jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Silvan Käser | Oliver T. Unke | Markus Meuwly
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-04-14)
Field: Chemistry — Spectroscopy

Acetaldehyde Dataset This data set was used to examine isomerization (to vinylalcohol, VA)<br> and decomposition reactions (to either CO+CH4 or H2+H2CCO) of<br> acetaldehyde (AA) relevant to atmospheric processes. Therefor, the data<br> is used to train a neural network which serves as a fully dimensional,<br> reactive potential energy surface and is used to run molecular dynamics<br> simulations. The data set contains different geometries for the mentioned molecules<br> and complexes as well as for some substructures (Amons [1]). In total the data set contains 432'399 geometries with reference<br> energies, forces and dipole moments calculated at the MP2/aug-cc-pVTZ<br> level of theory calculated using the MOLPRO [2]. For more details, see https://arxiv.org/abs/2003.08171 [1] Huang, B.; von Lilienfeld, O.A., arXiv:1707.04146 [2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schütz, M.; et al.<br> https://www.molpro.net Isomerization ; Chemistry ; Potential energy surface ; Photoexcitation ; Kinetic energy ; Potential energy ; Excited state ; Photochemistry ; Physical chemistry ; Computational chemistry ; Atomic physics ; Molecule ; Physics ; Catalysis Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Ryszard B. Nazarski
Source: Tetrahedron Letters (article, 2020-10-09)
Field: Chemistry — Spectroscopy

Summary of DFT calculations coupled with current statistical and/or artificial neural network (ANN) methods to assist experimental NMR data in identifying diastereomeric structures Diastereomer ; Chemistry ; Artificial neural network ; Chemical shift ; J-coupling ; Nuclear magnetic resonance spectroscopy ; Computational chemistry ; Molecule ; Biological system ; Artificial intelligence ; Stereochemistry ; Physical chemistry ; Computer science ; Organic chemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: B. Curto | Vidal Moreno | Juan Alberto García-Esteban | F.J. Blanco | I. González-Martı́n | Ana María Vivar Quintana | İsabel Revilla
Source: Sensors (article, 2020-06-24)
Field: Chemistry — Analytical Chemistry

Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical-chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations. Sensory system ; Artificial neural network ; Perception ; Computer science ; Artificial intelligence ; Set (abstract data type) ; Wine tasting ; Process (computing) ; Product (mathematics) ; Quality (philosophy) ; Machine learning ; Pattern recognition (psychology) ; Property (philosophy) ; Mathematics ; Food science ; Psychology ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Benjamin G. Peyton | Connor Briggs | Ruhee D’Cunha | Johannes T. Margraf | T. Daniel Crawford
Source: The Journal of Physical Chemistry A (article, 2020-05-15)
Field: Materials Science — Materials Chemistry

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation The introduction of machine-learning (ML) algorithms to quantum mechanics enables rapid evaluation of otherwise intractable expressions at the cost of prior training on appropriate benchmarks. Many computational bottlenecks in the evaluation of accurate electronic structure theory could potentially benefit from the application of such models, from reducing the complexity of the underlying wave function parameter space to circumventing the complications of solving the electronic Schrödinger equation entirely. Applications of ML to electronic structure have thus far been focused on learning molecular properties (mainly the energy) from geometric representations. While this line of study has been quite successful, highly accurate models typically require a "big data" approach with thousands of training data points. Herein, we propose a general, systematically improvable scheme for wave function-based ML of arbitrary molecular properties, inspired by the underlying equations that govern the canonical approach to computing the properties. To this end, we combine the established ML machinery of the <i>t-amplitude tensor representation</i> with a new reduced density matrix representation. The resulting model provides quantitative accuracy in both the electronic energy and dipoles of small molecules using only a few dozen training points per system. Representation (politics) ; Computer science ; Tensor (intrinsic definition) ; Wave function ; Function (biology) ; Coupled cluster ; Artificial neural network ; Artificial intelligence ; Function representation ; Matrix representation ; Theoretical computer science ; Machine learning ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics ; Object (grammar) ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: João Paulo Oliveira Ribeiro | André Dantas de Medeiros | ÍTALO PELIÇÃO CALIARI | Ana Clara Reis Trancoso | Rafaela Marques de Miranda | Francisco Cláudio Lopes de Freitas | Laércio Junio da Silva | Denise Cunha Fernandes dos Santos Dias
Source: Food Chemistry (article, 2020-10-10)
Field: Chemistry — Analytical Chemistry

FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals Linear discriminant analysis ; Near-infrared spectroscopy ; Quadratic classifier ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Machine learning ; Computer science ; Support vector machine ; Biology Spectroscopy and Chemometric Analyses ; Botanical Research and Chemistry ; Pesticide Residue Analysis and Safety
Authors: Chenru Duan | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: The Journal of Physical Chemistry Letters (article, 2020-07-21)
Field: Materials Science — Materials Chemistry

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model's robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening. Artificial intelligence ; Machine learning ; Computer science ; Transferability ; Leverage (statistics) ; Robustness (evolution) ; Workflow ; Classifier (UML) ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Elvis Osamudiamhen Ebikade | Yifan Wang | Nicholas Samulewicz | Bjorn Hasa | Dionisios G. Vlachos
Source: Reaction Chemistry & Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Active learning-driven quantitative synthesis–structure–property relations for improving performance and revealing active sites of nitrogen-doped carbon for the hydrogen evolution reaction A data-driven quantitative synthesis–structure–property relation methodology to elucidate correlations between catalyst synthesis conditions, structural properties and observed performance, providing a systematic way to optimize practical catalysts. Catalysis ; Property (philosophy) ; Hydrogen ; Nitrogen ; Carbon fibers ; Materials science ; Relation (database) ; Active site ; Nanotechnology ; Computer science ; Chemical engineering ; Biological system ; Chemistry ; Engineering ; Organic chemistry ; Data mining Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Markus Bursch | Thomas Gasevic | Julius B. Stückrath | Stefan Grimme
Source: Inorganic Chemistry (article, 2020-12-15)
Field: Chemistry — Spectroscopy

Comprehensive Benchmark Study on the Calculation of <sup>29</sup>Si NMR Chemical Shifts A comprehensive and diverse benchmark set for the calculation of 29Si NMR chemical shifts is presented. The SiS146 set includes 100 silicon containing compounds with 146 experimentally determined reference 29Si NMR chemical shifts measured in nine different solvents in a range from −400 to +828 ppm. Silicon atoms bound to main group elements as well as transition metals with coordination numbers of 2–6 in various bonding patterns including multiple bonds and coordinative and aromatic bonding are represented. The performance of various common and specialized density functional approximations including (meta-)GGA, hybrid, and double-hybrid functionals in combination with different AO basis sets and for differently optimized geometries is evaluated. The role of scalar-relativistic effects is further investigated by inclusion of the zeroth order regular approximation (ZORA) method into the calculations. GGA density functional approximations (DFAs) are found to outperform hybrid DFAs with B97-D3 performing best with an MAD of 7.2 ppm for the subset including only light atoms (Z < 18), while TPSSh is the best tested hybrid functional with an MAD of 10.3 ppm. For 29Si cores in the vicinity of heavier atoms, the application of ZORA proved indispensable. Inclusion of spin–orbit effects into the 29Si NMR chemical shift calculation decreases the mean absolute deviations by up to 74% compared to calculations applying effective core potentials. Chemistry ; Chemical shift ; Hybrid functional ; Density functional theory ; Basis set ; Scalar (mathematics) ; Benchmark (surveying) ; Computational chemistry ; Molecule ; Molecular physics ; Physical chemistry ; Mathematics ; Organic chemistry ; Geometry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Advanced Chemical Physics Studies
Authors: Julius de Rojas | Alberto Quintana | A. F. Lopeandía | Joaquín Salguero | J. L. Costa‐Krämer | Llibertat Abad | Maciej Oskar Liedke | Maik Butterling | A. Wagner | Lowie Henderick | Jolien Dendooven | Christophe Detavernier | Jordi Sort | Enric Menéndez
Source: Advanced Functional Materials (article, 2020-07-07)
Field: Materials Science — Materials Chemistry

Boosting Room‐Temperature Magneto‐Ionics in a Non‐Magnetic Oxide Semiconductor Abstract Voltage control of magnetism through electric field‐induced oxygen motion (magneto‐ionics) could represent a significant breakthrough in the pursuit for new strategies to enhance energy efficiency in magnetically actuated devices. Boosting the induced changes in magnetization, magneto‐ionic rates and cyclability continue to be key challenges to turn magneto‐ionics into real applications. Here, it is demonstrated that room‐temperature magneto‐ionic effects in electrolyte‐gated paramagnetic Co 3 O 4 films can be largely increased both in terms of generated magnetization (6 times larger) and speed (35 times faster) if the electric field is applied using an electrochemical capacitor configuration (utilizing an underlying conducting buffer layer) instead of placing the electric contacts at the side of the semiconductor (electric‐double‐layer transistor‐like configuration). This is due to the greater uniformity and strength of the electric field in the capacitor design. These results are appealing to widen the use of ion migration in technological applications such as neuromorphic computing or iontronics in general. Materials science ; Magnetization ; Boosting (machine learning) ; Semiconductor ; Magnetism ; Transistor ; Electric field ; Voltage ; Magneto ; Optoelectronics ; Electrolyte ; Capacitor ; Condensed matter physics ; Nanotechnology ; Magnetic field ; Electrical engineering ; Computer science ; Electrode ; Physics Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing ; ZnO doping and properties
Authors: Daniel G. A. Smith | Lori A. Burns | Andrew C. Simmonett | Robert M. Parrish | Matthew Schieber | Raimondas Galvelis | Peter Kraus | Holger Kruse | Roberto Di Remigio | Asem Alenaizan | Andrew M. James | Susi Lehtola | Jonathon P. Misiewicz | Maximilian Scheurer | Robert A. Shaw | Jeffrey B. Schriber | Yi Xie | Zachary L. Glick | Dominic A. Sirianni | Joseph O'Brien | Jonathan M. Waldrop | Ashutosh Kumar | Edward G. Hohenstein | Benjamin P. Pritchard | Bernard R. Brooks | Henry F. Schaefer | Alexander Yu. Sokolov | Konrad Patkowski | A. Eugene DePrince | Uğur Bozkaya | Rollin A. King | Francesco A. Evangelista | Justin M. Turney | Thomas O. Crawford | C. David Sherrill
Source: ChemRxiv (preprint, 2020-03-05)
Field: Materials Science — Materials Chemistry

Psi4 1.4: Open-Source Software for High-Throughput Quantum Chemistry &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Psi4 is a free and open-source ab initio electronic structure program providing Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of Psi4’s core functionality via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSchema data format, facilitating interoperability. A rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCArchive Infrastructure project, make the latest version of Psi4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Python (programming language) ; Computer science ; Workflow ; Interoperability ; Software ; Computation ; Computational science ; Coupled cluster ; Density functional theory ; Theoretical computer science ; Programming language ; Parallel computing ; Chemistry ; Computational chemistry ; Physics ; Molecule ; Operating system ; Quantum mechanics ; Database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Molecular spectroscopy and chirality
Authors: Hailong Shi | Weimin Gan | Claude Esling | Xiaojun Wang | Yudong Zhang | Emad Maawad | Andreas Stark | Xiaohu Li | Lidong Wang
Source: Acta Materialia (article, 2020-09-10)
Field: Materials Science — Materials Chemistry

Elastic strain induced abnormal grain growth in graphene nanosheets (GNSs) reinforced copper (Cu) matrix composites Materials science ; Grain growth ; Composite material ; Recrystallization (geology) ; Composite number ; Microstructure Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Simona Violino | Luciano Ortenzi | Francesca Antonucci | Federico Pallottino | Cinzia Benincasa | Simone Figorilli | Corrado Costa
Source: Foods (article, 2020-06-25)
Field: Chemistry — Analytical Chemistry

An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (<i>p</i> < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308-373 nm and 594-605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples' origin and thus could help to preserve the "Made in Italy" from fraud and sophistication related to its commerce. Traceability ; Multivariate analysis of variance ; Ingredient ; Artificial intelligence ; Olive oil ; Artificial neural network ; Computer science ; Food science ; Pattern recognition (psychology) ; Mathematics ; Chemistry ; Statistics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Liang Qi | Maocheng Zhao | Zhong Li | De-hong Shen | Jun Lü
Source: SN Applied Sciences (review, 2020-05-22)
Field: Chemistry — Analytical Chemistry

Non-destructive testing technology for raw eggs freshness: a review Electronic nose ; Yolk ; Raw material ; Computer science ; Process engineering ; Environmental science ; Food science ; Chemistry ; Engineering ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Glen Fox
Source: Applied Sciences (article, 2020-01-15)
Field: Chemistry — Analytical Chemistry

The Brewing Industry and the Opportunities for Real-Time Quality Analysis Using Infrared Spectroscopy Brewing is an ancient process which started in the middle east over 10,000 years ago. The style of beer varies across the globe but modern brewing is very much the same regardless of the style. While there are thousands of compounds in beer, current methods of analysis rely mostly on the content of only several important processing parameters such as gravity, bitterness, or alcohol. Near infrared and mid infrared spectroscopy offer opportunities to predict dozens to hundreds of compounds simultaneously at different stages of the brewing process. Importantly, this is an opportunity to move deeper into quality through measuring wort and beer composition, rather than just content. This includes measuring individual sugars and amino acids prior to fermentation, rather than total °Plato or free amino acids content. Portable devices and in-line probes, coupled with more complex algorithms can provide real time measurements, allowing brewers more control of the process, resulting in more consistent quality, reduced production costs and greater confidence for the future. Brewing ; Quality (philosophy) ; Fermentation ; Food science ; Alcohol content ; Mashing ; Process engineering ; Chemistry ; Pulp and paper industry ; Biochemical engineering ; Environmental science ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Simonas Savickas | Philipp Kastl | Ulrich auf dem Keller
Source: Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics (review, 2020-02-19)
Field: Chemistry — Spectroscopy

Combinatorial degradomics: Precision tools to unveil proteolytic processes in biological systems Computational biology ; Proteomics ; Proteome ; Computer science ; Workflow ; Protease ; Biology ; Bioinformatics ; Biochemistry ; Enzyme Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Hongyan Zhu | Aoife Gowen | Hailin Feng | Keping Yu | Jun‐Li Xu
Source: Sensors (article, 2020-09-17)
Field: Chemistry — Analytical Chemistry

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe ("myocommata") and red muscle ("myotome") pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Pixel ; Support vector machine ; Principal component analysis ; Computer science ; Convolutional neural network ; Linear discriminant analysis ; Artificial neural network ; Convolution (computer science) ; Computer vision Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies