jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shô Itô | Go Ueno | Masaki Yamamoto
Source: Journal of Synchrotron Radiation (article, 2019-06-03)
Field: Materials Science — Materials Chemistry

DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo-loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. This system achieves fully automated accurate crystal centering without using X-ray irradiation of crystals, and can be used for fully automated data collection in high-throughput macromolecular crystallography. Convolutional neural network ; Beamline ; Crystal (programming language) ; Throughput ; Synchrotron ; Computer science ; Deep learning ; Crystallography ; Process (computing) ; Artificial intelligence ; Materials science ; Chemistry ; Physics ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Anton O. Oliynyk | Jillian M. Buriak
Source: Chemistry of Materials (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Virtual Issue on Machine-Learning Discoveries in Materials Science ADVERTISEMENT RETURN TO ISSUEPREVEditorialNEXTVirtual Issue on Machine-Learning Discoveries in Materials ScienceAnton O. OliynykAnton O. OliynykManhattan College, Riverdale, New York, United StatesMore by Anton O. Oliynykhttp://orcid.org/0000-0003-0732-7340 and Jillian M. BuriakJillian M. BuriakUniversity of Alberta, Edmonton, Alberta, CanadaMore by Jillian M. Buriakhttp://orcid.org/0000-0002-9567-4328Cite this: Chem. Mater. 2019, 31, 20, 8243–8247Publication Date (Web):October 22, 2019Publication History Published online22 October 2019Published inissue 22 October 2019https://doi.org/10.1021/acs.chemmater.9b03854Copyright © 2019 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views5153Altmetric-Citations17LEARN 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 InReddit PDF (2 MB) Get e-AlertsSUBJECTS:Electrical conductivity,Machine learning,Materials,Optimization,Thermoelectric materials Get e-Alerts Citation ; Icon ; Social media ; Computer science ; World Wide Web ; Information retrieval ; Library science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Takayuki Shiraiwa | Kengo Tamura | Manabu Enoki
Source: Materials Science and Engineering A (article, 2019-09-28)
Field: Materials Science — Biomaterials

Analysis of kinking and twinning behavior in extruded Mg–Y–Zn alloys by acoustic emission method with supervised machine learning technique Crystal twinning ; Materials science ; Acoustic emission ; Waveform ; Compression (physics) ; Alloy ; Magnesium alloy ; Stacking ; Composite material ; Microstructure ; Computer science ; Physics ; Nuclear magnetic resonance Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: Dan Jia | Haitao Duan | Shengpeng Zhan | Yongliang Jin | Bingxue Cheng | Jian Li
Source: Scientific Reports (article, 2019-12-30)
Field: Materials Science — Materials Chemistry

Design and Development of Lubricating Material Database and Research on Performance Prediction Method of Machine Learning Long developing period and cumbersome evaluation for the lubricating materials performance seriously jeopardize the successful development and application of any database system in tribological field. Such major setback can be solved effectively by implementing approaches with high throughput calculation. However, it often involves with vast number of output files, which are computed on the basis of first principle computation, having different data format from that of their experimental counterparts. Commonly, the input, storage and management of first principle calculation files and their individually test counterparts, implementing fast query and display in the database, adding to the use of physical parameters, as predicted with the performance estimated by first principle approach, may solve such setbacks. Investigation is thus performed for establishing database website specifically for lubricating materials, which satisfies both data: (i) as calculated on the basis of first principles and (ii) as obtained by practical experiment. It further explores preliminarily the likely relationship between calculated physical parameters of lubricating oil and its respectively tribological and anti-oxidative performance as predicted by lubricant machine learning model. Success of the method facilitates in instructing the obtainment of optimal design, preparation and application for any new lubricating material so that accomplishment of high performance is possible. Computer science ; Field (mathematics) ; Database ; Lubricant ; Computation ; Basis (linear algebra) ; Tribology ; Development (topology) ; Throughput ; Experimental data ; Data mining ; Mechanical engineering ; Algorithm ; Mathematics ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Lubricants and Their Additives
Authors: Angelo Maiorino | Manuel Gesù Del Duca | Jaka Tušek | Urban Tomc | Andrej Kitanovski | Ciro Aprea
Source: Energies (article, 2019-05-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks The thermodynamic characterisation of magnetocaloric materials is an essential task when evaluating the performance of a cooling process based on the magnetocaloric effect and its application in a magnetic refrigeration cycle. Several methods for the characterisation of magnetocaloric materials and their thermodynamic properties are available in the literature. These can be generally divided into theoretical and experimental methods. The experimental methods can be further divided into direct and indirect methods. In this paper, a new procedure based on an artificial neural network to predict the thermodynamic properties of magnetocaloric materials is reported. The results show that the procedure provides highly accurate predictions of both the isothermal entropy and the adiabatic temperature change for two different groups of magnetocaloric materials that were used to validate the procedure. In comparison with the commonly used techniques, such as the mean field theory or the interpolation of experimental data, this procedure provides highly accurate, time-effective predictions with the input of a small amount of experimental data. Furthermore, this procedure opens up the possibility to speed up the characterisation of new magnetocaloric materials by reducing the time required for experiments. Magnetic refrigeration ; Isothermal process ; Adiabatic process ; Materials science ; Artificial neural network ; Thermodynamics ; Experimental data ; Refrigeration ; Computer science ; Machine learning ; Magnetic field ; Mathematics ; Physics ; Magnetization ; Statistics Magnetic and transport properties of perovskites and related materials ; Advanced Thermoelectric Materials and Devices ; Shape Memory Alloy Transformations
Authors: Mônica C. Santos | Paloma Andrade Martins Nascimento | Wesley Nascimento Guedes | Edenir Rodrigues Pereira‐Filho | Érica Regina Filletti | Fabíola Manhas Verbi Pereira
Source: Eclética Química (article, 2019-04-25)
Field: Chemistry — Analytical Chemistry

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018 A compilation of papers published between 2014 and 2018 was evaluated. Many papers related to multivariate calibration and classification have been reported, as well as, design of experiments applications and artificial intelligence methods. Some applications were highlighted, as medical and pharmaceutical, food analysis, fuels, biological and forensic for the chemometric techniques on this review. Most studies are related to developing methods for practical solutions in industry or routine analysis. A promising scenario is shown considering the number of published papers: a total of 832 for this period using the keywords, multivariate classification, multivariate calibration, analysis, chemometrics, prediction, analytical chemistry, artificial neural networks (ANN), design of experiments (DoE) and factorial design. An useful overview for Analytical Chemistry researchers´ combined with Chemometrics is presented in this review. Chemometrics ; Multivariate statistics ; Calibration ; Computer science ; Multivariate analysis ; Biochemical engineering ; Management science ; Machine learning ; Engineering ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Daniele Almonti | Gabriele Baiocco | V. Tagliaferri | Nadia Ucciardello
Source: Materials (article, 2019-11-12)
Field: Materials Science — Biomaterials

Artificial Neural Network in Fibres Length Prediction for High Precision Control of Cellulose Refining Paper, a web of interconnected cellulose fibres, is widely used as a base substrate. It has been applied in several applications since it features interesting properties, such as renewability, biodegradability, recyclability, affordability and mechanical flexibility. Furthermore, it offers a broad possibility to modify its surface properties toward specifics additives. The fillers retention and the fibres bonding ability are heavily affected by the cellulose refining process that influences chemical and morphological features of the fibres. Several refining theories were developed in order to determine the best refining conditions. However, it is not trivial to control the cellulose refining as different phenomena occur simultaneously. Therefore, it is intuitively managed by experienced papermakers to improve paper structures and properties. An approach based on the machine learning aimed at estimating the effects of refining on the fibres morphology is proposed in this study. In particular, an artificial neural network (ANN) was implemented and trained with experimental data to predict the fibres length as a function of refining process variables. The prediction of this parameter is crucial to obtain a high-performance process in terms of effectiveness and the optimisation of the final product performance as a function of the process parameter. To achieve these results, data mining of the experimental patterns collected was exploited. It led to the achievement of excellent performance and high accuracy in fibres length prediction. Refining (metallurgy) ; Artificial neural network ; Cellulose ; Process (computing) ; Flexibility (engineering) ; Materials science ; Process engineering ; Biological system ; Computer science ; Artificial intelligence ; Mathematics ; Engineering ; Chemical engineering Advanced Cellulose Research Studies ; Material Properties and Processing ; Industrial Vision Systems and Defect Detection
Authors: Patxi Fernandez-Zelaia | Yuksel C. Yabansu | Surya R. Kalidindi
Source: Integrating materials and manufacturing innovation (article, 2019-03-28)
Field: Materials Science — Materials Chemistry

A Comparative Study of the Efficacy of Local/Global and Parametric/Nonparametric Machine Learning Methods for Establishing Structure–Property Linkages in High-Contrast 3D Elastic Composites Nonparametric statistics ; Parametric statistics ; Curse of dimensionality ; Contrast (vision) ; Property (philosophy) ; Computer science ; Finite element method ; Machine learning ; Gaussian process ; Artificial intelligence ; Process (computing) ; Sensitivity (control systems) ; Gaussian ; Data mining ; Mathematics ; Engineering ; Structural engineering ; Statistics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced Multi-Objective Optimization Algorithms
Authors: Carla‐Cezarina Pădurețu | Raluca Isopescu | Ileana Rău | Manuela Rossemary Apetroaei | Verginica Schröder
Source: Korean Journal of Chemical Engineering (article, 2019-11-01)
Field: Materials Science — Biomaterials

Influence of the parameters of chitin deacetylation process on the chitosan obtained from crab shell waste Chitosan ; Chitin ; Crystallinity ; Biopolymer ; Extraction (chemistry) ; Acetylation ; Chemistry ; Chemical engineering ; Degree (music) ; Process engineering ; Materials science ; Pulp and paper industry ; Biological system ; Chromatography ; Organic chemistry ; Engineering ; Physics ; Biochemistry ; Biology ; Polymer Nanocomposite Films for Food Packaging ; Microencapsulation and Drying Processes ; Animal Nutrition and Physiology
Authors: Cas van der Oord | Geneviève Dusson | Gábor Cśanyi | Christoph Ortner
Source: arXiv (Cornell University) (preprint, 2019-10-14)
Field: Materials Science — Materials Chemistry

Regularised Atomic Body-Ordered Permutation-Invariant Polynomials for the Construction of Interatomic Potentials We investigate the use of invariant polynomials in the construction of data-driven interatomic potentials for material systems. The "atomic body-ordered permutation-invariant polynomials" (aPIPs) comprise a systematic basis and are constructed to preserve the symmetry of the potential energy function with respect to rotations and permutations. In contrast to kernel based and artificial neural network models, the explicit decomposition of the total energy as a sum of atomic body-ordered terms allows to keep the dimensionality of the fit reasonably low, up to just 10 for the 5-body terms. The explainability of the potential is aided by this decomposition, as the low body-order components can be studied and interpreted independently. Moreover, although polynomial basis functions are thought to extrapolate poorly, we show that the low dimensionality combined with careful regularisation actually leads to better transferability than the high dimensional, kernel based Gaussian Approximation Potential. Invariant (physics) ; Permutation (music) ; Interatomic potential ; Mathematics ; Pure mathematics ; Physics ; Combinatorics ; Mathematical physics ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum Mechanics and Non-Hermitian Physics
Authors: Ken‐ichi Nomura | Rajiv K. Kalia | Aiichiro Nakano | Pankaj Rajak | Priya Vashishta
Source: SoftwareX (article, 2019-12-27)
Field: Materials Science — Materials Chemistry

RXMD: A scalable reactive molecular dynamics simulator for optimized time-to-solution RXMD is a parallel reactive molecular dynamics (RMD) simulator designed to perform large-scale RMD simulations on commodity laptop computers to supercomputer platforms. With extensive Time-to-Solution (TtoS) optimization techniques and scalable algorithms implemented, RXMD enables researchers to explorer materials design space that requires vast spatial-extent and long-time atomistic events with highly accurate chemistry. Extensible and modular software architecture allows to implement innovative optimization techniques. RXMD is one of core simulation engines at Material Genome Innovation for Computational Software (MAGICS) center, and is freely distributed as an open-source software from MAGICS website and Github. RXMD is also used as computational materials courseware to provide basic training of parallel RMD simulations for computational researchers and community. Computer science ; Scalability ; Modular design ; Laptop ; Supercomputer ; Software ; Computational science ; Emulation ; Distributed computing ; Computer architecture ; Parallel computing ; Operating system Machine Learning in Materials Science ; Graphene research and applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Xiong Yang | Ziyi Li | Yingshu Liu | Yi Xing | Jinchao Wei | Bentao Yang | Chuanzhao Zhang | Ralph T. Yang | Chuen‐Jinn Tsai
Source: Aerosol and Air Quality Research (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Research Progress of Gaseous Polycyclic Aromatic Hydrocarbons Purification by Adsorption Gaseous polycyclic aromatic hydrocarbons (PAHs) are carcinogenic and mutagenic pollutants and also able to promote the growth of secondary particulate matters, which have attracted great attention in recent years. Control of PAHs emissions in exhaust gases using different techniques has been hot topics in the atmospheric environment field. Adsorption is one of the most effective ones and is well accepted by industries in gaseous pollutant removal in which the adsorbent characteristics play critical roles. In this paper, the latest advances in the field of PAHs adsorption are critically reviewed with special emphasis on adsorption-desorption characteristics and related applications. Studies on adsorption equilibrium, kinetics, and desorption properties of PAHs as special low-volatile gases on conventional activated carbons and novel mesoporous adsorbents are summarized and compared, showing the advantages of mesoporous adsorbents over traditional adsorbents in terms of the fast adsorption and facile desorption. The significant factors on the optimal balance between adsorption and desorption as well as the corresponding adsorbent materials are discussed in detail. This work can be a reference for further studies on adsorptive purifications of PAHs and other low-volatile gases. Adsorption ; Desorption ; Mesoporous material ; Pollutant ; Chemistry ; Environmental chemistry ; Particulates ; Chemical engineering ; Organic chemistry ; Catalysis Catalytic Processes in Materials Science ; Toxic Organic Pollutants Impact ; Mesoporous Materials and Catalysis
Authors: Wilmer Leal | Guillermo Restrepo
Source: Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2019-04-01)
Field: Chemistry — Physical and Theoretical Chemistry

