jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lidia Daimiel | Víctor Micó | Rosa M. Valls | Anna Pedret | María‐José Motilva | Laura Rubió | Montserrat Fitó | Marta Farràs | María Isabel Covas | Rosa Solà | José M. Ordovás
Source: Molecular Nutrition & Food Research (article, 2020-06-20)
Field: Chemistry — Organic Chemistry

Impact of Phenol‐Enriched Virgin Olive Oils on the Postprandial Levels of Circulating microRNAs Related to Cardiovascular Disease During the postprandial state, the levels of let-7e-5p decrease with EVOO regardless of polyphenol content suggesting a general response to the fatty acid composition of EVOO or/and the presence of at least 250 mg polyphenol kg<sup>-1</sup> olive oil. Moreover, the miR-17-92 cluster increases by low and medium polyphenol content suggesting a role in fatty acid metabolism and nutrient sensing. Thus, postprandial modulation of circulating microRNAs levels could be a potential mechanism for the cardiovascular benefits associated with EVOO intake. Postprandial ; Polyphenol ; Olive oil ; Phenols ; Chemistry ; Food science ; Fatty acid ; Phenol ; Biochemistry ; Internal medicine ; Medicine ; Organic chemistry Edible Oils Quality and Analysis ; Fatty Acid Research and Health ; MicroRNA in disease regulation
Authors: Nesrine Mechi | Abdulrahman Mallah | Sobhi Hcini | Mohamed Lamjed Bouazizi | Michel Boudard | Abdessalem Dhahri
Source: Journal of Superconductivity and Novel Magnetism (article, 2020-01-10)
Field: Materials Science — Materials Chemistry

Effects of Sintering Temperature on Microstructural, Magnetic, and Impedance Spectroscopic Properties of Ni0.4Cd0.3Zn0.3Fe2O4 Ferrites Materials science ; Sintering ; Rietveld refinement ; Grain boundary ; Coercivity ; Remanence ; Spinel ; Electrical resistivity and conductivity ; Condensed matter physics ; Grain size ; Dielectric spectroscopy ; Magnetization ; Nyquist plot ; Nuclear magnetic resonance ; Analytical Chemistry (journal) ; Composite material ; Metallurgy ; Crystal structure ; Microstructure ; Crystallography ; Magnetic field ; Chemistry ; Electrical engineering Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Magneto-Optical Properties and Applications
Authors: Jorge Alfredo Ardila‐Rey | Matías Patricio Cerda-Luna | Rodrigo Andres Rozas-Valderrama | Bruno Albuquerque de Castro | André Luiz Andreoli | Firdaus Muhammad‐Sukki
Source: IEEE Access (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Separation Techniques of Partial Discharges and Electrical Noise Sources: A Review of Recent Progress Partial discharge (PD) monitoring is one of the most used tools for diagnosing the condition of electrical equipment and machines that operate normally at high voltage levels. Ideally, PD identification can be easily done if there is a single source acting over the electrical asset during the measurement. However, in industrial environments, it is common to find the presence of multiple sources acting simultaneously, which hinders the identification process, due to sources of greater amplitude hiding the presence of other types of sources of lesser amplitude that could eventually be much more harmful to the insulation system. In this sense, the separation of PD through the use of clustering techniques allows individual source recognition once they have been clearly separated. This article describes the main clustering techniques that have been used over time to separate PD sources and electrical noise. The results obtained by the different authors in the utilization of each technique demonstrates good performance in terms of separation. Separation (statistics) ; Noise (video) ; Computer science ; Electrical engineering ; Engineering ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrohydrodynamics and Fluid Dynamics
Authors: Matthew L. Nisbet | Ian M. Pendleton | Gene M. Nolis | Kent J. Griffith | Joshua Schrier | Jordi Cabana | Alexander J. Norquist | Kenneth R. Poeppelmeier
Source: Journal of the American Chemical Society (article, 2020-04-01)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Assisted Synthesis of Polar Racemates Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)<sub>2</sub>(H<sub>2</sub>O)]<sub>2</sub>[MF<sub>6</sub>]<sub>2</sub>·3H<sub>2</sub>O (M = Ti, Zr, Hf; bpy = 2,2'-bipyridine) family (space group: <i>Pna</i>2<sub>1</sub>). In each (CuO, MO<sub>2</sub>)/bpy/HF(<i>aq</i>) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H<sub>2</sub>O)<sub>2</sub><sup>2+</sup> and MF<sub>6</sub><sup>2-</sup> basic building units (space groups: Ti-CS (<i>Pnma</i>), Zr-CS (<i>P</i>1̅), Hf-CS (<i>P</i>2/<i>n</i>)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1<i>s</i> X-ray absorption spectra reveal the strong ligand-to-metal π bonding between Ti(3<i>d</i> - <i>t</i><sub><i>2g</i></sub>) and F(2<i>p</i>) states that distinguishes the TiF<sub>6</sub><sup>2-</sup> anion from the ZrF<sub>6</sub><sup>2-</sup> and HfF<sub>6</sub><sup>2-</sup> anions. Chemistry ; Polar Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Chemical Synthesis and Reactions
Authors: Teppei Fukuya | Yasushi Shibuta
Source: Computational Materials Science (article, 2020-06-20)
Field: Materials Science — Materials Chemistry

Machine learning approach to automated analysis of atomic configuration of molecular dynamics simulation Three-dimensional convolutional neural network (3D-CNN) is employed for automated analysis of atomic configuration of molecular dynamics (MD) simulation. Solid and liquid atoms in the solid-liquid biphasic system of various elements at high temperature are identified by a 3D-CNN architecture. Accuracy of 3D-CNN successfully achieves more than 90% independent of crystal structure, whereas accuracy of common neighbor analysis (CNA) is approximately 50% at most for the same system. 3D-CNN can extract the morphology of solid-liquid interface very clearly including roughness at atomistic scale. Moreover, 3D-CNN trained by the data set of a certain element (iron) can be applied for another element (tungsten) of same crystal structure without further training. It is significant in this study to shed light on a high potential of machine learning (ML)-based approach for automated analysis of atomistic configuration since it is not straightforward to develop an identifier of atomic configuration manually when we face a new problem out of existing methodologies. Molecular dynamics ; Convolutional neural network ; Computer science ; Artificial neural network ; Set (abstract data type) ; Identifier ; Artificial intelligence ; Computational science ; Biological system ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Camilo Augusto Fernandes Salvador | Bruno Fedosse Zornio | Caetano R. Miranda
Source: ACS Applied Materials & Interfaces (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations The discovery of low-modulus Ti alloys for biomedical applications is challenging due to a vast number of compositions and available solute contents. In this work, machine learning (ML) methods are employed for the prediction of the bulk modulus (<i>K</i>) and the shear modulus (<i>G</i>) of optimized ternary alloys. As a starting point, the elasticity data of more than 1800 compounds from the Materials Project fed linear models, random forest regressors, and artificial neural networks (NN), with the aims of training predictive models for <i>K</i> and <i>G</i> based on compositional features. The models were then used to predict the resultant Young modulus (<i>E</i>) for all possible compositions in the Ti-Nb-Zr system, with variations in the composition of 2 at. %. Random forest (RF) predictions of <i>E</i> deviate from the NN predictions by less than 4 GPa, which is within the expected variance from the ML training phase. RF regressors seem to generate the most reliable models, given the selected target variables and descriptors. Optimal compositions identified by the ML models were later investigated with the aid of special quasi-random structures (SQSs) and density functional theory (DFT). According to a combined analysis, alloys with 22 Zr (at. %) are promising structural materials to the biomedical field, given their low elastic modulus and elevated beta-phase stability. In alloys with Nb content higher than 14.8 (at. %), the beta phase has lower energy than omega, which may be enough to avoid the formation of omega, a high-modulus phase, during manufacturing. Materials science ; Ternary operation ; Shear modulus ; Bulk modulus ; Modulus ; Random forest ; Titanium alloy ; Artificial neural network ; Omega ; Elastic modulus ; Thermodynamics ; Machine learning ; Computer science ; Composite material ; Alloy ; Physics Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Jiechun Liang | Shuqian Ye | Tianshu Dai | Ziyue Zha | Yuechen Gao | Xi Zhu
Source: Scientific Data (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

QM-symex, update of the QM-sym database with excited state information for 173 kilo molecules In the research field of material science, quantum chemistry database plays an indispensable role in determining the structure and properties of new material molecules and in deep learning in this field. A new quantum chemistry database, the QM-sym, has been set up in our previous work. The QM-sym is an open-access database focusing on transition states, energy, and orbital symmetry. In this work, we put forward the QM-symex with 173-kilo molecules. Each organic molecular in the QM-symex combines with the C<sub>n</sub>h symmetry composite and contains the information of the first ten singlet and triplet transitions, including energy, wavelength, orbital symmetry, oscillator strength, and other quasi-molecular properties. QM-symex serves as a benchmark for quantum chemical machine learning models that can be effectively used to train new models of excited states in the quantum chemistry region as well as contribute to further development of the green energy revolution and materials discovery. QM/MM ; Excited state ; Database ; State (computer science) ; Molecule ; Physics ; Computer science ; Chemistry ; Atomic physics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Nikolay Borodinov | Wan‐Yu Tsai | Vladimir V. Korolkov | Nina Balke | Sergei V. Kalinin | Olga S. Ovchinnikova
Source: Applied Physics Letters (article, 2020-01-27)
Field: Materials Science — Materials Chemistry

Machine learning-based multidomain processing for texture-based image segmentation and analysis Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique. Segmentation ; Computer science ; Artificial intelligence ; Orientation (vector space) ; Scalability ; Image processing ; Image segmentation ; Texture (cosmology) ; Feature extraction ; Machine learning ; Pattern recognition (psychology) ; Atomic force microscopy ; Materials science ; Image (mathematics) ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Force Microscopy Techniques and Applications
Authors: Aleks Reinhardt | Chris J. Pickard | Bingqing Cheng
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Predicting the phase diagram of titanium dioxide with random search and pattern recognition Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous free-energy calculations to account for entropic effects at finite temperatures. Here, we develop a framework that facilitates such predictions by exploiting all the information obtained from random searches of crystal structures. This framework combines automated clustering, classification and visualisation of crystal structures with machine-learning estimation of their enthalpy and entropy. We demonstrate the framework on the technologically important system of TiO<sub>2</sub>, which has many polymorphs, without relying on prior knowledge of known phases. We find a number of new phases and predict the phase diagram and metastabilities of crystal polymorphs at 1600 K, benchmarking the results against full free-energy calculations. Crystal structure prediction ; Phase diagram ; Maxima and minima ; Crystal (programming language) ; Crystal structure ; Enthalpy ; Phase (matter) ; Benchmarking Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Solidification and crystal growth phenomena
Authors: Kevin Kaufmann | Chaoyi Zhu | Alexander S. Rosengarten | Kenneth S. Vecchio
Source: Microscopy and Microanalysis (article, 2020-05-14)
Field: Materials Science — Materials Chemistry

Deep Neural Network Enabled Space Group Identification in EBSD Electron backscatter diffraction (EBSD) is one of the primary tools in materials development and analysis. The technique can perform simultaneous analyses at multiple length scales, providing local sub-micron information mapped globally to centimeter scale. Recently, a series of technological revolutions simultaneously increased diffraction pattern quality and collection rate. After collection, current EBSD pattern indexing techniques (whether Hough-based or dictionary pattern matching based) are capable of reliably differentiating between a "user selected" set of phases, if those phases contain sufficiently different crystal structures. EBSD is currently less well suited for the problem of phase identification where the phases in the sample are unknown. A pattern analysis technique capable of phase identification, utilizing the information-rich diffraction patterns potentially coupled with other data, such as EDS-derived chemistry, would enable EBSD to become a high-throughput technique replacing many slower (X-ray diffraction) or more expensive (neutron diffraction) methods. We utilize a machine learning technique to develop a general methodology for the space group classification of diffraction patterns; this is demonstrated within the $\lpar 4/m\comma \;\bar{3}\comma \;\;2/m\rpar$ point group. We evaluate the machine learning algorithm's performance in real-world situations using materials outside the training set, simultaneously elucidating the role of atomic scattering factors, orientation, and pattern quality on classification accuracy. Electron backscatter diffraction ; Diffraction ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Identification (biology) ; Neutron diffraction ; Set (abstract data type) ; Optics ; Materials science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Seokho Kang | Youngchun Kwon | Dongseon Lee | Youn-Suk Choi
Source: Journal of Chemical Information and Modeling (article, 2020-07-21)
Field: Chemistry — Spectroscopy

Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations Recently, machine learning has been successfully applied to the prediction of nuclear magnetic resonance (NMR) chemical shifts. To build a prediction model, the existing methods require a training data set that comprises molecules whose NMR-active atoms are annotated with their chemical shifts. However, the laborious task of atomic-level annotation must be manually conducted by chemists. Thus, it becomes difficult to perform large-scale annotation. To address this issue, we propose a weakly supervised learning method to enable the predictive modeling of NMR chemical shifts without requiring explicit atomic-level annotations in the training data set. For the training data set, the proposed method only requires the annotation of chemical shifts at the molecular level. As a prediction model, we build a message passing neural network (MPNN) that predicts the chemical shifts of individual NMR-active atoms in a molecule. Using a loss function that is invariant to the permutation of atoms in a molecule, the model is trained in a weakly supervised manner to minimize the molecular-level difference between a set of predicted chemical shifts and the corresponding set of actual chemical shifts across the training data set. Accordingly, during the training, the chemical shifts predicted by the model are approximately aligned with the actual chemical shifts in a data-driven fashion. The proposed method performs comparably to the existing fully supervised methods in terms of predicting the chemical shifts of <sup>1</sup>H and <sup>13</sup>C NMR spectra for small molecules. Chemical shift ; Test set ; Set (abstract data type) ; Training set ; Computer science ; Data set ; Artificial intelligence ; Machine learning ; Annotation ; Pattern recognition (psychology) ; Chemistry ; Physical chemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Haidi Wang | Yuzhi Zhang | Linfeng Zhang | Han Wang
Source: Frontiers in Chemistry (article, 2020-11-26)
Field: Materials Science — Materials Chemistry

Crystal Structure Prediction of Binary Alloys via Deep Potential Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum-magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg<sub>12</sub>Al<sub>8</sub> shows excellent ductility and Mg<sub>5</sub>Al<sub>27</sub> has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with <i>ab initio</i> accuracy can be dramatically reduced. Crystal structure prediction ; Convex hull ; Interatomic potential ; Density functional theory ; Ab initio ; Benchmark (surveying) ; Particle swarm optimization ; Computer science ; Algorithm ; Crystal structure ; Materials science ; Crystal (programming language) ; Molecular dynamics ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Regular polygon ; Crystallography ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Chaitanya M. Daksha | Jejoon Yeon | Sanjib C. Chowdhury | John W. Gillespie
Source: Computational Materials Science (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Automated ReaxFF parametrization using machine learning ReaxFF ; Parametrization (atmospheric modeling) ; Crossover ; Artificial neural network ; Surrogate model ; Computer science ; Genetic algorithm ; Fitness function ; Pareto principle ; Gaussian ; Algorithm ; Mathematical optimization ; Molecular dynamics ; Artificial intelligence ; Machine learning ; Mathematics ; Interatomic potential ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Alireza Nazarahari | D. Canadinç
Source: Materials Chemistry and Physics (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence Shape-memory alloy ; Nickel titanium ; SMA* ; Corrosion ; Materials science ; Composition (language) ; Artificial neural network ; Computer science ; Composite material ; Artificial intelligence Shape Memory Alloy Transformations ; Dental materials and restorations