jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilai Schwartz | Joachim Rosskopf | Simon Schmitt | Benedikt Tratzmiller | Qiong Chen | Liam P. McGuinness | Fedor Jelezko | Martin B. Plenio
Source: Scientific Reports (article, 2019-05-06)
Field: Materials Science — Materials Chemistry

Blueprint for nanoscale NMR Nitrogen vacancy (NV) centers in diamond have been used as ultrasensitive magnetometers to perform nuclear magnetic resonance (NMR) spectroscopy of statistically polarized samples at 1-100 nm length scales. However, the spectral linewidth is typically limited to the kHz level, both by the NV sensor coherence time and by rapid molecular diffusion of the nuclei through the detection volume which in turn is critical for achieving long nuclear coherence times. Here we provide a blueprint supported by detailed theoretical analysis for a set-up that combines a sensitivity sufficient for detecting NMR signals from nano- to micron-scale samples with a spectral resolution that is limited only by the nuclear spin coherence, i.e. comparable to conventional NMR. Our protocol detects the nuclear polarization induced along the direction of an external magnetic field with near surface NV centers using lock-in detection techniques to enable phase coherent signal averaging. Using the NV centers in a dual role of NMR detector and optical hyperpolarization source to increase signal to noise, and in combination with Bayesian inference models for signal processing, nano/microscale NMR spectroscopy can be performed on sample concentrations in the micromolar range, several orders of magnitude better than the current state of the art. Coherence (philosophical gambling strategy) ; Nuclear magnetic resonance ; Nuclear magnetic resonance spectroscopy ; Laser linewidth ; Physics ; Materials science ; Analytical Chemistry (journal) ; Optics ; Chemistry ; Laser Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Atomic and Subatomic Physics Research
Authors: Huiqin Ma | Wenjiang Huang | Yuanshu Jing | Stefano Pignatti | Giovanni Laneve | Yingying Dong | Huichun Ye | Linyi Liu | Anting Guo | Jing Jiang
Source: Sensors (article, 2019-12-19)
Field: Chemistry — Analytical Chemistry

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis <i>Fusarium</i> head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of <i>Fusarium</i> head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination <i>R</i><sup>2</sup> between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the <i>R</i><sup>2</sup> values and the statistically significant (<i>p</i>-value of <i>t</i>-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest <i>R</i><sup>2</sup> values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation <i>R</i> obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying <i>Fusarium</i> head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of <i>Fusarium</i> head blight in winter wheat ears. Wavelet ; Wavelet transform ; Mathematics ; Discrete wavelet transform ; Pattern recognition (psychology) ; Fusarium ; Cascade algorithm ; Artificial intelligence ; Statistics ; Computer science ; Biology ; Horticulture Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hassan Abedini | Ahmad Shariati | Mohammad Reza Khosravi‐Nikou
Source: Process Safety and Environmental Protection (article, 2019-10-18)
Field: Chemistry — Inorganic Chemistry

Adsorption of propane and propylene on M-MOF-74 (M = Cu, Co): Equilibrium and kinetic study Propane ; Adsorption ; Microporous material ; Diffusion ; Chemistry ; Selectivity ; Metal-organic framework ; Kinetics ; Propene ; Thermodynamics ; Materials science ; Physical chemistry ; Analytical Chemistry (journal) ; Chromatography ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Catalytic Processes in Materials Science
Authors: Johanna Engelke | Josef Brandt | Christopher Barner‐Kowollik | Albena Lederer
Source: Polymer Chemistry (article, 2019-01-01)
Field: Materials Science — Biomaterials

Strengths and limitations of size exclusion chromatography for investigating single chain folding – current status and future perspectives Synthetic approaches for Single-Chain Nanoparticles (SCNPs) developed rapidly during the last decade, opening a multitude of avenues for the design of functional macromolecular chains able to collapse into defined nanoparticles. However, the analytical evaluation of the SCNP formation process still requires critical improvements. Single chain ; Folding (DSP implementation) ; Nanotechnology ; Multitude ; Chain (unit) ; Size-exclusion chromatography ; Nanoparticle ; Process (computing) ; Biochemical engineering ; Computer science ; Chemistry ; Materials science ; Physics ; Engineering ; Biology ; Political science ; Organic chemistry biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies
Authors: Cheng-Han Chang | An-Che Li | Ilja Popovs | Watchareeya Kaveevivitchai | Jeng‐Lung Chen | Kai-Chun Chou | Ting-Shen Kuo | Teng‐Hao Chen
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Elucidating metal and ligand redox activities of a copper-benzoquinoid coordination polymer as the cathode for lithium-ion batteries A novel 1D copper-benzoquinoid coordination polymer with both metal and ligand redox activities delivered a capacity as high as 268 mA h g<sup>−1</sup>. Copper ; Redox ; Ligand (biochemistry) ; Lithium (medication) ; Metal ; Coordination polymer ; Inorganic chemistry ; Cathode ; Chemistry ; Polymer ; Ion ; Materials science ; Organic chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Santosh K. Gupta | Maya Abdou | Jose P. Zuniga | Alexander A. Puretzky | Yuanbing Mao
Source: ACS Omega (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Samarium-Activated La<sub>2</sub>Hf<sub>2</sub>O<sub>7</sub> Nanoparticles as Multifunctional Phosphors Recent developments in the field of designing novel nanostructures with various functionalities have pushed the scientific world to design and develop high-quality nanomaterials with multifunctional applications. Here, we propose a new kind of doped metal oxide pyrochlore nanostructure for solid-state phosphor, X-ray scintillator, and optical thermometry. The developed samarium-activated La<sub>2</sub>Hf<sub>2</sub>O<sub>7</sub> (LHOS) nanoparticles (NPs) emit a narrow and stable red emission with lower color temperature and adequate critical distance under near-UV and X-ray excitations. When the LHOS NPs are exposed to an energetic X-ray beam, the Sm<sup>3+</sup> ions situated at the symmetric environment get excited along with those located at the asymmetric environment, which results in a low asymmetry ratio of Sm<sup>3+</sup> under radioluminescence compared to photoluminescence. High activation energy and adequate thermal sensitivity of the LHOS NPs highlight their potential as a thermal sensor. Our results indicate that these Sm<sup>3+</sup>-activated La<sub>2</sub>Hf<sub>2</sub>O<sub>7</sub> NPs can serve as a multifunctional UV, X-ray, and thermographic phosphor. Phosphor ; Samarium ; Photoluminescence ; Radioluminescence ; Materials science ; Nanoparticle ; Nanomaterials ; Nanostructure ; Pyrochlore ; Solid-state lighting ; Excited state ; Nanotechnology ; Analytical Chemistry (journal) ; Scintillator ; Optoelectronics ; Inorganic chemistry ; Optics ; Chemistry ; Physics ; Atomic physics ; Light-emitting diode Luminescence Properties of Advanced Materials ; Nuclear materials and radiation effects ; Microwave Dielectric Ceramics Synthesis
Authors: Aleksander E. P. Durumeric | Gregory A. Voth
Source: The Journal of Chemical Physics (article, 2019-09-26)
Field: Materials Science — Materials Chemistry

Adversarial-residual-coarse-graining: Applying machine learning theory to systematic molecular coarse-graining We utilize connections between molecular coarse-graining (CG) approaches and implicit generative models in machine learning to describe a new framework for systematic molecular CG. Focus is placed on the formalism encompassing generative adversarial networks. The resulting method enables a variety of model parameterization strategies, some of which show similarity to previous CG methods. We demonstrate that the resulting framework can rigorously parameterize CG models containing CG sites with no prescribed connection to the reference atomistic system (termed virtual sites); however, this advantage is offset by the lack of a closed-form expression for the CG Hamiltonian at the resolution obtained after integration over the virtual CG sites. Computational examples are provided for cases in which these methods ideally return identical parameters as relative entropy minimization CG but where traditional relative entropy minimization CG optimization equations are not applicable. Granularity ; Residual ; Computer science ; Statistical physics ; Adversarial system ; Artificial intelligence ; Mathematics ; Physics ; Algorithm Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Enhanced Oil Recovery Techniques
Authors: Shi-Ping Peng | Yi Zhao
Source: Journal of Chemical Information and Modeling (article, 2019-11-11)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors Convolutional neural network (CNN) is employed to construct generative and prediction models for the design and analysis of non-fullerene acceptors (NFAs) in organic solar cells. It is demonstrated that the dilated causal CNN can be trained as a good string-based molecular generation model, and the diversity of the generated NFAs is influenced by the depth of convolutional layers. In the property prediction model, the features of NFAs are extracted from the string representations by the dilated CNN. Specially, the attention mechanism is adopted to pool the extracted information, from which the contributions of fragments to molecular properties can be obtained by calculating the corresponding weighted sum. The promising NFAs among the predicted molecules are further verified by quantum chemistry calculations. The proposed generative, prediction models and the theoretical calculations perform as a complete cycle from molecular generation and property prediction to verification, which offer a strategy for the application of CNN in material discovery. Convolutional neural network ; Computer science ; String (physics) ; Generative grammar ; Property (philosophy) ; Artificial intelligence ; Construct (python library) ; Fullerene ; Generative model ; Pattern recognition (psychology) ; Biological system ; Chemistry ; Physics ; Theoretical physics Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Organic Electronics and Photovoltaics
Authors: Ben Martineau | Duncan N. Johnstone | Antonius T. J. van Helvoort | Paul A. Midgley | Alexander S. Eggeman
Source: Advanced Structural and Chemical Imaging (article, 2019-03-15)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning applied to scanning precession electron diffraction data Abstract Scanning precession electron diffraction involves the acquisition of a two-dimensional precession electron diffraction pattern at every probe position in a two-dimensional scan. The data typically comprise many more diffraction patterns than the number of distinct microstructural volume elements (e.g. crystals) in the region sampled. A dimensionality reduction, ideally to one representative diffraction pattern per distinct element, may then be sought. Further, some diffraction patterns will contain contributions from multiple crystals sampled along the beam path, which may be unmixed by harnessing this oversampling. Here, we report on the application of unsupervised machine learning methods to achieve both dimensionality reduction and signal unmixing. Potential artefacts are discussed and precession electron diffraction is demonstrated to improve results by reducing the impact of bending and dynamical diffraction so that the data better approximate the case in which each crystal yields a given diffraction pattern. Diffraction ; Electron diffraction ; Precession ; Materials science ; Electron crystallography ; Electron backscatter diffraction ; Oversampling ; Optics ; Physics ; Condensed matter physics ; Optoelectronics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Jacob Townsend | Konstantinos D. Vogiatzis
Source: The Journal of Physical Chemistry Letters (article, 2019-06-25)
Field: Materials Science — Materials Chemistry

Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver Solving the coupled-cluster (CC) equations is a cost-prohibitive process that exhibits poor scaling with system size. These equations are solved by determining the set of amplitudes (<i>t</i>) that minimize the system energy with respect to the coupled-cluster equations at the selected level of truncation. Here, a novel approach to predict the converged coupled-cluster singles and doubles (CCSD) amplitudes, thus the coupled-cluster wave function, is explored by using machine learning and electronic structure properties inherent to the MP2 level. Features are collected from quantum chemical data, such as orbital energies, one-electron Hamiltonian, Coulomb, and exchange terms. The data-driven CCSD (DDCCSD) is not an alchemical method because the actual iterative coupled-cluster equations are solved. However, accurate energetics can also be obtained by bypassing solving the CC equations entirely. Our preliminary data show that it is possible to achieve remarkable speedups in solving the CCSD equations, especially when the correct physics are encoded and used for training of machine learning models. Coupled cluster ; Solver ; Wave function ; Hamiltonian (control theory) ; Truncation (statistics) ; Amplitude ; Physics ; System of linear equations ; Cluster (spacecraft) ; Statistical physics ; Applied mathematics ; Computer science ; Mathematics ; Quantum mechanics ; Mathematical analysis ; Mathematical optimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Hamid Soleimanzadeh | Aligholi Niaei | Dariush Salari | Ali Tarjomannejad | Simon Penner | Matthias Grünbacher | Seyed Ali Hosseini | Seyed Mahdi Mousavi
Source: Journal of Environmental Management (article, 2019-03-08)
Field: Materials Science — Materials Chemistry

Modeling and optimization of V2O5/TiO2 nanocatalysts for NH3-Selective catalytic reduction (SCR) of NOx by RSM and ANN techniques Calcination ; Nanomaterial-based catalyst ; Response surface methodology ; Catalysis ; NOx ; Materials science ; Selective catalytic reduction ; Central composite design ; Chemical engineering ; Chemistry ; Chromatography ; Engineering ; Physical chemistry ; Combustion ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Industrial Gas Emission Control
Authors: Mohammad Atif Faiz Afzal | Mojtaba Haghighatlari | Sai Prasad Ganesh | Chong Cheng | Johannes Hachmann
Source: The Journal of Physical Chemistry C (article, 2019-05-15)
Field: Materials Science — Polymers and Plastics

