jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stuart Chalk
Source: University of North Florida Digital Commons (University of North Florida) (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

The IUPAC Gold Book website In the past 100 years IUPAC has become well known for the development of nomenclature standards for chemicals and terminology for communication of chemically related concepts. Initially published in IUPAC's Pure and Applied Chemistry (PAC), terminology recommendations have been incorporated into the IUPAC Color Books published by the divisions. Subsequently, many terms from the color books have been incorporated into the Gold Book - the Compendium of Chemical Terminology. While the work to date as been focused on the standardization of concept (term) definitions for human use, the aggregated set of all PAC recommendations on terminology constitutes a corpus of high quality definitions for entries into an ontology for use in computer representation of chemical concepts. This is sorely needed at a time when there is a significant move toward machine learning approaches to understand science both within and outside chemistry. With it chemical data scientists can envision and apply this 'common language' into their cheminformatics work, promoting interoperability in chemical data. This paper will review the history of the PAC recommendations, the color books, and highlight how the existing guidelines for the development of terms supports the renovation of the terms for computer use. The current update to the Gold Book website will be discussed (including machine processability) as well as future ontological representation of the terms. Finally, this development will be highlighted as one of the most important in the next 100 years of IUPAC. Chemical nomenclature ; World Wide Web ; Computer science ; Chemistry History and advancements in chemistry ; Inorganic and Organometallic Chemistry ; Radioactive element chemistry and processing
Authors: Kai Huang | Fei Lv | Dongfang Wu | Zhili Wang
Source: Chemical Engineering & Technology (article, 2019-03-28)
Field: Materials Science — Materials Chemistry

Optimization of Process Conditions for Styrene Epoxidation Based on the Artificial Intelligence Method Abstract A novel prediction and optimization method based on improved generalized regression neural network (GRNN) and particle swarm optimization (PSO) algorithm is proposed to optimize the process conditions for styrene epoxidation to achieve higher yields. This model was designed to optimize the five input parameters reaction temperature and time as well as catalyst, solvent, and oxidant dosage. The output of the improved GRNN was given to the PSO algorithm to optimize the process conditions. The optimal smoothing parameter σ of GRNN was chosen from the training sample with a minimum cross validation error. Under the five optimized process conditions the maximum yield reached 95.76 %. This innovative model of improved GRNN hybrid PSO algorithm proved to be a useful tool for optimization of process conditions for styrene epoxidation. Particle swarm optimization ; Process (computing) ; Artificial neural network ; Styrene ; Smoothing ; Computer science ; Mathematical optimization ; Mean squared prediction error ; Mathematics ; Artificial intelligence ; Algorithm ; Materials science Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Reactions ; Catalysis and Oxidation Reactions
Authors: Shinjiro Yagyu | Michiko Yoshitake | Toyohiro Chikyow | Takahiro Nagata
Source: Vacuum and Surface Science (article, 2019-08-09)
Field: Materials Science — Surfaces, Coatings and Films

Validation with Measured Data of Photoelectron Yield Spectroscopy (PYS) Threshold Using Machine Learning In order to estimate the threshold of photoelectron yield spectroscopy (PYS), regression estimator was created by machine learning using calculation data based on Fowler's theory. We compared verification data with the prediction by regression estimators. The verification data measured by AC-3 instrument manufactured by Riken Keiki Co. are the number of 87 (Au : 31, Metals except for Au : 16, oxide · semiconductor : 15, organic : 25) with estimate value by analyst (label data). As a result, the prediction performance was 70% over in the range of difference between label data and estimation from ±0.2 eV. On the other hand, spectra in the difference of 0.3 eV were included in difficult labeling by analysts. The prediction by machine learning gives the guide to analysis. Estimator ; X-ray photoelectron spectroscopy ; Regression ; Yield (engineering) ; Regression analysis ; Linear regression ; Range (aeronautics) ; Statistics ; Artificial intelligence ; Analytical Chemistry (journal) ; Machine learning ; Computer science ; Mathematics ; Materials science ; Chemistry ; Physics ; Nuclear magnetic resonance Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Marian Kraus | Florian Gebert | Arne Walter | Carsten Pargmann | Frank Duschek
Source: Journal of Chemometrics (article, 2019-04-09)
Field: Chemistry — Analytical Chemistry

Online discrimination of chemical substances using standoff laser‐induced fluorescence signals Abstract Chemical contamination of objects and surfaces, caused by accident or on purpose, is a common security issue. Immediate countermeasures depend on the class of risk and consequently on the characteristics of the substances. Laser‐based standoff detection techniques can help to provide information about the thread without direct contact of humans to the hazardous materials. This article explains a data acquisition and classification procedure for laser‐induced fluorescence spectra of several chemical agents. The substances are excited from a distance of 3.5 m by laser pulses of two UV wavelengths (266 and 355 nm) with less than 0.1 mJ per laser pulse and a repetition rate of 100 Hz. Each pair of simultaneously emitted laser pulses is separated using an optical delay line. Every measurement consists of a dataset of 100 spectra per wavelength containing the signal intensities in the spectral range from 250 to 680 nm, recorded by a 32‐channel photo multiplying tube array. Based on this dataset, three classification algorithms are trained which can distinguish the samples by their single spectra with an accuracy of over 98%. These predictive models, generated with decision trees, support vector machines, and neural networks, can identify all agents (eg, benzaldehyde, isoproturon, and piperine) within the current set of substances. Laser ; Wavelength ; Spectral line ; Computer science ; Fluorescence ; Materials science ; Optics ; Biological system ; Analytical Chemistry (journal) ; Artificial intelligence ; Chemistry ; Optoelectronics ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pesticide Residue Analysis and Safety
Authors: Lauri Himanen | Amber Geurts | Adam S. Foster | Patrick Rinke
Source: Advanced Science (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019) Data-driven science is heralded as the new paradigm in materials science. Data infrastructures store vast amounts of materials data. Machine learning algorithms systematically extract knowledge from materials data streams to discover new materials for future technologies and the well-being of society. In article number 1900808, Patrick Rinke and co-worker review the current state of data-driven materials science. Data science ; State (computer science) ; Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science
Authors: Mohammad J. Eslamibidgoli | Mehrdad Mokhtari | Michael Eikerling
Source: arXiv (Cornell University) (preprint, 2019-09-23)
Field: Materials Science — Materials Chemistry

Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations The presented work demonstrates the training of recurrent neural networks (RNNs) from distributions of atom coordinates in solid state structures that were obtained using ab initio molecular dynamics (AIMD) simulations. AIMD simulations on solid state structures are treated as a multi-variate time-series problem. By referring interactions between atoms over the simulation time to temporary correlations among them, RNNs find patterns in the multi-variate time-dependent data, which enable forecasting trajectory paths and potential energy profiles. Two types of RNNs, namely gated recurrent unit and long short-term memory networks, are considered. The model is described and compared against a baseline AIMD simulation on an iridium oxide slab. Findings demonstrate that both networks can potentially be harnessed for accelerated statistical sampling in computational materials research. Recurrent neural network ; Trajectory ; Computer science ; Series (stratigraphy) ; Molecular dynamics ; Artificial neural network ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Neural Networks and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Mohammad J. Eslamibidgoli | Mehrdad Mokhtari | Michael Eikerling
Source: arXiv (Cornell University) (preprint, 2019-09-22)
Field: Materials Science — Materials Chemistry

Recurrent Neural Network-based Model for Accelerated Trajectory Analysis\n in AIMD Simulations The presented work demonstrates the training of recurrent neural networks\n(RNNs) from distributions of atom coordinates in solid state structures that\nwere obtained using ab initio molecular dynamics (AIMD) simulations. AIMD\nsimulations on solid state structures are treated as a multi-variate\ntime-series problem. By referring interactions between atoms over the\nsimulation time to temporary correlations among them, RNNs find patterns in the\nmulti-variate time-dependent data, which enable forecasting trajectory paths\nand potential energy profiles. Two types of RNNs, namely gated recurrent unit\nand long short-term memory networks, are considered. The model is described and\ncompared against a baseline AIMD simulation on an iridium oxide slab. Findings\ndemonstrate that both networks can potentially be harnessed for accelerated\nstatistical sampling in computational materials research.\n Recurrent neural network ; Trajectory ; Computer science ; Series (stratigraphy) ; Molecular dynamics ; Time series ; Artificial neural network ; Statistical physics ; Algorithm ; Artificial intelligence ; Machine learning ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Neural Networks and Applications
Authors: Troy D. Loeffler | Henry Chan | Stephen K. Gray | Subramanian K. R. S. Sankaranarayanan
Source: The Journal of Physical Chemistry A (article, 2019-04-02)
Field: Materials Science — Materials Chemistry

“Teamwork Makes the Dream Work”: Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions Crystal structure prediction has been a grand challenge in material science owing to the large configurational space that one must explore. Evolutionary (genetic) algorithms coupled with first principles calculations are commonly used in crystal structure prediction to sample the ground and metastable states of materials based on configurational energies. However, crystal structure predictions at finite temperature ( T), pressure ( P), and composition ( X) require a free-energy-based search that is often computationally expensive and tedious. Here, we introduce a new machine-learning workflow for structure prediction that is based on a concept inspired by the evolution of human tribes in primitive society. Our tribal genetic algorithm (GA) combines configurational sampling with evolutionary optimization to accurately predict entropically stabilized phases at finite ( T, P, X), at a computational cost that is an order of magnitude smaller than that required for a free-energy-based search. In a departure from standard GA techniques, the populations of individuals are divided into multiple tribes based on a bond-order fingerprint, and genetic operations are modified to ensure that cluster configurations are sampled adequately to capture entropic contributions. Team competition introduced into the evolutionary process allows winning teams (representing a better set of individuals) to expand their sizes; this translates into a more expanded search of the phase space allowing us to explore solutions near possible global minimum. Each team explores a specific section of the structural phase space and avoids bias on solutions arising from the use of individual populations in a purely energy-based search. We demonstrate the efficacy of our approach by performing the structural prediction of a representative two-dimensional two-body system as well as Lennard-Jones clusters over a range of temperatures up to its melting point. Our approach outperforms the standard GA approaches and enables structural search under "real nonambient conditions" on both bulk systems and finite-sized clusters. Crystal structure prediction ; Evolutionary algorithm ; Computer science ; Statistical physics ; Algorithm ; Artificial intelligence ; Mathematical optimization ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Hanine Hadni | Mohamed Mazigh | Menana El Hallaoui
Source: Mediterranean Journal of Chemistry (article, 2019-04-04)
Field: Chemistry — Organic Chemistry

QSAR and Molecular docking studies of 4-anilinoquinoline-triazine hybrids as pf-DHFR inhibitors &lt;p&gt;A quantitative structure-activity relationship (QSAR) investigation was performed towards 41 hybrids of 4-anilinoquinoline-triazines as potential antimalarial agents. The study was carried out using descendant multiple linear regression analyses (MLR), and artificial neural networks (ANN). Quantum chemical descriptors were calculated using DFT-B3LYP method, with the basis set 6-31G. The values obtained for the correlation coefficient of 0.87 and 0.92 by MLR and ANN, respectively, show a good predictive quality of the established model. In addition, the predicted model has been confirmed by several validation methods such as leave-one-out (LOO) cross-validation, Y-randomization, and external validation. The observed activity and the structural features of the studied molecules were further highlighted by molecular docking study on both wild and quadruple mutant type of &lt;em&gt;pf-DHFR&lt;/em&gt; protein. Furthermore, the present work deals to study the binding modes and the key protein-ligand interactions. This methodology will be used to design new antimalarial drugs.&lt;/p&gt; Chemistry ; Loo ; Quantitative structure–activity relationship ; Triazine ; Docking (animal) ; Molecular descriptor ; Linear regression ; Cross-validation ; Quantum chemical ; Stereochemistry ; Computational chemistry ; Molecule ; Computational biology ; Artificial intelligence ; Machine learning ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Computational Drug Discovery Methods ; Synthesis and biological activity
Authors: Lucy Ngo | Anton D. Nathanson | Tomasz Garbowski | Ulf Knothe | D. Zeidler | Melissa L. Knothe Tate
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-01-29)
Field: Materials Science — Surfaces, Coatings and Films

Sample preparation protocol enabling nano-to-mesoscopic mapping of cellular connectomes and their habitats in human tissues and organs Multibeam scanning electron microscopy (multiSEM) provides a technical platform for seamless nano-to-mesoscale mapping of cells in human tissues and organs, which is a major new initiative of the U.S. National Institutes of Health. Developed for rapid throughput imaging of minute defects on semiconductor wafers, multiSEM has recently been adapted for imaging of human organs, their constituent tissues, and their respective cellular inhabitants. Through integration of geospatial approaches, statistical and network modelling, advances in computing and the management of immense datasets, as well as recent developments in machine learning that enable the automation of big data analyses, multiSEM and other cross-cutting imaging technologies have the potential to exert a profound impact on elucidation of disease mechanisms, translating to improvements in human health. Here we provide a protocol for acquisition and preparation of sample specimen sizes of diagnostic relevance for human anatomy and physiology. We discuss challenges and opportunities to integrate this approach with multibeam scanning electron microscopy work flows as well as multiple imaging modalities for mapping of organ and tissue structure and function. Computer science ; Protocol (science) ; Sample (material) ; Workflow ; Artificial intelligence ; Modalities ; Nanotechnology ; Data science ; Materials science ; Medicine ; Pathology ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Fluorescence Microscopy Techniques
Authors: Jonghwan Lee
Source: IEEE Electron Device Letters (article, 2019-09-28)
Field: Materials Science — Materials Chemistry

Physics-Guided Neural Modeling for Low-Dimensional Thermoelectric Module A generic framework for combining the thermoelectric (TE) transport in low dimensional materials with neural networks is presented to advance the predictions of thermoelectric module. By training the integrand of transport integrals and implementing into circuit simulators, this approach directly predicts the TE performance under various electronic density of states (DOS) for low dimensional TE modules. This approach enables efficient modeling and produces physically meaningful results for thermoelectric properties. Thermoelectric effect ; Thermoelectric materials ; Artificial neural network ; Thermoelectric generator ; Computer science ; Electronic engineering ; Materials science ; Engineering physics ; Physics ; Engineering ; Artificial intelligence ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Alexandra Scherbart | Wiebke Timm | Sebastian Boecker | Tim W. Nattkemper
Source: PUB – Publications at Bielefeld University (Bielefeld University) (article, 2019-06-03)
Field: Chemistry — Spectroscopy

SOM-based Peptide Prototyping for Mass Spectrometry Peak Intensity Prediction In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type based on Self-Organizing Maps (SOMs) for peptide prototyping and learning locally tuned regression functions for peak intensity prediction in MALDI-TOF mass spectra. We obtain results comparable to those obtained by nu-Support Vector Regression and show how the SOM learning architecture provides a basis for peptide feature profiling and visualisation. Mass spectrometry ; Computer science ; Artificial neural network ; Visualization ; Peptide ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Mass spectrum ; Biological system ; Data mining ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Arash Kamari | Amir H. Mohammadi | Deresh Ramjugernath
Source: Petroleum Science and Technology (article, 2019-04-08)
Field: Chemistry — Analytical Chemistry

Characterization of C<sub>7+</sub> fraction properties of crude oils and gas-condensates using data driven models Petroleum fractions, in particular heptane-plus fraction (C7+), are complex mixtures and measuring properties of hydrocarbon-plus fractions is difficult and time-consuming. In this study, the normal boiling point, specific gravity and molecular weight of C7+ are estimated as a function of molecular weight, specific gravity as well as cumulative weight fractions using four soft computing strategies called least squares support vector machine, decision tree, gene expression programing, and artificial neural network. The results obtained in this study demonstrate that the developed models could be applied properly for the characterization and estimation of hydrocarbon–plus properties of crude oils and gas-condensates. Fraction (chemistry) ; Characterization (materials science) ; Crude oil ; Chemistry ; Materials science ; Petroleum engineering ; Chromatography ; Nanotechnology ; Engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; NMR spectroscopy and applications
Authors: Qinghua Wang | Feng Xu | L. Xing | Dedong Huang | Bin Wu
Source: IOP Conference Series Materials Science and Engineering (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Intelligent optimization design of specimen on sheet materials for Split Hopkinson Tensile Bar tests Abstract The Split Hopkinson Tensile Bar (SHTB) is one of the most widely used methods to study the high strain rate behavior of materials. For these experiments usually dogbone-shaped sheet specimens are used. However, there’s no agreement on the exact dimensions. In the present study, mechanism of the influence of specimen responses on accuracy of SHTB experiments was investigated with finite element program ABAQUS (Explicit). Indicators which can evaluate the measurement accuracy of specimens are proposed based on this. Orthogonal test is designed to establish the sample database for back-propagation (BP) neural network, which is adopted to fit the non-linear mapping from structure parameters to accuracy indicators of specimen. Optimal design of structure for sheet specimen is obtained with Genetic Algorithm (GA) according to the fitness of individual determined by trained and qualified BP neural network. At last, numerical simulations are adopted to verify the validity of the optimal structure for sheet specimen. The result of this study can provide recommendations for specimen design and data reliability analysis in Split Hopkinson Tensile experiments. Split-Hopkinson pressure bar ; Ultimate tensile strength ; Artificial neural network ; Bar (unit) ; Finite element method ; Materials science ; Sheet metal ; Tensile testing ; Structural engineering ; Genetic algorithm ; Sample (material) ; Reliability (semiconductor) ; Computer science ; Composite material ; Strain rate ; Engineering ; Artificial intelligence ; Machine learning High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Electromagnetic Launch and Propulsion Technology