jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kurt R. Brorsen
Source: The Journal of Chemical Physics (article, 2019-05-22)
Field: Materials Science — Materials Chemistry

Reproducing global potential energy surfaces with continuous-filter convolutional neural networks Neural networks fit to reproduce the potential energy surfaces of quantum chemistry methods offer a realization of analytic potential energy surfaces with the accuracy of ab initio methods at a computational cost similar to classical force field methods. One promising class of neural networks for this task is the SchNet architecture, which is based on the use of continuous-filter convolutional neural networks. Previous work has shown the ability of the SchNet architecture to reproduce density functional theory energies and forces for molecular configurations sampled during equilibrated molecular dynamics simulations. Due to the large change in energy when bonds are broken and formed, the fitting of global potential energy surfaces is normally a more difficult task than fitting the potential energy surface in the region of configurational space sampled during equilibrated molecular dynamics simulations. Herein, we demonstrate the ability of the SchNet architecture to reproduce the energies and forces of the potential energy surfaces of the H + H<sub>2</sub> and Cl + H<sub>2</sub> reactions and the OCHCO<sup>+</sup> and H<sub>2</sub>CO/cis-HCOH/trans-HCOH systems. The SchNet models reproduce the potential energy surface of the reactions well with the best performing SchNet model having a test set root-mean-squared error of 0.52 meV and 2.01 meV for the energies of the H + H<sub>2</sub> and Cl + H<sub>2</sub> reactions, respectively, and a test set mean absolute error for the force of 0.44 meV/bohr for the H + H<sub>2</sub> reaction. For the OCHCO<sup>+</sup> and H<sub>2</sub>CO/cis-HCOH/trans-HCOH systems, the best performing SchNet model has a test set root-mean-squared error of 2.92 meV and 13.55 meV, respectively. Potential energy surface ; Potential energy ; Molecular dynamics ; Force field (fiction) ; Bohr model ; Density functional theory ; Filter (signal processing) ; Statistical physics ; Physics ; Ab initio ; Chemistry ; Computational chemistry ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Xavier F. Cadet | Ophélie Lo-Thong | Sylvie Bureau | Réda Dehak | Miloud Bessafi
Source: Scientific Reports (article, 2019-12-16)
Field: Chemistry — Analytical Chemistry

Use of Machine Learning and Infrared Spectra for Rheological Characterization and Application to the Apricot Fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing. Among the various types of data available for analysis, the Fourier Transform InfraRed (FTIR) spectroscopy spectra are very challenging datasets to consider. In this study, machine learning is used to analyze and predict a rheological parameter: firmness. Various statistics have been gathered including both chemistry (such as ethylene, titrable acidity or sugars) and spectra values to visualize and analyze a dataset of 731 biological samples. Two-dimensional (2D) and three-dimensional (3D) principal component analyses (PCA) are used to evaluate their ability to discriminate for one parameter: firmness. Partial least squared regression (PLSR) modeling has been carried out to predict the rheological parameter using either sixteen physicochemical parameters or only the infrared spectra. We show that (i) the spectra alone allows good discrimination of the samples based on rheology, (ii) 3D-PCA allows comprehensive and informative visualization of the data, and (iii) that the rheological parameters are predicted accurately using a regression method such as PLSR; instead of using chemical parameters which are laborious to obtain, Mid-FTIR spectra gathering all physicochemical information could be used for efficient prediction of firmness. As a conclusion, rheological and chemical parameters allow good discrimination of the samples according to their firmness. However, using only the IR spectra leads to better results. A good predictive model was built for the prediction of the firmness of the fruit, and we reached a coefficient of determination R<sup>2</sup> value of 0.90. This method outperforms a model based on physicochemical descriptors only. Such an approach could be very helpful to technologists and farmers. Principal component analysis ; Partial least squares regression ; Artificial intelligence ; Fourier transform infrared spectroscopy ; Rheology ; Coefficient of determination ; Computer science ; Biological system ; Fourier transform ; Infrared spectroscopy ; Regression analysis ; Spectral line ; Infrared ; Machine learning ; Mathematics ; Materials science ; Chemistry ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Plant Surface Properties and Treatments
Authors: Pál Péter Hanzelik | Szilveszter Gergely | C. Gaspar | László Győry
Source: Journal of Chemometrics (article, 2019-12-25)
Field: Chemistry — Analytical Chemistry

Machine learning methods to predict solubilities of rock samples Abstract Interests in the use of chemometric and data science methods for laboratory techniques have grown rapidly over the last 10 years, for the reason that they are cheaper and faster than traditional analytical methods of material testing. This study uses 888 rock samples collected from the exploration and production (E&amp;P) sector of the oil industry. Based on the Fourier‐transform infrared (FT‐IR) spectra of these rock samples their solubility predictions have been developed and investigated with nine methods including both linear and non‐linear ones. Two of these methods such as Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) are available in a commercial software package and the other seven methods, Extreme Gradient Boosting (XGBoost), Ridge Regression (RR), k ‐nearest neighbours ( k ‐NN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Regression (SVR), Artificial Neural Network (ANN) with TensorFlow (TF), were coded by the authors based either on commercial applications or open source libraries. The investigation starts with spectral data pre‐processing carried out by standard normal variate (SNV), baseline correction and feature selection methods creating the feature set for all machine learning (ML) applications. The accuracy of predictions has been evaluated with mean squared error as a performance metric for each investigated method. The comparisons of predicted values to real data of test samples have shown that mineral solubility in acids can be well predicted in the range of the uncertainties of real laboratory measurements, therefore it can be used to improve the response time of these investigations and reduce the risk in industrial applications. In those cases, where the unknown samples have got some out of the range features, the limitations in the accuracy of predictions have become clear. We have also identified the limitations in the methodology and planned steps to further improve the prediction capabilities. The identified constraint of samples' multitude further emphasizes the need for database building efforts, so that the real potential in big data and machine learning can be realized. Feature selection ; Support vector machine ; Partial least squares regression ; Artificial intelligence ; Artificial neural network ; Multilayer perceptron ; Linear regression ; Computer science ; Mean squared error ; Machine learning ; Regression ; Extreme learning machine ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Geochemistry and Geologic Mapping
Authors: Jan Ravnik | Igor Vaskivskyi | Yaroslav A. Gerasimenko | Michele Diego | Jaka Vodeb | V. V. Kabanov | D. Mihailović
Source: ACS Applied Nano Materials (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Strain-Induced Metastable Topological Networks in Laser-Fabricated TaS<sub>2</sub> Polytype Heterostructures for Nanoscale Devices The stacking of layered materials into heterostructures offers diverse possibilities for generating deformed moiré states arising from their mutual interaction. Here we report self-assembled two-dimensional nanoscale strain networks formed within a single prismatic (H) polytype monolayer of TaS<sub>2</sub> created in situ on the surface of an orthorhombic 1T-TaS<sub>2</sub> single crystal by a low-temperature laser-induced polytype transformation. The networks revealed by scanning tunneling microscopy (STM) take on diverse configurations at different temperatures, including extensive double stripes and a twisted 3-gonal mesh of connected 6-pronged vertices. The resulting phase diagram can be understood to be a consequence of thermally driven minimization of discommensurations between the H and 1T layers. Nontrivial dislocation defects of embedded 2- and 4-gonal structures are shown to be associated with local inhomogeneous strains. The creation of metastable heterostructures by laser quench at cryogenic temperatures in combination with STM manipulation of local strain demonstrates nanoscale control of topological defects in transition metal dichalcogenide heterostructures may be utilized in the fabrication of nanoscale electronic devices and neural networks. Heterojunction ; Materials science ; Nanoscopic scale ; Scanning tunneling microscope ; Stacking ; Orthorhombic crystal system ; Condensed matter physics ; Monolayer ; Metastability ; Dislocation ; Laser ; Nanotechnology ; Topology (electrical circuits) ; Optoelectronics ; Crystallography ; Crystal structure ; Optics ; Chemistry ; Physics ; Composite material 2D Materials and Applications ; Topological Materials and Phenomena ; Transition Metal Oxide Nanomaterials
Authors: Amorndej Puttipipatkajorn | Amornrit Puttipipatkajorn
Source: Journal of Innovative Optical Health Sciences (article, 2019-12-20)
Field: Chemistry — Analytical Chemistry

Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards. Calibration ; Natural rubber ; Mean squared error ; Partial least squares regression ; Coefficient of determination ; Near-infrared spectroscopy ; Biological system ; Water content ; Moisture ; Mathematics ; Environmental science ; Materials science ; Statistics ; Engineering ; Optics ; Composite material ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Industrial Vision Systems and Defect Detection
Authors: Ambuj Saxena | A. Kumaraswamy | Nitin Kotkunde | Kurra Suresh
Source: Journal of Materials Engineering and Performance (article, 2019-09-20)
Field: Materials Science — Materials Chemistry

Constitutive Modeling of High-Temperature Flow Stress of Armor Steel in Ballistic Applications: A Comparative Study Materials science ; Mean absolute percentage error ; Constitutive equation ; Strain rate ; Flow stress ; Standard deviation ; Isothermal process ; Approximation error ; Plasticity ; Correlation coefficient ; Artificial neural network ; Composite material ; Mechanics ; Structural engineering ; Thermodynamics ; Mathematics ; Statistics ; Physics ; Artificial intelligence ; Engineering ; Computer science ; Finite element method High-Velocity Impact and Material Behavior ; Metal and Thin Film Mechanics ; Laser-Plasma Interactions and Diagnostics
Authors: Eléonor Acher | Michel Masella | Valérie Vallet | Florent Réal
Source: Physical Chemistry Chemical Physics (article, 2019-12-30)
Field: Chemistry — Inorganic Chemistry

Properties of the tetravalent actinide series in aqueous phase from a microscopic simulation self-consistent engine In the context of nuclear fuel recycling and environmental issues, the understanding of the properties of radio-elements with various approaches remains a challenge regarding their dangerousness. Moreover, experimentally, some issues are also of importance; first, it is imperative to work at sufficiently high concentrations to reach the sensitivities of the analytical tools, however this condition often leads to precipitation for some of them; second, stabilizing specific oxidation states of some actinides remains a challenge, thus making it difficult to extract general trends across the actinide series. Complementary to experiments, modeling can be used to unbiasedly probe the actinide's properties in an aquatic environment and offers a predictive tool. We report the first molecular dynamics simulations based on homogeneously built force fields for the whole series of the tetravalent actinides in aqueous phase from ThIV to BkIV and including PuIV. The force fields used to model the interactions among the constituents include polarization and charge donation microscopic effects. They are built from a self-consistent iterative ab initio based engine that can be included in future developments as an element of a potential machine learning procedure devoted to generating accurate force fields. The comparison of our simulated hydrated actinide properties to available experimental data shows the model robustness and the relevance of our parameter assignment engine. Moreover, our simulated structural, dynamical and evolution of the hydration free energy data show that, apart from AmIV and CmIV, the actinide properties change progressively along the series. Series (stratigraphy) ; Actinide ; Self consistent ; Molecular dynamics ; Ab initio ; Aqueous solution ; Computational chemistry ; Materials science ; Statistical physics ; Chemistry ; Physics ; Physical chemistry ; Inorganic chemistry ; Quantum electrodynamics ; Organic chemistry ; Geology Radioactive element chemistry and processing ; Lanthanide and Transition Metal Complexes ; Nuclear Materials and Properties
Authors: J. Brindha | M. M. Balamurali | Kaushik Chanda
Source: RSC Advances (review, 2019-01-01)
Field: Materials Science — Biomaterials

Evolutionary approaches in protein engineering towards biomaterial construction The tailoring of proteins for specific applications by evolutionary methods is a highly active area of research. Rational design and directed evolution are the two main strategies to reengineer proteins or create chimeric structures. Rational engineering is often limited by insufficient knowledge about proteins' structure-function relationships; directed evolution overcomes this restriction but poses challenges in the screening of candidates. A combination of these protein engineering approaches will allow us to create protein variants with a wide range of desired properties. Herein, we focus on the application of these approaches towards the generation of protein biomaterials that are known for biodegradability, biocompatibility and biofunctionality, from combinations of natural, synthetic, or engineered proteins and protein domains. Potential applications depend on the enhancement of biofunctional, mechanical, or other desired properties. Examples include scaffolds for tissue engineering, thermostable enzymes for industrial biocatalysis, and other therapeutic applications. Biomaterial ; Toolbox ; Protein engineering ; Tissue engineering ; Engineering ; Biocompatible material ; Computer science ; Biochemical engineering ; Nanotechnology ; Materials science ; Chemistry ; Biomedical engineering ; Mechanical engineering ; Biochemistry Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Nanofabrication and Lithography Techniques
Authors: Peter Bjørn Jørgensen | Estefanía Garijo del Río | Mikkel N. Schmidt | Karsten W. Jacobsen
Source: Physical review. B./Physical review. B (article, 2019-09-26)
Field: Materials Science — Materials Chemistry

Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors Accurate property prediction for structures without knowledge of exact atomic positions is achieved with a machine-learning model. The model is formulated as neural-message passing on Voronoi graphs using a new method for the encoding of local symmetry information. Density functional theory ; Position (finance) ; Computer science ; Artificial neural network ; Symmetry (geometry) ; Quotient ; Voronoi diagram ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Algorithm ; Database ; Mathematics ; Physics ; Quantum mechanics ; Geometry ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Nathan A. Mahynski | Evan Pretti | Vincent K. Shen | Jeetain Mittal
Source: Nature Communications (article, 2019-05-02)
Field: Materials Science — Materials Chemistry

