Shanei | Davood Fathi | Fatemeh Ghasemifard | Óscar Quevedo-Teruel
Source: Scientific Reports (article, 2019-09-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All-silicon reconfigurable metasurfaces for multifunction and tunable performance at optical frequencies based on glide symmetry Dielectric metasurfaces have opened promising possibilities to enable a versatile platform in the miniaturization of optical elements at visible and infrared frequencies. Due to high efficiency and compatibility with CMOS fabrication technology, silicon-based metasurfaces have a remarkable potential for a wide variety of optical devices. Adding tunability mechanisms to metasurfaces could be beneficial for their application in areas such as communications, imaging and sensing. In this paper, we propose an all-silicon reconfigurable metasurface based on the concept of glide symmetry. The reconfigurability is achieved by a phase modulation of the transmitted wave activated by a lateral displacement of the layers. The misalignment between the layers creates a new inner periodicity which leads to the formation of a metamolecule with a new sort of near-field interaction. The proposed approach is highly versatile for developing multifunctional and tunable metadevices at optical frequencies. As a proof of concept, in this paper, we design a bifunctional metadevice, as well as a tunable lens and a controllable beam deflector operating at 1.55 μm. Reconfigurability ; Miniaturization ; Proof of concept ; Materials science ; Optoelectronics ; Silicon ; Fabrication ; Optics ; Lens (geology) ; Computer science ; Nanotechnology ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Marek Paściak | P. Ondrejkovič | J. Kulda | P. Vaněk | Jan Drahokoupil | Gwladys Steciuk | Lukáš Palatinus | T. R. Welberry | Henry E. Fischer | J. Hlinka | E. Buixaderas
Source: Physical review. B./Physical review. B (article, 2019-03-05)
Field: Materials Science — Materials Chemistry

Local structure of relaxor ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Sr</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mi>Ba</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>Nb</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math> from a pair distribution function analysis Pair distribution function analysis of neutron-scattering data and of ab initio molecular dynamics results have been employed to study short-range structural correlations and their temperature dependence in a heavily disordered dielectric material ${\mathrm{Sr}}_{x}{\mathrm{Ba}}_{1\ensuremath{-}x}{\mathrm{Nb}}_{2}{\mathrm{O}}_{6}\phantom{\rule{4pt}{0ex}}(x=0.35,\phantom{\rule{0.28em}{0ex}}0.5$, and 0.61). Intrinsic disorder caused by a partial occupation of the cationic sites by differently sized Sr and Ba atoms and their vacancies introduces important local strains to the structure and directly influences the Nb-${\mathrm{O}}_{6}$ octahedra tilting. The resulting complex system of tilts is found to be both temperature and Sr-doping sensitive with the biggest tilt magnitudes reached at low temperatures and high strontium contents, where ferroelectric relaxor behavior appears. We find evidence for two Nb-${\mathrm{O}}_{6}$ subsystems with different variations of niobium-oxygen bond lengths, distinct dynamics, and disparate levels of deviation from macroscopic polarization direction. These findings establish a detailed picture of the local structure of ${\mathrm{Sr}}_{x}{\mathrm{Ba}}_{1\ensuremath{-}x}{\mathrm{Nb}}_{2}{\mathrm{O}}_{6}$ and provide a deeper insight into the origins of the materials dielectric properties. Physics ; Algorithm ; Mathematics Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Acoustic Wave Resonator Technologies
Authors: Alfonso Gómez-Espinosa | Roberto C. Sundin | Ion Loidi Eguren | Enrique Cuan‐Urquizo | Cecilia D. Treviño‐Quintanilla
Source: Sensors (article, 2019-06-06)
Field: Materials Science — Materials Chemistry

Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83° was achieved when validated against several set-points within the possible range. Artificial neural network ; Control theory (sociology) ; Shape-memory alloy ; Actuator ; Controller (irrigation) ; Computer science ; Position (finance) ; Hysteresis ; Control system ; Control engineering ; Artificial intelligence ; Engineering ; Control (management) Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control
Authors: Christian Hoffmann | Roberto Menichetti | Kiran H. Kanekal | Tristan Bereau
Source: Physical review. E (article, 2019-09-03)
Field: Materials Science — Materials Chemistry

Controlled exploration of chemical space by machine learning of coarse-grained representations The size of chemical compound space is too large to be probed exhaustively. This leads high-throughput protocols to drastically subsample and results in sparse and nonuniform datasets. Rather than arbitrarily selecting compounds, we systematically explore chemical space according to the target property of interest. We first perform importance sampling by introducing a Markov chain Monte Carlo scheme across compounds. We then train a machine learning (ML) model on the sampled data to expand the region of chemical space probed. Our boosting procedure enhances the number of compounds by a factor 2 to 10, enabled by the ML model's coarse-grained representation, which both simplifies the structure-property relationship and reduces the size of chemical space. The ML model correctly recovers linear relationships between transfer free energies. These linear relationships correspond to features that are global to the dataset, marking the region of chemical space up to which predictions are reliable; this is a more robust alternative to the predictive variance. Bridging coarse-grained simulations with ML gives rise to an unprecedented database of drug-membrane insertion free energies for 1.3 million compounds. Chemical space ; Computer science ; Boosting (machine learning) ; Property (philosophy) ; Space (punctuation) ; Markov chain ; Bridging (networking) ; Markov chain Monte Carlo ; Sampling (signal processing) ; Variance (accounting) ; Representation (politics) ; Algorithm ; Monte Carlo method ; Statistical physics ; Drug discovery ; Mathematics ; Artificial intelligence ; Machine learning ; Statistics ; Chemistry ; Physics ; Bayesian probability Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kohei Shimamura | Shogo Fukushima | Akihide Koura | Fuyuki Shimojo | Masaaki Misawa | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta | Takashi Matsubara | Shigenori Tanaka
Source: The Journal of Chemical Physics (article, 2019-09-25)
Field: Materials Science — Materials Chemistry

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag2Se First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. We present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [g(r)]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for α-Ag<sub>2</sub>Se were created as an application example, and it has been confirmed that not only g(r) and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 10<sup>4</sup> acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again. Molecular dynamics ; Computer science ; Interatomic potential ; Artificial neural network ; Convergence (economics) ; Acceleration ; Scale (ratio) ; Materials science ; Statistical physics ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Robert Masters | Nicola Stehling | Kerry J. Abrams | Vikas Kumar | Martina Azzolini | Nicola M. Pugno | Maurizio Dapor | Andreas Huber | Philip Schäfer | David G. Lidzey | Cornelia Rodenburg
Source: Advanced Science (article, 2019-01-20)
Field: Materials Science — Surfaces, Coatings and Films

Mapping Polymer Molecular Order in the SEM with Secondary Electron Hyperspectral Imaging Understanding nanoscale molecular order within organic electronic materials is a crucial factor in building better organic electronic devices. At present, techniques capable of imaging molecular order within a polymer are limited in resolution, accuracy, and accessibility. In this work, presented are secondary electron (SE) spectroscopy and secondary electron hyperspectral imaging, which make an exciting alternative approach to probing molecular ordering in poly(3-hexylthiophene) (P3HT) with scanning electron microscope-enabled resolution. It is demonstrated that the crystalline content of a P3HT film is reflected by its SE energy spectrum, both empirically and through correlation with nano-Fourier-transform infrared spectroscopy, an innovative technique for exploring nanoscale chemistry. The origin of SE spectral features is investigated using both experimental and modeling approaches, and it is found that the different electronic properties of amorphous and crystalline P3HT result in SE emission with different energy distributions. This effect is exploited by acquiring hyperspectral SE images of different P3HT films to explore localized molecular orientation. Machine learning techniques are used to accurately identify and map the crystalline content of the film, demonstrating the power of an exciting characterization technique. Hyperspectral imaging ; Nanoscopic scale ; Materials science ; Characterization (materials science) ; Amorphous solid ; Chemical imaging ; Polymer ; Spectroscopy ; Resolution (logic) ; Nanotechnology ; Chemistry ; Computer science ; Physics ; Crystallography ; Artificial intelligence Electron and X-Ray Spectroscopy Techniques ; Force Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Reza Soleimani | Amir Hossein Saeedi Dehaghani | Ali Rezai-Yazdi | Seyed Abolhassan Hosseini | Seyedeh Pegah Hosseini | Alireza Bahadori
Source: Chemical Engineering & Technology (article, 2019-11-25)
Field: Materials Science — Polymers and Plastics

Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers Abstract Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time‐consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO 2 solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO 2 solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach. Solubility ; Polymer ; Decision tree ; Compatibility (geochemistry) ; Boosting (machine learning) ; Carbon dioxide ; Gradient boosting ; Computer science ; Process engineering ; Chemistry ; Materials science ; Thermodynamics ; Biological system ; Machine learning ; Organic chemistry ; Random forest ; Engineering ; Physics ; Composite material Polymer Foaming and Composites ; Phase Equilibria and Thermodynamics ; Carbon dioxide utilization in catalysis
Authors: Tran Doan Huan | Rohit Batra | James Chapman | Chiho Kim | Anand Chandrasekaran | Rampi Ramprasad
Source: The Journal of Physical Chemistry C (article, 2019-08-09)
Field: Materials Science — Materials Chemistry

Iterative-Learning Strategy for the Development of Application-Specific Atomistic Force Fields Emerging data-driven approaches in materials science have triggered the development of numerous machine-learning force fields. In practice, they are constructed by training a statistical model on a reference database to predict potential energy and/or atomic forces. Although most of the force fields can accurately recover the properties of the training set, some of them are becoming useful for actual molecular dynamics simulations. In this work, we employ a simple iterative-learning strategy for the development of machine-learning force fields targeted at specific simulations (applications). The strategy involves (1) preparing and fingerprinting a diverse reference database of atomic configurations and forces, (2) generating a pool of machine-learning force fields by learning the reference data, (3) validating the force fields against a series of targeted applications, and (4) selectively and recursively improving the force fields that are unsuitable for a given application while keeping their performance on other applications uncompromised. We demonstrate this strategy by developing a series of machine-learning-based Al and Cu force fields that can simultaneously be used for various applications, e.g., (elastic) stress/strain analysis, stacking-fault energy calculations, and melting simulations. This strategy is also generalizable, i.e., it may be used for other materials as well. Force field (fiction) ; Computer science ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Alexa B. Hanson | Rachel Nicholls Lee | Clement Vachet | Ian J. Schwerdt | Tolga Taşdizen | Luther W. McDonald
Source: Analytical Chemistry (article, 2019-06-28)
Field: Materials Science — Materials Chemistry

Quantifying Impurity Effects on the Surface Morphology of α-U<sub>3</sub>O<sub>8</sub> The morphological effect of impurities on α-U<sub>3</sub>O<sub>8</sub> has been investigated. This study provides the first evidence that the presence of impurities can alter nuclear material morphology, and these changes can be quantified to aid in revealing processing history. Four elements: Ca, Mg, V, and Zr were implemented in the uranyl peroxide synthesis route and studied individually within the α-U<sub>3</sub>O<sub>8</sub>. Six total replicates were synthesized, and replicates 1-3 were filtered and washed with Millipore water (18.2 MΩ) to remove any residual nitrates. Replicates 4-6 were filtered but not washed to determine the amount of impurities removed during washing. Inductively coupled plasma mass spectrometry (ICP-MS) was employed at key points during the synthesis to quantify incorporation of the impurity. Each sample was characterized using powder X-ray diffraction (p-XRD), high-resolution scanning electron microscopy (HRSEM), and SEM with energy dispersive X-ray spectroscopy (SEM-EDS). p-XRD was utilized to evaluate any crystallographic changes due to the impurities; HRSEM imagery was analyzed with Morphological Analysis for MAterials (MAMA) software and machine learning classification for quantification of the morphology; and SEM-EDS was utilized to locate the impurity within the α-U<sub>3</sub>O<sub>8</sub>. All samples were found to be quantifiably distinguishable, further demonstrating the utility of quantitative morphology as a signature for the processing history of nuclear material. Chemistry ; Impurity ; Morphology (biology) ; Surface (topology) ; Crystallography ; Nanotechnology ; Geometry ; Organic chemistry Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Ce Zhou | Christian Ieritano | William Scott Hopkins
Source: Frontiers in Chemistry (review, 2019-08-07)
Field: Chemistry — Spectroscopy

Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility Evolutionary algorithms such as the basin-hopping (BH) algorithm have proven to be useful for difficult non-linear optimization problems with multiple modalities and variables. Applications of these algorithms range from characterization of molecular states in statistical physics and molecular biology to geometric packing problems. A key feature of BH is the fact that one can generate a coarse-grained mapping of a potential energy surface (PES) in terms of local minima. These results can then be utilized to gain insights into molecular dynamics and thermodynamic properties. Here we describe how one can employ concepts from unsupervised machine learning to augment BH PES searches to more efficiently identify local minima and the transition states connecting them. Specifically, we introduce the concepts of similarity indices, hierarchical clustering, and multidimensional scaling to the BH methodology. These same machine learning techniques can be used as tools for interpreting and rationalizing experimental results from spectroscopic and ion mobility investigations (e.g., spectral assignment, dynamic collision cross sections). We exemplify this in two case studies: (1) assigning the infrared multiple photon dissociation spectrum of the protonated serine dimer and (2) determining the temperature-dependent collision cross-section of protonated alanine tripeptide. Maxima and minima ; Computer science ; Cluster analysis ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Mathematics Mass Spectrometry Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics