jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Avilene Rodríguez‐Lara | María Dolores Mesa | Jerónimo Aragón‐Vela | Rafael A. Casuso | Cristina Casals | Jesús Martín Zúñiga | Jesús R. Huertas
Source: Nutrients (article, 2019-09-06)
Field: Chemistry — Organic Chemistry

Acute/Subacute and Sub-Chronic Oral Toxicity of a Hidroxytyrosol-Rich Virgin Olive Oil Extract The objective of this study was to determine the acute (one single dose), subacute (14 days), and sub-chronic (90 days) toxicity of an aqueous virgin olive oil (VOO) extract rich in hydroxytyrosol in rats. For acute/subacute toxicity, rats were divided into three groups. The control group received distilled water (<i>n</i> = 9), another experimental group received a single dose of 300 mg/kg (<i>n</i> = 3), and a third group received one dose of 2000 mg/kg (<i>n</i> = 4) during 14 days. The sub-chronic study included 60rats distributed in three groups (<i>n</i> = 20: 10 males and 10 females) receiving daily different three doses of the VOO extract in the drinking water during 90 days: (1) 100 mg/kg, (2) 300 mg/kg, and (3) 1000 mg/kg. In parallel, a fourth additional group (<i>n</i> = 20: 10 males and 10 females) did not receive any extract (control group). Clinical signs, body weight, functional observations of sensory and motor reactivity, hematological and biochemical analyses, and macroscopic and microscopic histopathology were evaluated. No adverse effects were observed after the administration of the different doses of the hydroxytyrosol-rich VOO extract, which suggests that the enrichment of VOO in its phenolic compound is safe, and can be used as functional foods for the treatment of chronic degenerative diseases. Hydroxytyrosol ; Olive oil ; Chronic toxicity ; Toxicity ; Distilled water ; Histopathology ; Acute toxicity ; Medicine ; Body weight ; Adverse effect ; Chemistry ; Physiology ; Internal medicine ; Food science ; Pathology ; Biochemistry ; Polyphenol ; Antioxidant Edible Oils Quality and Analysis ; Antioxidant Activity and Oxidative Stress ; Cholesterol and Lipid Metabolism
Authors: Boodsarin Sawatlon | Matthew D. Wodrich | Benjamin Meyer | Alberto Fabrizio | Clémence Corminbœuf
Source: ChemCatChem (article, 2019-04-30)
Field: Materials Science — Materials Chemistry

Data Mining the C−C Cross‐Coupling Genome Abstract The speed and precision of machine‐learning (ML) techniques in determining quantum chemical properties has resulted in a considerable computational speed up in comparison to traditional quantum chemical methods, and now allows a desired property of thousands of molecules to be assessed virtually instantaneously. The large databases that result from employing ML can, in turn, be mined with the goal of uncovering relationships that may be missed through more commonly used small scale screening procedures. Due to its prominent place in chemistry, catalysis represents a particularly fruitful playground, where drawing connections between the quantum chemical properties of catalysts and their overall catalytic performance may lead to the identification of new, highly functional species. In this spirit, we previously trained ML models to predict the performance of 18000 prospective catalysts for a Suzuki coupling reaction using molecular volcano plots. Here, we apply concepts from big data to probe a type of “C−C cross‐coupling genome” that explores results from many different named cross‐coupling reactions. The use of interactive dimensionality‐reducing data‐clustering maps facilitates the identification of relationships between the thermodynamics of different catalysts and the chemical properties of their constituent metal and ligands. Analyzing large numbers of species in this manner leads to the identification of not only unexpected catalysts that have thermodynamically ideal profiles to catalyze C−C cross‐coupling reactions, but also reveals a wealth of interesting chemical trends regarding the influence played by different metals and ligands, as well as their unique combinations. Catalysis ; Identification (biology) ; Curse of dimensionality ; Quantum chemical ; Coupling (piping) ; Cluster analysis ; Molecule ; Quantum ; Chemistry ; Chemical physics ; Chemical reaction ; Computer science ; Biochemical engineering ; Biological system ; Materials science ; Machine learning ; Physics ; Organic chemistry ; Quantum mechanics ; Biology Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Complex Network Analysis Techniques
Authors: Suisui Chen | Tao Liu | Degang Xu | Yan Huo | Yixuan Yang
Source:  (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Image based Measurement of Population Growth Rate for L-Glutamic Acid Crystallization For online monitoring of crystal population growth quality during the L-glutamic acid crystallization process, an efficient image analysis method is proposed to detect the crystal population growth rate in real time, based on a non-invasive imaging system. A trained weights network model is established for real-time analysis of in-situ captured images, by using a deep-learning based image segmentation algorithm named Mask R-CNN (Regional Convolutional Neural Network), which is capable of accurate image segmentation and classification of crystals in blurred images. Correspondingly, crystal size measurements are conducted for each crystal image in terms of elliptical fitting. Then, density estimation with Gaussian kernel function is used to smooth the numerically computed crystal size distribution (CSD) for evaluation. By introducing two indices for describing crystal population properties, namely symmetric variant of relative entropy (SVRE) and CSD dispersion, a realtime computation method is given to estimate the crystal population growth rate. Experimental results on the cooling crystallization of β form L-glutamic acid well demonstrate the effectiveness of the proposed image analysis method. Artificial intelligence ; Image segmentation ; Population ; Computer science ; Gaussian function ; Kernel (algebra) ; Crystallization ; Pattern recognition (psychology) ; Convolutional neural network ; Crystal (programming language) ; Image (mathematics) ; Algorithm ; Gaussian ; Mathematics ; Physics Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses
Authors: Beatriz von der Esch | Johannes C. B. Dietschreit | Laurens D. M. Peters | Christian Ochsenfeld
Source: Journal of Chemical Theory and Computation (article, 2019-11-25)
Field: Materials Science — Materials Chemistry

