jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Justin S. Smith | Benjamin Nebgen | R.I. Zubatyuk | Nicholas Lubbers | Christian Devereux | Kipton Barros | Sergei Tretiak | Olexandr Isayev | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2018-07-06)
Field: Materials Science — Materials Chemistry

Outsmarting Quantum Chemistry Through Transfer Learning Computer simulations are foundational to theoretical chemistry. Quantum-mechanical (QM) methods provide the highest accuracy for simulating molecules but have difficulty scaling to large systems. Empirical interatomic potentials (classical force fields) are scalable, but lack transferability to new systems and are hard to systematically improve. Automated, data-driven machine learning is close to achieving the best of both approaches. Here we use transfer learning to retrain a general purpose neural network potential, ANI-1x, on a dataset of gold standard QM calculations (CCSD(T)/CBS level) that is relatively small but designed to optimally span chemical space. The resulting potential, ANI-1ccx, approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. ANI-1ccx is broadly applicable to materials science, biology and chemistry, and billions of times faster than the parent CCSD(T)/CBS calculations. Thermochemistry ; Quantum chemistry ; Transferability ; Computer science ; Theoretical chemistry ; Scalability ; Scaling ; Quantum ; Chemical space ; Statistical physics ; Computational science ; Chemistry ; Computational chemistry ; Theoretical computer science ; Molecule ; Physics ; Drug discovery ; Physical chemistry ; Machine learning ; Quantum mechanics ; Mathematics ; Supramolecular chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: He Zhao | Yixing Wang | Anqi Lin | Bingyin Hu | Rui Yan | Jamie P. McCusker | Wei Chen | Deborah L. McGuinness | Linda S. Schadler | L. Catherine Brinson
Source: APL Materials (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

NanoMine schema: An extensible data representation for polymer nanocomposites Polymer nanocomposites consist of a polymer matrix and fillers with at least one dimension below 100 nanometers (nm) [L. Schadler et al., Jom 59(3), 53–60 (2007)]. A key challenge in constructing an effective data resource for polymer nanocomposites is building a consistent, coherent, and clear data representation of all relevant parameters and their interrelationships. The data resource must address (1) data representation for representing, saving, and accessing the data (e.g., a data schema used in a data resource such as a database management system), (2) data contribution and uploading (e.g., an MS Excel template file that users can use to input data), (3) concept and knowledge modeling in a computationally accessible form (e.g., generation of a knowledge graph and ontology), and (4) ultimately data analytics and mining for new materials discovery. This paper addresses the first three issues, paving the way for rich, nuanced data analysis. We present the NanoMine polymer nanocomposite schema as an XML-based data schema designed for nanocomposite materials data representation and distribution and discuss its relationship to a higher level polymer data core consistent with other centralized materials data efforts. We also demonstrate aspects of data entry in an accessible manner consistent with the XML schema and discuss our mapping and augmentation approach to provide a more comprehensive representation in the form of an ontology and an ontology-enabled knowledge graph framework for nanopolymer systems. The schema and ontology and their easy accessibility and compatibility with parallel material standards provide a platform for data storage and search, customized visualization, and machine learning tools for material discovery and design. Computer science ; External Data Representation ; XML ; Schema (genetic algorithms) ; Ontology ; Information retrieval ; Document Structure Description ; Database ; Data management ; Data mining ; Data science ; World Wide Web ; Artificial intelligence Machine Learning in Materials Science ; Semantic Web and Ontologies ; Data Quality and Management
Authors: Kirill Veselkov | Jonathan P. Sleeman | Emmanuelle Claude | Johannes P.C. Vissers | Dieter Galea | Anna Mróz | Ivan Laponogov | Mark Towers | Robert Tonge | Reza Mirnezami | Zoltán Takáts | Jeremy K. Nicholson | James Langridge
Source: Scientific Reports (article, 2018-02-28)
Field: Chemistry — Spectroscopy

BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI. Computer science ; Scalability ; Mass spectrometry imaging ; Big data ; Scale (ratio) ; Data mining ; Process (computing) ; Bioinformatics ; Mass spectrometry ; Artificial intelligence ; Database ; Chemistry ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: N. Huber
Source: Frontiers in Materials (article, 2018-11-26)
Field: Materials Science — Materials Chemistry

Connections Between Topology and Macroscopic Mechanical Properties of Three-Dimensional Open-Pore Materials This work addresses a number of fundamental questions regarding the topological description of materials characterized by a highly porous three-dimensional structure with bending as the major deformation mechanism. Highly efficient finite-element beam models were used for generating data on the mechanical behavior of structures with different topologies, ranging from highly coordinated bcc to Gibson–Ashby structures. Random cutting enabled a continuous modification of average coordination numbers ranging from the maximum connectivity to the percolation-cluster transition of the 3D network. The computed macroscopic mechanical properties – Young’s modulus, yield strength, and Poisson’s ratio – combined with the cut fraction, average coordination number, and statistical information on the local coordination numbers formed a database consisting of more than 100 different structures. Via data mining, the interdependencies of topological parameters and relationships between topological parameters with mechanical properties were discovered. A scaled genus density could be identified, which assumes a linear dependency on the average coordination number. Feeding statistical information about the local coordination numbers of detectable junctions with coordination number of 3 and higher to an artificial neural network enables the determination the average coordination number without any knowledge of the fully connected structure. This parameter serves as a common key for determining the cut fraction, the scaled genus density, and the macroscopic mechanical properties. The dependencies of macroscopic Young’s modulus, yield strength, and Poisson’s ratio on the cut fraction (or average coordination number) could be represented as master curves, covering a large range of structures from a coordination number of 8 (bcc reference) to 1.5, close to the percolation-cluster transition. The suggested fit functions with a single adjustable parameter agree with the numerical data within a few percent error. Artificial neural networks allow a further reduction of the error by at least a factor of 2. All data for macroscopic Young’s modulus and yield strength are covered by a single master curve. This leads to the important conclusion that the relative loss of macroscopic strength due to pinching-off of ligaments corresponds to that of macroscopic Young’s modulus. Experimental data in literature support this unexpected finding. Coordination number ; Topology (electrical circuits) ; Materials science ; Percolation (cognitive psychology) ; Cluster (spacecraft) ; Number density ; Statistical physics ; Mathematics ; Computer science ; Physics ; Combinatorics ; Thermodynamics Nanoporous metals and alloys ; Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Arvin Kakekhani | Luke T. Roling | Ambarish Kulkarni | Allegra A. Latimer | Hadi Abroshan | Julia Schumann | Hassan Aljama | Samira Siahrostami | Sohrab Ismail‐Beigi | Frank Abild‐Pedersen | Jens K. Nørskov
Source: Inorganic Chemistry (article, 2018-06-04)
Field: Materials Science — Materials Chemistry