Using symmetry to elucidate the importance of stoichiometry in colloidal crystal assembly We demonstrate a method based on symmetry to predict the structure of self-assembling, multicomponent colloidal mixtures. This method allows us to feasibly enumerate candidate structures from all symmetry groups and is many orders of magnitude more computationally efficient than combinatorial enumeration of these candidates. In turn, this permits us to compute ground-state phase diagrams for multicomponent systems. While tuning the interparticle potentials to produce potentially complex interactions represents the conventional route to designing exotic lattices, we use this scheme to demonstrate that simple potentials can also give rise to such structures which are thermodynamically stable at moderate to low temperatures. Furthermore, for a model two-dimensional colloidal system, we illustrate that lattices forming a complete set of 2-, 3-, 4-, and 6-fold rotational symmetries can be rationally designed from certain systems by tuning the mixture composition alone, demonstrating that stoichiometric control can be a tool as powerful as directly tuning the interparticle potentials themselves. Symmetry (geometry) ; Stoichiometry ; Colloidal crystal ; Nanotechnology ; Crystal (programming language) ; Crystallography ; Colloid ; Materials science ; Chemistry ; Computer science ; Physical chemistry ; Mathematics ; Geometry Pickering emulsions and particle stabilization ; Crystallization and Solubility Studies ; Minerals Flotation and Separation Techniques
Authors: Jeffrey Méndez | L.H. Ros Mendoza | J.P. Cruz‐Tirado | Roberto Quevedo | Raúl Siché
Source: Scientia Agropecuaria (article, 2019-03-29)
Field: Chemistry — Analytical Chemistry

Trends in application of NIR and hyperspectral imaging for food authentication Food fraud can cause damage to consumer health and affect their confidence, destroy brands and generate large economic losses in the industry. Food authenticity allows to identify if food composition, geographical origin, genetic variety and farming system corresponds to what has been declared on the label. Although there are currently standardized methods to identify certain adulterants, the complexity of the food, the complexity of the supply chain and the appearance of new adulterants require the continuous development of analytical techniques to detect food fraud. NIR and Hyperspectral imaging (HSI) in tandem with chemometrics are non-destructive, non-invasive and accurate techniques for food authentication. This review focuses on NIR and HIS approaches to food authentication, including adulteration by substitution, geographical origin and farming system. In this context, the advances in NIR and HSI approaches reported since 2014 are discussed regarding their potential use in food authentication. Both techniques have shown to have efficiency, precision and selectivity to detect adulterants and identify geographic origin, genetic variety and farming system. Portability and remote access are shown as the next step for the industrialization of NIR and HSI devices. Authentication (law) ; Hyperspectral imaging ; Context (archaeology) ; Food security ; Software portability ; Computer science ; Variety (cybernetics) ; Agriculture ; Biotechnology ; Business ; Artificial intelligence ; Geography ; Computer security ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Saibabu Madas | S. K. Mishra | Subhendu Kahaly | Mousumi Upadhyay Kahaly
Source: Scientific Reports (article, 2019-07-16)
Field: Materials Science — Materials Chemistry

Superior Photo-thermionic electron Emission from Illuminated Phosphorene Surface This work demonstrates that black phosphorene, a two dimensional allotrope of phosphorus, has the potential to be an efficient photo-thermionic emitter. To investigate and understand the novel aspects we use a combined approach in which ab initio quantum simulation tools are utilized along with semiclassical description for the emission process. First by using density functional theory based formalism, we study the band structure of phosphorene. From the locations of electronic bands, and band edges, we estimate the Fermi level and work function. This leads us to define a valid material specific parameter space and establish a formalism for estimating thermionic electron emission current from phosphorene. Finally we demonstrate how the emission current can be enhanced substantially under the effect of photon irradiation. We observe that photoemission flux to strongly dominate over its coexisting counterpart thermionic emission flux. Anisotropy in phosphorene structure plays important role in enhancing the flux. The approach which is valid over a much wider range of parameters is successfully tested against recently performed experiments in a different context. The results open up a new possibility for application of phosphorene based thermionic and photo-thermionic energy converters. Phosphorene ; Thermionic emission ; Physics ; Electron ; Density functional theory ; Electronic band structure ; Computational physics ; Condensed matter physics ; Materials science ; Band gap ; Quantum mechanics 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; Graphene research and applications