Formal structure of periodic system of elements For more than 150 years, the structure of the periodic system of the chemical elements has intensively motivated research in different areas of chemistry and physics. However, there is still no unified picture of what a periodic system is. Herein, based on the relations of order and similarity, we report a formal mathematical structure for the periodic system, which corresponds to an ordered hypergraph. It is shown that the current periodic system of chemical elements is an instance of the general structure. The definition is used to devise a tailored periodic system of polarizability of single covalent bonds, where order relationships are quantified within subsets of similar bonds and among these classes. The generalized periodic system allows envisioning periodic systems in other disciplines of science and humanities. Periodic system ; Periodic sequence ; Quasi periodic ; Periodic table ; Order (exchange) ; Polarizability ; Periodic function History and advancements in chemistry ; Quasicrystal Structures and Properties ; Graph theory and applications
Authors: Leon Otis | Eric Neuscamman
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo We present a comparison between a number of recently introduced low-memory wave function optimization methods for variational Monte Carlo in which we find that first and second derivative methods possess strongly complementary relative advantages. While we find that low-memory variants of the linear method are vastly more efficient at bringing wave functions with disparate types of nonlinear parameters to the vicinity of the energy minimum, accelerated descent approaches are then able to locate the precise minimum with less bias and lower statistical uncertainty. By constructing a simple hybrid approach that combines these methodologies, we show that all of these advantages can be had at once when simultaneously optimizing large determinant expansions, molecular orbital shapes, traditional Jastrow correlation factors, and more nonlinear many-electron Jastrow factors. Monte Carlo method ; Ansatz ; Derivative (finance) ; Perspective (graphical) ; Variational Monte Carlo ; Hybrid Monte Carlo ; Mathematical optimization ; Quasi-Monte Carlo method ; Applied mathematics ; Markov chain Monte Carlo ; Computer science ; Quantum Monte Carlo ; Statistical physics ; Mathematics ; Physics ; Artificial intelligence ; Mathematical physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Yiming Fang | Fan Yang | Zhu Zhou | Lujun Lin | Xiaoqin Li
Source: Journal of Spectroscopy (article, 2019-11-14)
Field: Chemistry — Analytical Chemistry

Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears Wavelength selection is a challenging job for the detection of the bruises on pears using hyperspectral imaging. Most modern research used the feature wavelength set selected by a single selection method which is generally unable to handle the wide variability of the hyperspectral data. A novel framework was proposed in this work to increase the performance of the bruise detection, through combining three state-of-the-art variable selection methods and the concept of feature-level integration. Successive projection algorithm, competitive adaptive reweighted sampling, and RELIEF were first applied to the spectra of the Korla pear, respectively. Then, the corresponding feature wavelength subsets were integrated and an optimal feature wavelength set was constructed. An ELM-based classifier was employed for the pear bruise identification finally. Experimental results demonstrated that the feature wavelength integration resulted in lower detection errors. The proposed method is simple and promising for bruise detection of Korla pears, and it can be utilized for other types of defects on fruits. Hyperspectral imaging ; Bruise ; Feature selection ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Classifier (UML) ; Feature (linguistics) ; Projection (relational algebra) ; Computer vision ; Algorithm Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Postharvest Quality and Shelf Life Management