jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Richárd Gubó | Gábor Vári | János Kiss | A.P. Farkas | Krisztián Palotás | László Óvári | A. Berkó | Zoltán Kónya
Source: Physical Chemistry Chemical Physics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Tailoring the hexagonal boron nitride nanomesh on Rh(111) with gold It is known that the hexagonal boron nitride (h-BN) monolayer has a periodically corrugated structure on Rh(111), termed "nanomesh", while the h-BN layer is planar on the close packed surfaces of coinage metals (Cu, Ag, Au) due the weak interactions. Our studies are aimed at understanding the metal-h-BN interaction, when both Rh and Au are present. On the one hand, the growth and thermal properties of gold deposited on h-BN nanomesh prepared on Rh(111) were studied. On the other hand, the formation of h-BN was examined on Au/Rh surface alloys prepared by the deposition of Au on Rh(111) and subsequent annealing at 1000 K. In each case, the h-BN was prepared by the decomposition of borazine at about 1000 K. Low energy ion scattering (LEIS), X-ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM) measurements revealed that the growth of Au on h-BN/Rh(111) at room temperature leads to the formation of mainly three dimensional (3D) gold nanoparticles, although at low coverages (<0.2 ML) 2D particles formed as well. Stepwise annealing to higher temperatures induces the intercalation of Au below the nanomesh, which was complete at around 1050 K. Some agglomeration and desorption of Au also took place. Interestingly, the nanomesh structure was observable after intercalation up to relatively large Au coverages. Measurements performed in the reverse order, namely exposing a Au/Rh(111) surface alloy to borazine, revealed that Rh atoms get covered by h-BN (or by its precursors) at significantly smaller borazine exposures than Au atoms. The nanomesh structure was essentially present up to a gold coverage of 0.9 ML, but with a smaller pore diameter, while it gradually disappeared at higher gold amounts. In this way the application of surface alloy supports provides a key for gradual tuning of the mesh morphology. Density functional theory calculations confirmed the decreased pore diameter of the BN layer upon the formation of a surface Rh-Au alloy layer. Nanomesh ; Hexagonal boron nitride ; Boron nitride ; Hexagonal crystal system ; Materials science ; Nanotechnology ; Nitride ; Boron ; Chemical engineering ; Chemistry ; Graphene ; Crystallography ; Organic chemistry ; Layer (electronics) Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Surface and Thin Film Phenomena
Authors: Brian C. Barnes | Daniel C. Elton | Zois Boukouvalas | DeCarlos E. Taylor | William D. Mattson | Mark Fuge | Peter W. Chung
Source: arXiv (Cornell University) (preprint, 2018-07-17)
Field: Materials Science — Materials Chemistry

Machine Learning of Energetic Material Properties In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure. Further, analysis is applied to individual molecules in order to explore the contribution of bonding motifs to these properties. Feature descriptors evaluated include Morgan fingerprints, E-state vectors, a custom "sum over bonds" descriptor, and coulomb matrices. Algorithms discussed include kernel ridge regression, least absolute shrinkage and selection operator ("LASSO") regression, Gaussian process regression, and the multi-layer perceptron (a neural network). Effects of regularization, kernel selection, network parameters, and dimensionality reduction are discussed. We determine that even when using a small training set, non-linear regression methods may create models within a useful error tolerance for screening of materials. Kriging ; Artificial intelligence ; Feature selection ; Detonation ; Perceptron ; Lasso (programming language) ; Artificial neural network ; Machine learning ; Computer science ; Curse of dimensionality ; Gaussian process ; Detonation velocity ; Regression ; Dimensionality reduction ; Kernel (algebra) ; Pattern recognition (psychology) ; Algorithm ; Explosive material ; Gaussian ; Mathematics ; Chemistry ; Statistics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Christopher Sutton | Luca M. Ghiringhelli | T. Yamamoto | Yury Lysogorskiy | Lars Blumenthal | Thomas Hammerschmidt | Jacek Gołębiowski | Xiangyue Liu | Angelo Ziletti | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2018-11-30)
Field: Materials Science — Materials Chemistry

NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing Machine learning (ML) is increasingly used in the field of materials science, where statistical estimates of computed properties are employed to rapidly examine the chemical space for new compounds. However, a systematic comparison of several ML models for this domain has been hindered by the scarcity of appropriate datasets of materials properties, as well as the lack of thorough benchmarking studies. To address this, a public data-analytics competition was organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and hosted by the on-line platform Kaggle using a dataset of $3\,000$ (Al$_x$ Ga$_y$ In$_z$)$_2$ O$_3$ compounds (with $x+y+z = 1$). The aim of this challenge was to identify the best ML model for the prediction of two key physical properties that are relevant for optoelectronic applications: the electronic band gap energy and the crystalline formation energy. In this contribution, we present a summary of the top three ML approaches of the competition including the 1st place solution based on a crystal graph representation that is new for ML of the properties of materials. The 2nd place model combined many candidate descriptors from a set of compositional, atomic environment-based, and average structural properties with the light gradient-boosting machine regression model. The 3rd place model employed the smooth overlap of atomic positions representation with a neural network. To gain insight into whether the representation or the regression model determines the overall model performance, nine ML models obtained by combining the representations and regression models of the top three approaches were compared by looking at the correlations among prediction errors. At fixed representation, the largest correlation is observed in predictions made with kernel ridge regression and neural network, reflecting a similar performance on the same test set samples. Benchmarking ; Computer science ; Representation (politics) ; Artificial intelligence ; Machine learning ; Competition (biology) ; Predictive modelling ; Data mining Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Mingyang Chen | David A. Dixon
Source: The Journal of Physical Chemistry C (article, 2018-07-20)
Field: Materials Science — Materials Chemistry

Machine-Learning Approach for the Development of Structure–Energy Relationships of ZnO Nanoparticles The structure–energy relationships for the zinc oxide morphologies were investigated using a newly developed fragment-based energy decomposition approach. In this approach, the local chemical compositions of a material are abstracted as fragment types that serve as the material’s genes with respect to its thermodynamic properties. A machine learning-based fragment recognition scheme was developed to learn about the fragment-related knowledge from a relatively small training set consisting of computationally viable ultrasmall nanoparticles. The knowledge gained including the fragment geometries and fragment energy parameters can be used for the classification and energy expression of the test sets consisting of different polymorphs and morphologies of that material at various scales. The stabilities of ZnO nanoparticles with different morphologies were expressed explicitly as functions of the particle size. The size-related phase transitions among various morphologies including wurtzite prisms, wurtzite octahedrons, body-centered tetragonal particles, sodalite-like particles, single-layered cages, multilayered cages, and nonpolar hexagonal prisms were predicted. The multilayered cages with nonpolar surfaces exhibit superior stability among the low-energy morphologies, but wurtzite nanoparticles are more favorable under practical synthesis and growth conditions under the control of the kinetics. The growth mechanism for ZnO clusters, ultrasmall nanoparticles, nanocrystals, and bulk-sized particles is proposed based on the synergy between the size-related phase transitions and external factors that affect the surface energies of the particles. Our interpretation of the Wulff theorem at a fragment-sized resolution provides new chemical insight for understanding the structural phase transition and particle growth for ZnO at various scales. Wurtzite crystal structure ; Nanoparticle ; Tetragonal crystal system ; Materials science ; Chemical physics ; Surface energy ; Nanotechnology ; Phase (matter) ; Crystallography ; Crystal structure ; Zinc ; Chemistry ; Composite material ; Metallurgy ZnO doping and properties ; Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Farren Curtis | Timothy Rose | Noa Marom
Source: Faraday Discussions (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Evolutionary niching in the GAtor genetic algorithm for molecular crystal structure prediction The goal of molecular crystal structure prediction (CSP) is to find all the plausible polymorphs for a given molecule. This requires performing global optimization over a high-dimensional search space. Genetic algorithms (GAs) perform global optimization by starting from an initial population of structures and generating new candidate structures by breeding the fittest structures in the population. Typically, the fitness function is based on relative lattice energies, such that structures with lower energies have a higher probability of being selected for mating. GAs may be adapted to perform multi-modal optimization by using evolutionary niching methods that support the formation of several stable subpopulations and suppress the over-sampling of densely populated regions. Evolutionary niching is implemented in the GAtor molecular crystal structure prediction code by using techniques from machine learning to dynamically cluster the population into niches of structural similarity. A cluster-based fitness function is constructed such that structures in less populated clusters have a higher probability of being selected for breeding. Here, the effects of evolutionary niching are investigated for the crystal structure prediction of 1,3-dibromo-2-chloro-5-fluorobenzene. Using the cluster-based fitness function increases the success rate of generating the experimental structure and additional low-energy structures with similar packing motifs. Fitness function ; Evolutionary algorithm ; Population ; Genetic algorithm ; Crystal structure prediction ; Evolutionary computation ; Fitness approximation ; Function (biology) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Michael Scherbela | Lukas Hörmann | Andreas Jeindl | Veronika Obersteiner | Oliver T. Hofmann
Source: Physical Review Materials (article, 2018-04-17)
Field: Materials Science — Materials Chemistry

Charting the energy landscape of metal/organic interfaces via machine learning The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. We demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally. Tetracyanoethylene ; Density functional theory ; Energy landscape ; Energy (signal processing) ; Anisotropy ; Power (physics) ; Surface (topology) Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Advanced Chemical Physics Studies
Authors: Jineeth Joseph | Raju Mallepally | T. K. Sindhu
Source:  (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Classification of Partial Discharge Sources in XLPE Cables by Artificial Neural Networks and Support Vector Machine Classification of partial discharge (PD) patterns is a significant tool in identifying the type of defects in cables. Development of reliable classifiers to identify various defects in the cable insulation is of vital importance in assessing the condition of cables in service. This paper proposes the development of Artificial Neural Network (ANN) based classifiers and Support Vector Machine (SVM) classifier for identification of cable defects such as voids, metal particle in the insulation, high potential metal tip, semiconductor layer tip, metals in the insulation and insulation incision. PD measurements are done on 11 kV XLPE cables with defects and wavelet based de-noising method is applied to abstract the PD pulses. Various PRPD features are extracted and used for training the ANN and SVM based models in MATLAB environment. The performance of SVM classifier and ANN based back propagation neural network classifier are analyzed for various types of defects. Classification accuracy of each models are analyzed and feasibility of optimum models for classification of cable defects is presented. Support vector machine ; Artificial neural network ; Partial discharge ; Classifier (UML) ; Pattern recognition (psychology) ; Artificial intelligence ; Backpropagation ; Computer science ; Machine learning ; Engineering ; Electrical engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Alessandro Simeone | Bin Deng | Nicholas J. Watson | Elliot Woolley
Source: Sensors (article, 2018-11-02)
Field: Chemistry — Analytical Chemistry

Enhanced Clean-In-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks Clean-in-place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted. Fouling ; Process engineering ; Process (computing) ; Artificial neural network ; Image processing ; Computer science ; Artificial intelligence ; Engineering ; Simulation ; Real-time computing ; Environmental science ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Fault Detection and Control Systems
Authors: Evangelia V. Avramidou | Andreas G. Doulis | Panos V. Petrakis
Source: Journal of Food Processing and Preservation (review, 2018-11-01)
Field: Chemistry — Analytical Chemistry

Chemometrical and molecular methods in olive oil analysis: A review Foods based on Olea europaea are of substantial importance for Mediterranean people. The association between olive products with great civilizations emphasizes the role that played the olive tree products and especially the olive oil. This generated the need for authentication, quality control and traceability of olive products. The physicochemical and sensory analysis methods, both classical (discrimination, ordination, classification) and novel (artificial neural networks, fuzzy logic, expert systems, decision trees, support vector machines) supplemented with molecular marker techniques (SSR, AFLP, RAPD), generated the need for effective multivariate analytical methods. These mathematically complex methods are greatly needed in the detection of adulterations of oil products and particularly for olive oil. The lack of mathematical symbolism and jargon is believed to make the text attractive to all potential workers in the field and help them improve the already low level of adulterations and augment the quality control databases for Mediterranean olive products. Practical applications In this review, multivariate methods applied until now in chemometrics supplemented with molecular methods of olive-based products were presented. Data processing in chemometris and molecular methods is an important issue in order to examine, explain, and evaluate such priorities of olive oil-based products. This review will facilitate researches to select the best suitable statistic model or equation regarding their hypothesis and use the most appropriate method in order to improve the low level of adulterations and augment in the quality control databases for Mediterranean olive products. Olive oil ; Chemometrics ; Quality (philosophy) ; Computer science ; Olea ; Biochemical engineering ; Biotechnology ; Machine learning ; Food science ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food
Authors: Rachael A. Mansbach | Andrew L. Ferguson
Source: The Journal of Physical Chemistry B (article, 2018-10-15)
Field: Materials Science — Biomaterials

Patchy Particle Model of the Hierarchical Self-Assembly of π-Conjugated Optoelectronic Peptides Self-assembling peptides containing aromatic groups are an attractive target for bioelectronic materials design because of their ease of manufacture, biocompatibility, aqueous solubility, and chemical tunability. Microscopic understanding of the properties that control assembly is a prerequisite for rational design. In this work, we study the assembly of a family of DXXX-Π-XXXD oligopeptides possessing a π-conjugated core flanked by Asp-terminated tetrapeptide wings that display pH-triggered assembly into supramolecular aggregates. We develop a coarse-grained patchy particle model to conduct molecular dynamics simulations of the assembly of ten thousand oligopeptides over hundreds of nanometers and hundreds of microseconds. We study the effects of core and side chain interaction strength and side chain steric volume upon the morphology and kinetics of assembly. By characterizing the rate and fractal dimension of hierarchical nanoaggregate growth, we identify parameter regimes that favor rapid assembly of linear aggregates and map these regimes to sequence-defined candidate peptides for experimental synthesis and testing. This work establishes new understanding of assembly on previously unexplored time and length scales and presents an efficient and extensible protocol for computational screening and prediction of promising peptide chemistries to assemble nanostructures with desirable optoelectronic properties. Supramolecular chemistry ; Tetrapeptide ; Conjugated system ; Materials science ; Nanotechnology ; Rational design ; Side chain ; Granularity ; Molecular dynamics ; Self-assembly ; Supramolecular assembly ; Oligopeptide ; Steric effects ; Peptide ; Chemistry ; Polymer ; Stereochemistry ; Computational chemistry ; Molecule ; Computer science ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Luminescence and Fluorescent Materials
Authors: Nazila Biglari | Marjan Ganjali Dashti | Peyman Abdeshahian | Izumi Orita | Toshiaki Fukui | Kumar Sudesh
Source: 3 Biotech (article, 2018-07-19)
Field: Materials Science — Biomaterials

Enhancement of bioplastic polyhydroxybutyrate P(3HB) production from glucose by newly engineered strain Cupriavidus necator NSDG-GG using response surface methodology Polyhydroxybutyrate ; Bioplastic ; Cupriavidus necator ; Strain (injury) ; Polyhydroxyalkanoates ; Materials science ; Chemistry ; Biology ; Bacteria ; Waste management ; Engineering biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Microplastics and Plastic Pollution
Authors: J. Liu | Li Jin | Zhijun Jiang | Laijun Liu | Lauri Himanen | Jie Wei | Nan Zhang | Dawei Wang | Chun‐Lin Jia
Source: The Journal of Chemical Physics (article, 2018-12-28)
Field: Materials Science — Materials Chemistry

Understanding doped perovskite ferroelectrics with defective dipole model While doping is widely used for tuning physical properties of perovskites in experiments, it remains a challenge to exactly know how doping achieves the desired effects. Here, we propose an empirical and computationally tractable model to understand the effects of doping with Fe-doped BaTiO<sub>3</sub> as an example. This model assumes that the lattice sites occupied by a Fe ion and its nearest six neighbors lose their ability to polarize, giving rise to a small cluster of defective dipoles. Employing this model in Monte Carlo simulations, many important features such as reduced polarization and the convergence of phase transition temperatures, which have been observed experimentally in acceptor doped systems, are successfully obtained. Based on microscopic information of dipole configurations, we provide insights into the driving forces behind doping effects and propose that active dipoles, which exist in proximity to the defective dipoles, can account for experimentally observed phenomena. Close attention to these dipoles is necessary to understand and predict doping effects. Doping ; Dipole ; Condensed matter physics ; Materials science ; Polarization (electrochemistry) ; Phase transition ; Chemical physics ; Monte Carlo method ; Lattice (music) ; Statistical physics ; Physics ; Chemistry ; Quantum mechanics ; Physical chemistry ; Mathematics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Dielectric properties of ceramics