Accelerated Discovery of High-Refractive-Index Polyimides via <i>First-Principles</i> Molecular Modeling, Virtual High-Throughput Screening, and Data Mining We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising lead compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches. Computer science ; Virtual screening ; Throughput ; High-throughput screening ; Software ; Code (set theory) ; Feature (linguistics) ; Data mining ; Computer engineering ; Computational science ; Machine learning ; Drug discovery ; Bioinformatics Synthesis and properties of polymers ; Photochromic and Fluorescence Chemistry ; Chemical Synthesis and Analysis
Authors: Behnam Khanizadeh | Morteza Khosravi | Mohammad A. Behnajady | Ali Shamel | Behrouz Vahid
Source: Periodica Polytechnica Chemical Engineering (article, 2019-04-23)
Field: Materials Science — Materials Chemistry

Mg and La Co-doped ZnO Nanoparticles Prepared by Sol–gel Method: Synthesis, Characterization and Photocatalytic Activity In this study, La and Mg doped, and co-doped ZnO nanoparticles were prepared using the sol-gel method. The prepared samples were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), UV-Vis diffuse reflectance spectroscopy (DRS), and N2 physisorption techniques. The XRD results indicated that the prepared nanoparticles can be well adopted by the hexagonal wurtzite structure crystal and there are no second impurity peaks. Studies of the FESEM, EDX and TEM have shown that the samples have uniform spherical-like morphology with a homogenous distribution. The incorporation of La and Mg into the ZnO lattice had no effect on the morphology of the nanoparticles, but a reduction in the size of the grains (≈ 14 nm to ≈ 7 nm) was observed due to the insertion of these ions. The results of N2 physisorption indicated that there was an increase in BET surface area and pore volume for doped and co-doped samples. The results of DRS showed an increase in band gap energy and a blue shift at the absorption edge for doped and co-doped samples. The photocatalytic activity of the prepared catalysts was evaluated in the removal of RhB under UVA irradiation. The results showed that Mg5%-La5%/ZnO had the highest photoactivity (91.18 %) among all samples. Wurtzite crystal structure ; Materials science ; Photocatalysis ; Physisorption ; Transmission electron microscopy ; Doping ; Nanoparticle ; Band gap ; Diffuse reflectance infrared fourier transform ; Analytical Chemistry (journal) ; Scanning electron microscope ; Absorption edge ; Selected area diffraction ; Nuclear chemistry ; Spectroscopy ; Chemical engineering ; Nanotechnology ; Catalysis ; Chemistry ; Zinc ; Optoelectronics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques
Authors: Maya Khatun | Rajat Shubhro Majumdar | Anakuthil Anoop
Source: Frontiers in Chemistry (article, 2019-09-27)
Field: Materials Science — Materials Chemistry

A Global Optimizer for Nanoclusters We have developed an algorithm to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. While generating the trial geometries, a Tabu list is used for storing the information of the already used trial geometries to avoid using the similar trial geometries. In this recursive algorithm, an <i>n</i>-sized cluster is built from the geometries of <i>n</i>-1 clusters. The overall procedure automatically generates many unique minimum energy geometries of clusters with size from 2 up to <i>n</i> using this evolutionary growth strategy. We have used our strategy on some of the well-studied clusters such as Pd, Pt, Au, and Al homometallic clusters, Ru-Pt and Au-Pt binary clusters, and Ag-Au-Pt ternary cluster. We have analyzed some of the popular parameters to characterize the clusters, such as relative energy, singlet-triplet energy difference, binding energy, second-order energy difference, and mixing energy, and compared with the reported properties. Maxima and minima ; Nanoclusters ; Ternary operation ; Cluster (spacecraft) ; Energy (signal processing) ; Global optimization ; Tree (set theory) ; Computer science ; Binary number ; Tabu search ; Energy minimization ; Materials science ; Algorithm ; Mathematics ; Physics ; Combinatorics ; Nanotechnology ; Statistics ; Mathematical analysis ; Quantum mechanics Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies