jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ana Martín-Recio | Carlos Romero‐Muñiz | Pablo Jauralde Pou | Rúben Pérez | José M. Gómez‐Rodríguez
Source: Carbon (article, 2018-01-03)
Field: Materials Science — Materials Chemistry

Combining nitrogen substitutional defects and oxygen intercalation to control the graphene corrugation and doping level Graphene ; Intercalation (chemistry) ; Dopant ; Materials science ; Substrate (aquarium) ; Scanning tunneling microscope ; Doping ; Nitrogen ; Chemical physics ; Oxygen ; Ion ; Nanotechnology ; Crystallography ; Inorganic chemistry ; Chemistry ; Optoelectronics Graphene research and applications ; Advancements in Battery Materials ; Quantum and electron transport phenomena
Authors: Anatoly V. Koshelev | Konstantin V. Zakharov | Alexander P. Pyatakov | Larisa V. Shvanskaya | Alexander A. Shakin | Olga S. Volkova | Dmitry A. Chareev | Sirko Kamusella | Hans-Henning Klauss | Kaimujjaman Molla | Badiur Rahaman | Tanusri Saha-Dasgupta | Alexander N. Vasiliev
Source: Physical Review Applied (article, 2018-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-Order-Induced Ferroelectricity and Magnetoelectric Effect in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>Li</mml:mi><mml:mi>Cu</mml:mi><mml:msub><mml:mi>Fe</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>VO</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:msub><mml:mo stretchy="false">)</mml:mo><mml:mn>3</mml:mn></mml:msub></mml:math> The coexistence of ferroelectricity and magnetism---for a long time considered mutually exclusive phenomena---now underpins the flourishing field of magnetoelectric multiferroicity. The authors investigate the multiferroic properties induced by spin ordering in a complex oxide that in many respects differs from any magnetoelectric material studied to date. Besides its general interest, this compound would seem to be of direct relevance for exquisitely sensitive measurements of magnetic fields. Magnetoelectric effect ; Multiferroics ; Ferroelectricity ; Spin (aerodynamics) ; Magnetic field ; Field (mathematics) Multiferroics and related materials ; Advanced Condensed Matter Physics ; Physics of Superconductivity and Magnetism
Authors: Alexander Gebharter | Nina Retzlaff
Source: Synthese (article, 2018-11-16)
Field: Chemistry — Physical and Theoretical Chemistry

A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: fixing the chemical factory Cartwright (Synthese 121(1/2):3-27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright's as well as similar scenarios (such as decay processes, EPR/B experiments, or spontaneous macro breaking processes) feature a certain kind of noncausal dependence that kicks in once the common cause occurs. We then develop a representation of this specific kind of non-causal dependence that allows for modeling the problematic scenarios in such a way that the Markov condition is not violated anymore. Counterexample ; Causation ; Markov chain ; Factory (object-oriented programming) ; Representation (politics) ; Philosophy of language ; Epistemology ; Feature (linguistics) ; Markov process ; Philosophy of science ; Computer science ; Mathematics ; Philosophy ; Discrete mathematics ; Metaphysics ; Programming language ; Linguistics ; Machine learning ; Statistics ; Political science History and advancements in chemistry ; Philosophy and History of Science
Authors: Alexander Müller | Irina Roslyakova | Mario Sprenger | P. Git | Ralf Rettig | Matthias Markl | Carolin Körner | Robert F. Singer
Source: Modelling and Simulation in Materials Science and Engineering (article, 2018-11-14)
Field: Materials Science — Materials Chemistry

MultOpt++: a fast regression-based model for the development of compositions with high robustness against scatter of element concentrations Alloys-by-design is a term used to describe new alloy development techniques based on numerical simulation. These approaches are extensively used for nickel-base superalloys to increase the chance of success in alloy development. During alloy production of numerically optimized compositions, unavoidable scattering of the element concentrations occurs. In the present paper, we investigate the effect of this scatter on the alloy properties. In particular, we describe routes to identify alloy compositions by numerical simulations that are more robust than other compositions. In our previously developed alloy development program package MultOpt, we introduced a sensitivity parameter that represents the influence of alloying variations on the final alloy properties in the post-optimization process, because the established sensitivity calculations require high computational effort. In this work, we derive a regressionbased model for calculating the sensitivity that only requires one-time calculation of the regression coefficients. The model can be applied to any function with nearly linear behavior within the uncertainty range. The model is Materials science ; Robustness (evolution) ; Regression ; Regression analysis ; Biological system ; Statistics ; Mathematics Crystallization and Solubility Studies ; Metallurgical Processes and Thermodynamics ; Radioactive element chemistry and processing
Authors: Kuang Liu | Priya Vashishta | Manaschai Kunaseth | Saber Naserifar | William A. Goddard | Ye Luo | Nichols A. Romero | Fuyuki Shimojo | Subodh Tiwari | Chunyang Sheng | Aravind Krishnamoorthy | Sungwook Hong | Pankaj Rajak | Rajiv K. Kalia | Aiichiro Nakano | Kenichi Nomura
Source:  (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

Shift-Collapse Acceleration of Generalized Polarizable Reactive Molecular Dynamics for Machine Learning-Assisted Computational Synthesis of Layered Materials Reactive molecular dynamics is a powerful simulation method for describing chemical reactions. Here, we introduce a new generalized polarizable reactive force-field (ReaxPQ+) model to significantly improve the accuracy by accommodating the reorganization of surrounding media. The increased computation is accelerated by (1) extended Lagrangian approach to eliminate the speed-limiting charge iteration, (2) shift-collapse computation of many-body renormalized n-tuples, which provably minimizes data transfer, (3) multithreading with round-robin data privatization, and (4) data reordering to reduce computation and allow vectorization. The new code achieves (1) weak-scaling parallel efficiency of 0.989 for 131,072 cores, and (2) eight-fold reduction of time-to-solution (T2S) compared with the original code, on an Intel Knights Landing-based computer. The reduced T2S has for the first time allowed purely computational synthesis of atomically-thin transition metal dichalcogenide layers assisted by machine learning to discover a novel synthetic pathway. Computer science ; Parallel computing ; Computation ; Molecular dynamics ; Acceleration ; Multithreading ; Computational science ; Tuple ; Surrogate model ; Algorithm ; Chemistry ; Computational chemistry ; Physics ; Machine learning ; Mathematics 2D Materials and Applications ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Marek Hawryluk | B. Mrzygłód
Source: Journal of Mining and Metallurgy Section B Metallurgy (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

A system of analysis and prediction of the loss of forging tool material applying artificial neural networks The article presents the use of artificial neural networks (ANN) to build a system of analysis and forecasting of the durability of forging tools and the process of acquiring the source knowledge necessary for the network learning process. In particular, the study focuses on the prediction of the geometrical loss of the tool material after different surface treatment variants.The methodology of developing neural network models and their quality parameters is also presented. The standard single-layer MLP networks were used here; their quality parameters are at a high level and the results presented with their participation give satisfactory results in line with technological practice. The data used in the learning process come from extensive comprehensive performance tests of forging tools operating under extreme operating conditions (cyclic mechanical and thermal loads). The parameterization of the factors important for the selected forging process was made and a database was developed, including 900 knowledge vectors, each of which provided information on the size of the geometrical loss of the tool material (explained variables). The value of wear was determined for the set values of explanatory variables such as: number of forgings, pressure, temperature on selected tool surfaces, friction path and the variant of the applied surface treatment. The results presented in the study, confirmed by expert technologists, have a clear applicational character, because based on the presented solutions, the optimal treatment can be chosen and the appropriate preventive measures applied, which will extend the service life. Forging ; Artificial neural network ; Process (computing) ; Computer science ; Durability ; Quality (philosophy) ; Machine learning ; Service life ; Artificial intelligence ; Mechanical engineering ; Data mining ; Engineering ; Database Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Metallurgy and Material Forming
Authors: Jack Yang | Sandip De | Joshua E Campbell | Sean Li | Michele Ceriotti | Graeme M. Day
Source: ChemRxiv (preprint, 2018-04-27)
Field: Materials Science — Materials Chemistry

Large–Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole azaphenacene molecule (pyrido[2,3-b]pyrido[3`,2`:4,5]-pyrrolo[3,2-g]indole, 1) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of 1 to assess charge mobility in their predicted crystal structures. Machine--learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to 1 and suggest two molecules in particular that represent attractive synthetic targets. Crystal structure prediction ; Molecule ; Stacking ; Organic semiconductor ; Crystal (programming language) ; Chemical physics ; Crystal structure ; Hydrogen bond ; Materials science ; Pyrrole ; Computational chemistry ; Small molecule ; Crystal engineering ; Indole test ; Charge (physics) ; Semiconductor ; Chemistry ; Crystallography ; Computer science ; Stereochemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Stefan Chmiela | Huziel E. Sauceda | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: RePEc: Research Papers in Economics (article, 2018-02-26)
Field: Materials Science — Materials Chemistry

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations. Molecular dynamics ; Force field (fiction) ; Statistical physics ; Quantum ; Field (mathematics) ; Physics ; Interatomic potential ; Computer science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: Geoffrey A. Ozin | Todd Siler
Source: Chem (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Catalyst: New Materials Discovery: Machine-Enhanced Human Creativity Genius ; The arts ; Creativity ; Government (linguistics) ; Law ; Nanotechnology ; Sociology ; Chemistry ; Art history ; Political science ; Art ; Materials science ; Philosophy Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Graphene and Nanomaterials Applications
Authors: Mohammad Zabihi | Nasser Babajani
Source: RSC Advances (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Modeling, optimization and experimental studies of supported nano-bimetallic catalyst for simultaneous total conversion of toluene and cyclohexane in air using a hybrid intelligent algorithm This study reveals the simultaneous deep oxidation of toluene and cyclohexane over optimal supported bimetallic catalysts over almond shell based activated carbon. To the best of our knowledge, this study is the first to construct a hybrid intelligent model to predict and determine an optimal supported bimetallic catalyst to oxidize aromatic and aliphatic compounds in air. The effects of preparation and operating parameters, including oxidation temperature, initial concentration of VOCs, structure of the catalyst and metal oxide content, on VOCs conversion were studied by modeling a database containing 50 data points derived from our previously published study, by an artificial neural network (ANN). Reported experimental data were predicted by a feed-forward network with 11 neurons and tansig function in the hidden layer. The non-linear network demonstrated stronger influence of oxidation temperature and cobalt content on the complete conversion of toluene and cyclohexane in the mixture. A hybrid model containing a genetic algorithm (GA) and an ANN were employed to realize the optimum catalyst at constant operating conditions for the complete conversion of toluene and cyclohexane in air. A well dispersed optimal alloy catalyst with 8 wt% metal oxide content (2.5 wt% copper oxide and 5.5 wt% cobalt oxide) over activated carbon was synthesized by heterogeneous deposition-precipitation for the complete conversion of toluene (model = 95.50%, experimental = 96%) and cyclohexane (model = 91.88% and experimental = 91%), simultaneously. Characterizations of the optimal catalyst were carried out by XRD, TEM, ICP, FESEM and BET analyses to justify its highest performance. Cyclohexane ; Bimetallic strip ; Toluene ; Catalysis ; Oxide ; Cobalt oxide ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Muhamad Azahar Abas | N. S. A. Zubir | Nuraiza Ismail | Ahmad Ihsan Mohd Yassin | Nada Ali | Mohd Hezri Fazalul Rahiman | N. T. Saiful | Mohd Nasir Taib
Source: Journal of Fundamental and Applied Sciences (article, 2018-01-23)
Field: Chemistry — Organic Chemistry

Agarwood oil quality classifier using machine learning Agarwood oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies and religious prayers. Its quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine learning model k-nearest neighbour (k-NN). The chemical compounds of the agarwood oil from high and low quality are used to train and build the k-NN classifier model. Correlation reduce the dimension of the data before it is being fed into the model. The results show a very high accuracy (100%) model trained and can be used to classify the agarwood oil quality accurately. Keywords: agarwood oil; k-nearest neighbours; quality; machine learning. Agarwood ; Classifier (UML) ; Artificial intelligence ; Machine learning ; Quality (philosophy) ; Computer science ; Pattern recognition (psychology) ; Mathematics Wood and Agarwood Research ; Forensic Fingerprint Detection Methods ; Image Processing and 3D Reconstruction
Authors: William F. Schneider | Hua Guo
Source: The Journal of Physical Chemistry A (article, 2018-02-01)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning ADVERTISEMENT RETURN TO ISSUEEditorialNEXTMachine LearningWilliam F. Schneider and Hua GuoCite this: J. Phys. Chem. A 2018, 122, 4, 879Publication Date (Web):February 1, 2018Publication History Published online1 February 2018Published inissue 1 February 2018https://pubs.acs.org/doi/10.1021/acs.jpca.8b00034https://doi.org/10.1021/acs.jpca.8b00034editorialACS PublicationsCopyright © 2018 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views5791Altmetric-Citations7LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (233 KB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Machine learning,Neural networks,Physical chemistry Get e-Alerts Citation ; Altmetrics ; Computer science ; Social media ; Icon ; World Wide Web ; Information retrieval ; Artificial intelligence Various Chemistry Research Topics ; Advanced Chemical Sensor Technologies ; Machine Learning in Materials Science
Authors: Xiaoyue Jiang | Feng Xiang | Chenxi Jia | Amanda R. Buchberger | Lingjun Li
Source: ACS Chemical Neuroscience (article, 2018-01-22)
Field: Chemistry — Spectroscopy

Relative Quantitation of Neuropeptides at Multiple Developmental Stages of the American Lobster Using <i>N</i>,<i>N</i>-Dimethyl Leucine Isobaric Tandem Mass Tags Neuromodulators and neurotransmitters play important roles in neural network development. The quantitative changes of these signaling molecules often reflect their regulatory roles in physiological processes. Currently, several commercial tags (e.g., iTRAQ and TMT) have been widely used in proteomics. With reduced cost and higher labeling efficiency, we employed a set of custom-developed N, N-dimethyl leucine (DiLeu) 4-plex isobaric tandem mass tags as an attractive alternative for the relative quantitation of neuropeptides in brain tissue of American lobster Homarus americanus at multiple developmental stages. A general workflow for isobaric labeling of neuropeptides followed by LC-MS/MS analysis has been developed, including optimized sample handling procedures. Overall, we were able to quantify 18 trace-amount neuropeptides from 6 different families using a single adult brain as a control. The quantitation results indicated that the expressions of different neuropeptide families had significant changes over distinct developmental stages. Additionally, our data revealed intriguing elevated expression of neuropeptides in the early juvenile development stage. The methodology presented here advanced the workflow of DiLeu as an alternative labeling approach and the application of DiLeu-based quantitative peptidomics, which can be extended to areas beyond neuroscience. Isobaric labeling ; Neuropeptide ; Tandem mass tag ; Homarus ; Biology ; Quantitative proteomics ; Proteomics ; Computational biology ; Tandem mass spectrometry ; Chemistry ; Neuroscience ; Bioinformatics ; Biochemistry ; Mass spectrometry ; Chromatography ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Neurobiology and Insect Physiology Research