Finding Reactive Configurations: A Machine Learning Approach for Estimating Energy Barriers Applied to Sirtuin 5 Sirtuin 5 is a class III histone deacetylase that, unlike its classification, mainly catalyzes desuccinylation and demanoylation reactions. It is an interesting drug target that we use here to test new ideas for calculating reaction pathways of large molecular systems such as enzymes. A major issue with most schemes (e.g., adiabatic mapping) is that the resulting activation barrier height heavily depends on the chosen educt conformation. This makes the selection of the initial structure decisive for the success of the characterization. Here, we apply machine learning to a large number of molecular dynamics frames and potential energy barriers obtained by quantum mechanics/molecular mechanics calculations in order to identify (1) suitable start-conformations for reaction path calculations and (2) structural features relevant for the first step of the desuccinylation reaction catalyzed by Sirtuin 5. The latter generally aids the understanding of reaction mechanisms and important interactions in active centers. Using our novel approach, we found eleven key features that govern the reactivity. We were able to estimate reaction barriers with a mean absolute error of 3.6 kcal/mol and identified reactive configurations. Sirtuin ; Molecular mechanics ; Molecular dynamics ; Computer science ; Adiabatic process ; Histone deacetylase ; Chemistry ; Reactive intermediate ; Reactivity (psychology) ; Computational chemistry ; Histone ; Computational biology ; Enzyme ; Catalysis ; Physics ; Biochemistry ; Biology ; Medicine ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Leigh Weston | Vahe Tshitoyan | John Dagdelen | Olga Kononova | Kristin A. Persson | Gerbrand Ceder | Anubhav Jain
Source: ChemRxiv (preprint, 2019-06-05)
Field: Materials Science — Materials Chemistry

Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature &lt;div&gt;&lt;div&gt;&lt;div&gt;Over the past decades, the number of published materials science articles has increased manyfold. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw-text of published articles onto a structured database entry that allows for programmatic querying. To this end, we apply text-mining with named entity recognition (NER), along with entity normalization, for large-scale information extraction from the published materials science literature. The NER is based on supervised machine learning with a recurrent neural network architecture, and the model is trained to extract summary-level information from materials science documents, including: inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifer, with an overall accuracy (f1) of 87% on a test set, is applied to information extraction from 3.27 million materials science abstracts - the most information-dense section of published articles.&lt;/div&gt;&lt;div&gt;Overall, we extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. Our database shows far greater recall in document retrieval when compared to traditional text-based searches due to an entity normalization procedure that recognizes synonyms. We demonstrate that simple database queries can be used to answer complex \meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. All of our data has been made freely available for bulk download; we have also made a public facing application programming interface (https://github.com/materialsintelligence/matscholar) and website http://matscholar.herokuapp.com/search for easy interfacing with the data, trained models and functionality described in this paper. These results will allow researchers to access targeted information on a scale and with a speed that has not been previously available, and can be expected to accelerate the pace of future materials science discovery.&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Named-entity recognition ; Bottleneck ; Computer science ; Normalization (sociology) ; Information retrieval ; Bridging (networking) ; Information extraction ; Pipeline (software) ; Natural language processing ; Raw data ; Artificial intelligence ; Task (project management) Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: John Thomas | Jonathon S. Bechtel | Anirudh Raju Natarajan | Anton Van der Ven
Source: Physical review. B./Physical review. B (article, 2019-10-02)
Field: Materials Science — Materials Chemistry

Machine learning the density functional theory potential energy surface for the inorganic halide perovskite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">CsPbBr</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> The temperature and pressure dependence of structural phase transitions determine the structure-functionality relationships in many technologically important materials. Harmonic Hamiltonians have proven successful in predicting the vibrational properties of many materials. However, they are inadequate for modeling structural phase transitions in crystals with potential energy surfaces that are either strongly anharmonic or nonconvex with respect to collective atomic displacements or homogeneous strains. In this paper we develop a framework to express highly anharmonic first-principles potential energy surfaces as polynomials of collective cluster deformations. We further adapt the approach to a nonlinear extension of the cluster expansion formalism through the use of an artificial neural net model. The machine learning models are trained on a large database of first-principles calculations and are shown to reproduce the potential energy surface with low error. Anharmonicity ; Potential energy surface ; Phase transition ; Density functional theory ; Cluster (spacecraft) ; Artificial neural network ; Potential energy ; Nonlinear system ; Halide ; Statistical physics ; Ab initio ; Materials science ; Machine learning ; Computer science ; Physics ; Algorithm ; Chemistry ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Nils Zimmermann | Anubhav Jain
Source: ChemRxiv (preprint, 2019-05-03)
Field: Materials Science — Materials Chemistry

Local Structure Order Parameters and Site Fingerprints for Quantification of Coordination Environment and Crystal Structure Similarity Structure characterization and classification is frequently based on local environment information of all or selected atomic sites in the crystal structure. Therefore, reliable and robust procedures to find coordinated neighbors and to evaluate the resulting coordination pattern (e.g., tetrahedral, square planar) are critically important for both traditional and machine learning approaches that aim to exploit site or structure information for predicting materials properties. Here, we introduce new local structure order parameters (LoStOPs) that are specifically designed to rapidly detect rotationally symmetric (e.g., tetrahedron) as well as asymmetric local coordination environments (e.g., square pyramid). Furthermore, we introduce a Monte Carlo optimization approach to ensure that the different LoStOPs are comparable with each other. We then apply the new local environment descriptors to define site and structure fingerprints and to measure similarity between 61 known coordination environments and 40 commonly studied crystal structures, respectively. After extensive testing and optimization, we determine the most accurate structure similarity assessment procedure to compute all 2.45 billion structure similarities between each pair of the ≈ 70,000 materials that are currently present in the Materials Project database.&lt;br&gt; Similarity (geometry) ; Tetrahedron ; Crystal structure ; Computer science ; Pyramid (geometry) ; Local structure ; Monte Carlo method ; Planar ; Data mining ; Coordination number ; Artificial intelligence ; Algorithm ; Mathematics ; Crystallography ; Chemistry ; Geometry ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Jordan Hoffmann | Louis Maestrati | Yoshihide Sawada | Jian Tang | Jean Michel Sellier | Yoshua Bengio
Source: arXiv (Cornell University) (preprint, 2019-09-03)
Field: Materials Science — Materials Chemistry

Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative models have led to rapid progress in automated drug discovery. Many of the current methods focus on either 1-D or 2-D representations of typically small, drug-like molecules. However, many molecules require 3-D descriptors and exceed the chemical complexity of commonly used dataset. We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50,000 stable crystal unit cells that vary from containing 1 to over 100 atoms. We construct a smooth and continuous 3-D density representation of each crystal based on the positions of different atoms. Two different neural networks were trained on a dataset of over 120,000 three-dimensional samples of single and repeating crystal structures, made by rotating the single unit cells. The first, an Encoder-Decoder pair, constructs a compressed latent space representation of each molecule and then decodes this description into an accurate reconstruction of the input. The second network segments the resulting output into atoms and assigns each atom an atomic number. By generating compressed, continuous latent spaces representations of molecules we are able to decode random samples, interpolate between two molecules, and alter known molecules. Encoding (memory) ; Decoding methods ; Representation (politics) ; Computer science ; Encoder ; ENCODE ; Crystal (programming language) ; Molecule ; Decodes ; Generative model ; Focus (optics) ; Atom (system on chip) ; Algorithm ; Generative grammar ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pìotr Bregestovski | Galyna Maleeva
Source: Neuroscience and Behavioral Physiology (review, 2019-01-31)
Field: Materials Science — Materials Chemistry

Photopharmacology: A Brief Review Using the Control of Potassium Channels as an Example Photochromism ; Chemistry ; Ion channel ; Biophysics ; Neuroscience ; Glutamate receptor ; Receptor ; Biochemistry ; Biology ; Photochemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Neurobiology and Insect Physiology Research
Authors: Giulia S. da Silva | Luiza P. de Oliveira | Gabriel F. Costa | Gabriela F. Giordano | Caroline Y. N. Nicoliche | Alexandre Ataíde da Silva | Latif Ullah Khan | Gabriela H. Da Silva | Ângelo L. Gobbi | J. V. Silveira | A. G. Souza Filho | Gabriel R. Schleder | A. Fazzio | Diego Stéfani T. Martinez | Renato S. Lima
Source: Sensors and Actuators B Chemical (article, 2019-11-28)
Field: Chemistry — Electrochemistry

Ordinary microfluidic electrodes combined with bulk nanoprobe produce multidimensional electric double-layer capacitances towards metal ion recognition Cleanroom ; Microfluidics ; Materials science ; Capacitance ; Scalability ; Fabrication ; Electrode ; Nanotechnology ; Computer science ; Optoelectronics ; Analytical Chemistry (journal) ; Chemistry ; Chromatography Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Ying‐Yan Jiang | Hua Su | 韦玮 Wei Wei | Yongjie Wang | Hong‐Yuan Chen | Wei Wang
Source: Proceedings of the National Academy of Sciences (article, 2019-03-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tracking the rotation of single CdS nanorods during photocatalysis with surface plasmon resonance microscopy While rotational dynamics of anisotropic nanoobjects has often been limited in plasmonic and fluorescent nanomaterials, here we demonstrate the capability of a surface plasmon resonance microscopy (SPRM) to determine the orientation of all kinds of anisotropic nanomaterials. By taking CdS nanorods as an example, it was found that two-dimensional Fourier transform of the asymmetrical wave-like SPRM image resulted in a peak in its angular spectrum in <i>k</i> space. Consistency between the peak angle and the geometrical orientation of the nanorod was validated by both in situ scanning electron microscope characterizations and theoretical calculations. Real-time monitoring of the rotational dynamics of single CdS nanorods further revealed the accelerated rotation under appropriate reaction conditions for photocatalyzed hydrogen generation. The driving force was attributed to the asymmetric production of hydrogen molecules as a result of inhomogeneous distribution of reactive sites within the nanorod. The present work not only builds the experimental and theoretical connections between the orientation of anisotropic nanomaterials and its SPRM images; the general suitability of SPRM also sheds light on broad types of nonfluorescent and nonplasmonic anisotropic nanoobjects from semiconductors to bacteria and viruses. Nanorod ; Materials science ; Nanomaterials ; Surface plasmon resonance ; Anisotropy ; Plasmon ; Microscopy ; Nanotechnology ; Optics ; Optoelectronics ; Nanoparticle ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Plasmonic and Surface Plasmon Research
Authors: Kristian Berland | Nina Shulumba | Olle Hellman | Clas Persson | Ole Martin Løvvik
Source: Journal of Applied Physics (article, 2019-10-08)
Field: Materials Science — Materials Chemistry

Thermoelectric transport trends in group 4 half-Heusler alloys The thermoelectric properties of 54 different group 4 half-Heusler (HH) alloys have been studied from first principles. Electronic transport was studied with density functional theory using hybrid functionals facilitated by the k⋅p method, while the temperature-dependent effective potential method was used for the phonon contributions to the figure of merit ZT. The phonon thermal conductivity was calculated including anharmonic phonon-phonon, isotope, alloy, and grain-boundary scattering. HH alloys have an XYZ composition, and those studied here are in the group 4-9-15 (Ti,Zr,Hf)(Co,Rh,Ir)(As,Sb,Bi) and group 4-10-14 (Ti,Zr,Hf)(Ni,Pd,Pt)(Ge,Sn,Pb). The electronic part of the thermal conductivity was found to significantly impact ZT and thus the optimal doping level. Furthermore, the choice of functional was found to significantly affect thermoelectric properties, particularly for structures exhibiting band alignment features. The intrinsic thermal conductivity was significantly reduced when alloy and grain-boundary scattering were accounted for, which also reduced the spread in thermal conductivity. It was found that sublattice disorder on the Z-site, i.e., the site occupied by group 14 or 15 elements, was more effective than X-site substitution, occupied by group 4 elements. The calculations confirmed that ZrNiSn, ZrCoSb, and ZrCoBi based alloys display promising thermoelectric properties. A few other n-type and p-type compounds were also predicted to be potentially excellent thermoelectric materials, given that sufficiently high charge carrier concentrations can be achieved. This study provides insight into the thermoelectric potential of HH alloys and casts light on strategies to optimize the thermoelectric performance of multicomponent alloys. Thermoelectric effect ; Thermal conductivity ; Thermoelectric materials ; Phonon scattering ; Density functional theory ; Phonon ; Anharmonicity ; Doping Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Topological Materials and Phenomena