Nature of Lone-Pair–Surface Bonds and Their Scaling Relations We investigate the (surface) bonding of a class of industrially and biologically important molecules in which the chemically active orbital is a 2 p electron lone pair located on an N or O atom bound via single bonds to H or alkyl groups. This class includes water, ammonia, alcohols, ethers, and amines. Using extensive density functional theory (DFT) calculations, we discover scaling relations (correlations) among molecular binding energies of different members of this class: the bonding energetics of a single member can be used as a descriptor for other members. We investigate the bonding mechanism for a representative (H<sub>2</sub>O) and find the most important physical surface properties that dictate the strength and nature of the bonding through a combination of covalent and noncovalent electrostatic effects. We describe the importance of surface intrinsic electrostatic, geometric, and mechanical properties in determining the extent of the lone-pair-surface interactions. We study systems including ionic materials in which the surface positive and negative centers create strong local surface electric fields, which polarize the dangling lone pair and lead to a strong "electrostatically driven bond". We emphasize the importance of noncovalent electrostatic effects and discuss why a fully covalent picture, common in the current first-principles literature on surface bonding of these molecules, is not adequate to correctly describe the bonding mechanism and energy trends. By pointing out a completely different mechanism (charge transfer) as the major factor for binding N- and O-containing unsaturated (radical) adsorbates, we explain why their binding energies can be tuned independently from those of the aforementioned species, having potential implications in scaling-driven catalyst discovery. Chemistry ; Lone pair ; Scaling ; Surface (topology) ; Stereochemistry ; Crystallography ; Computational chemistry ; Molecule ; Organic chemistry ; Geometry Graphene research and applications ; Aerogels and thermal insulation ; Machine Learning in Materials Science
Authors: Ye Sun | Kangli Wei | Qiang Liu | Leiqing Pan | Kang Tu
Source: Sensors (article, 2018-04-23)
Field: Chemistry — Analytical Chemistry

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels. Hyperspectral imaging ; Principal component analysis ; Linear discriminant analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Partial least squares regression ; Reflectivity ; Computer science ; Sample (material) ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Advanced Chemical Sensor Technologies
Authors: Samadi Samadi | Sitti Wajizah | Agus Arip Munawar
Source: Tropical Animal Science Journal (article, 2018-07-31)
Field: Chemistry — Analytical Chemistry

Rapid and Simultaneous Determination of Feed Nutritive Values by Means of Near Infrared Spectroscopy Feed plays a significant role in livestock production worldwide. Digestibility of feed such as in vitro dry matter digestibility (IVDMD) and in vitro organic matter digestibility (IVOMD) as well as the fractions of crude fiber such as neutral detergent fiber (NDF) and acid detergent fiber (ADF) can be used as indicators of feed quality. To determine these nutritive parameters, several methods based on solvent extraction and other laboratory analyses are widely employed which are normally time consuming and may cause pollution since this analysis involves chemical materials. Near infrared spectroscopy (NIRS) is one of rapid alternative methods that can be considered to be applied due to its advantages. Therefore, the main objective of this present study is to apply NIRS in determining IVDMD, IVOMD, NDF, and ADF of animal feed samples simultaneously. Reflectance spectra were acquired for animal feed samples in wavelength range from 1000 to 2500 nm. Actual quality and nutritive values were measured by standard laboratory method. Spectra data were corrected by baseline shift correction (BSC), standard normal variate (SNV), and de-trending (DT). Prediction models were developed using principal component regression. Superior results for IVDMD, IVOMD, and NDF predictions were achieved using SNV with correlation coefficient (r) and RPD index of 0.93 and 2.78 for IVDMD, 0.90 and 2.35 for IVOMD, and 0.89 and 2.31 for NDF, respectively. ADF nutritive value was predicted accurately using DT spectrum with r and RPD index of 0.94 and 3.00, respectively. It may be concluded that NIRS can be used and applied as a rapid and simultaneous method in determining nutritive values of feed samples. Neutral Detergent Fiber ; Dry matter ; Organic matter ; Animal science ; Principal component analysis ; Coefficient of determination ; Correlation coefficient ; Fiber ; Chemistry ; Mathematics ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Weiran Song | Hui Wang | Paul Maguire | Omar Nibouche
Source: Analytica Chimica Acta (article, 2018-02-06)
Field: Chemistry — Analytical Chemistry

Nearest clusters based partial least squares discriminant analysis for the classification of spectral data Linear discriminant analysis ; Partial least squares regression ; Pattern recognition (psychology) ; Artificial intelligence ; Cluster analysis ; Kernel (algebra) ; Chemistry ; Statistics ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Jia Liu | Ming Bian | Guo-Hua Gong | Di Wang | Gui-Lan Bao | YU Li-jun
Source: RSC Advances (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Current research on anti-breast cancer synthetic compounds Breast cancer (BC) is the most common cancer for females and its incidence tends to increase year by year. Breast cancer ; Incidence (geometry) ; Cancer ; Current (fluid) ; Oncology ; Internal medicine ; Medicine ; Gynecology ; Cancer research ; Mathematics ; Physics Synthesis and biological activity ; Ferrocene Chemistry and Applications ; Synthesis and Biological Evaluation
Authors: Cecilia Bottecchia | Raúl Martín | Irini Abdiaj | Ettore Crovini | Jesús Alcázar | J. Orduna | María‐Jesús Blesa | José R. Carrillo | Pilar Prieto | Timothy Noël
Source: Advanced Synthesis & Catalysis (article, 2018-12-18)
Field: Chemistry — Organic Chemistry

<i>De novo</i> Design of Organic Photocatalysts: Bithiophene Derivatives for the Visible‐light Induced C−H Functionalization of Heteroarenes Abstract Herein, we report the de novo synthesis and characterization of a series of substituted bithiophene derivatives as novel and inexpensive organic photocatalysts. DFT calculations were used to predict a priori their absorption spectra and redox potentials, which were then confirmed with empirical data. The photocatalytic activity of this novel class of organic photoredox catalyst was demonstrated in two visible‐light mediated strategies for the C−H functionalization of heteroarenes. The implementation of these strategies in a continuous‐flow photo‐microreactor afforded moderate to excellent yields within few minutes of reaction time. Due to their straightforward synthesis, low cost and good photocatalytic properties we believe that the proposed bithiophene derivatives could be employed as a new class of organic photoredox catalysts. magnified image Chemistry ; Surface modification ; Photocatalysis ; Photoredox catalysis ; Catalysis ; Microreactor ; Visible spectrum ; Organic synthesis ; Absorption (acoustics) ; Combinatorial chemistry ; Photochemistry ; Redox ; Flow chemistry ; Organic chemistry ; Physical chemistry ; Materials science Radical Photochemical Reactions ; Advanced Photocatalysis Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Brandon M. Invergo | Pedro Beltrão
Source: Essays in Biochemistry (review, 2018-08-02)
Field: Chemistry — Spectroscopy

Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks. Phosphoproteomics ; Kinome ; Phosphorylation ; Protein phosphorylation ; Signalling ; Computational biology ; Biology ; Identification (biology) ; Kinase ; Signal transduction ; Cell biology ; Protein kinase A Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Gene expression and cancer classification
Authors: Estefanía Fernández | Mercedes Boronat
Source: Journal of Physics Condensed Matter (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

Sub nanometer clusters in catalysis Sub nanometer transition metal clusters composed of a small number of atoms exhibit unexpected electronic, optical, magnetic and catalytic properties that often change substantially as a function of cluster atomicity. Several factors influence their unique catalytic behavior, including their discrete electronic structure of molecular-like orbitals and the accessibility of their low-coordinated atoms. In addition, these factors are strongly correlated so that changes in their morphology may provoke large modifications to their electronic structure and vice versa. The thermodynamic instability of clusters makes it necessary to stabilize them with protective ligands in solution or to support them on solid matrices for practical applications, which introduces non-negligible modifications into their properties. Understanding their cause and extent is the key point to potentially achieve a fine tuning of their catalytic behavior. Selected examples are discussed illustrating important points on this matter, such as the influence of cluster morphology on reactivity, the need of anchoring clusters to avoid sintering and deactivation, and the possible formation of clusters in solution or under reaction conditions, with the associated difficulty to identify them as the true active species. Cluster (spacecraft) ; Chemical physics ; Catalysis ; Reactivity (psychology) ; Atomicity ; Nanotechnology ; Chemistry ; Nanometre ; Atomic orbital ; Electronic structure ; Transition metal ; Materials science ; Computational chemistry ; Physics ; Electron ; Computer science ; Organic chemistry Nanocluster Synthesis and Applications ; Catalytic Processes in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications