jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Justin G. Reiner | Michael T. Schembri
Source: DigitalCommons - CalPoly (California State Polytechnic University) (article, 2018-01-01)
Field: Materials Science — Ceramics and Composites

SLM Materials Development The purpose of this project was to create a procedure to efficiently determine a “recipe” of parameter values that create a desired set of mechanical properties. Research was conducted into the laser powder bed fusion process with focus on underdeveloped materials. A design of experiment was used with a set of density correlated parameters to establish a method of producing nearly dense parts. Our methods include SLM printing, metrology, and statistical analysis. An experimental procedure for materials development was designed, but unable to be validated during the scope of this project, due to unforeseen safety issues (exposure to Chromium 6, a toxic substance). Next steps include validation of this procedure using various metal powders in a SLM 125 HL. Geology Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Silicone and Siloxane Chemistry
Authors: Alessandro David | Guido Burkard | Andor Kormányos
Source: 2D Materials (preprint, 2018-05-01)
Field: Materials Science — Materials Chemistry

Effective theory of monolayer TMDC double quantum dots Monolayer Transition Metal Dichalcogenides (TMDCs) are promising candidates for quantum technologies, such as quantum dots, because they are truly two-dimensional semiconductors with a direct band gap. In this work, we analyse theoretically the behaviour of a double quantum dot (DQD) system created in the conduction band of these materials, with two electrons in the (1,1) charge configuration. Motivated by recent experimental progress, we consider several scenarios, including different spin-orbit splittings in the two dots and including the case when the valley degeneracy is lifted due to an insulating ferromagnetic substrate. Finally, we discuss in which cases it is possible to reduce the low energy subspace to the lowest Kramers pairs. We find that in this case the low energy model is formally identical to the Heisenberg exchange Hamiltonian. Quantum dot ; Monolayer ; Hamiltonian (control theory) ; Condensed matter physics ; Semiconductor ; Electron ; Ferromagnetism ; Physics ; Band gap ; Conduction band ; Quantum mechanics ; Materials science ; Nanotechnology ; Mathematics 2D Materials and Applications ; Iron-based superconductors research ; Topological Materials and Phenomena
Authors: Gulraiz Ahmed | Nektaria Koursari | Anna Trybała | Víctor M. Starov
Source: Preprints.org (preprint, 2018-09-20)
Field: Materials Science — Surfaces, Coatings and Films

Sessile droplets on deformable substrates Wetting of deformable substrates has gained significant interest over the past decade due to its extensive applications and uses. This interest has developed due to technological advances which are able to capture interfacial behavior taking place when a liquid droplet is placed on a deformable substrate. Researchers have developed different theories to explain processes taking place in the process of wetting of deformable/soft substrates. For the scope of this review, we will consider the fluid to be Newtonian, partially wetting, and surface forces are incorporated with the help of disjoining/conjoining pressure acting in the vicinity of the apparent, three-phase contact line. The following subjects are briefly reviewed: (i) Equilibrium of droplets on soft substrates. It is shown that properties of the disjoining/conjoining pressure isotherm and properties of the deformable substrate determine both the shape of the liquid droplet and deformation of the substrate; (ii) Equilibrium conditions of droplets on deformable substrates. It is shown that for a droplet to be at equilibrium on a deformable substrate under consideration, Jacobi’s sufficient condition is satisfied; (iii) Hysteresis of contact angle of sessile droplets on deformable substrates. It is shown that as the elasticity of the deformable substrate is increased, both advancing and receding contact angles are reduced. Disjoining pressure ; Wetting ; Contact angle ; Substrate (aquarium) ; Hysteresis ; Materials science ; Elasticity (physics) ; Mechanics ; Newtonian fluid ; Nanotechnology ; Composite material ; Physics ; Geology ; Condensed matter physics Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Adhesion, Friction, and Surface Interactions
Authors: Vishnu Saseendran | Christian Berggreen | Leif A. Carlsson
Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne) (article, 2018-07-13)
Field: Materials Science — Materials Chemistry

Kinematic study of DCB-UBM sandwich fracture specimen Kinematics ; Materials science ; Composite material ; Fracture (geology) ; Structural engineering ; Geology ; Physics ; Engineering ; Classical mechanics High-Velocity Impact and Material Behavior ; Advanced ceramic materials synthesis ; Mechanical Behavior of Composites
Authors: Maxim Aleksandrovskiy | Lidiya Zaharova
Source: E3S Web of Conferences (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Bending of an Infinite beam on a base with two parameters in the absence of a part of the base Currently, in connection with the rapid development of high-rise construction and the improvement of joint operation of high-rise structures and bases models, the questions connected with the use of various calculation methods become topical. The rigor of analytical methods is capable of more detailed and accurate characterization of the structures behavior, which will affect the reliability of objects and can lead to a reduction in their cost. In the article, a model with two parameters is used as a computational model of the base that can effectively take into account the distributive properties of the base by varying the coefficient reflecting the shift parameter. The paper constructs the effective analytical solution of the problem of a beam of infinite length interacting with a two-parameter voided base. Using the Fourier integral equations, the original differential equation is reduced to the Fredholm integral equation of the second kind with a degenerate kernel, and all the integrals are solved analytically and explicitly, which leads to an increase in the accuracy of the computations in comparison with the approximate methods. The paper consider the solution of the problem of a beam loaded with a concentrated force applied at the point of origin with a fixed value of the length of the dip section. The paper gives the analysis of the obtained results values for various parameters of coefficient taking into account cohesion of the ground. Base (topology) ; Computation ; Mathematical analysis ; Mathematics ; Fredholm integral equation ; Beam (structure) ; Integral equation ; Physics ; Algorithm ; Optics Structural mechanics and materials ; Geodetic Measurements and Engineering Structures ; Engineering and Agricultural Innovations
Authors: Nikolaj Atarov
Source: MATEC Web of Conferences (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Calculation of a beam on an inhomogeneous basis The questions of analytical calculating the beam on the base with the modulus of deformation (modulus of elasticity), which is changed by thickness of the layer by power law, was deals in the article. The purpose of work was receiving finite expressions for reactive pressure of the basе on a beam and internal efforts in a beam when using model of the base with two characteristics (coefficient of subgrade resistance). A system of differential equations second-order with respect to the displacements of points the surface of a layer with a continuously changing modulus of elasticity was obtained based on the minimum of the total potential strain energy. The calculation of the rigid beam on the base on the action of the symmetric load was performed and the formulas for the reactive pressures of the base were got. Numerical calculation is executed and the analysis of influence of change of the module of deformation of a layer by the amount of reactive pressure and the bending moment in a beam was given. It is shown that with increase in the module of deformation on layer thickness the basе with two characteristics on nature of work is approaching vinklerovsky. Beam (structure) ; Bending moment ; Materials science ; Deformation (meteorology) ; Mechanics ; Elasticity (physics) ; Finite element method ; Young's modulus ; Structural engineering ; Mathematics ; Physics ; Composite material ; Engineering Structural mechanics and materials ; Material Properties and Applications ; Mechanical Systems and Engineering
Authors: Drahomír Rychecký
Source: Digital Library (University of West Bohemia) (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Modelování poddajných rotujících těles s kontaktními vazbami Disertační práce si klade za cíl seznámit čtenáře s jednou z možností modelování poddajných těles spojených kontaktními vazbami, jejichž vzájemný pohyb je relativně malý. Nejprve byla provedena rešerže, která byla východiskem pro zkoumání dané problematiky a má čtenáře seznámit s řešenou problematikou v širším spektru. Na ní navazuje druhá kapitola zaměřená na představení použitých matematických modelů. Postupy jsou popsány tak, aby je čtenář mohl přímo aplikovat a aby zcela objasňovaly modely použité v dalších částech práce. V závěru této kapitoly je uveden přístup pro zahrnutí vlivu rotace do modální redukovaného matematického modelu. Třetí a čtvrtá kapitola jsou čistě aplikační&#13;\n(popis použité modální redukce v kapitole 3 a modelování kontaktních vazeb v kapitole 4), obsahují výsledky jednotlivých naprogramovaných přístupů. Všechny potřebné programy jsou připravené v prostředí MATLAB. Získané výsledky jsou komentovány a vysvětlovány.&#13;\nStěžejní částí diserta?ní práce je 5. kapitola, kde jsou oba dva postupy (tj. modelování kontaktů poddajných těles a zahrnutí vlivu rotace do modální zredukovaného modelu) spojeny a jsou provedeny parametrické studie rotujícího olopatkovaného disku rhombických lopatek při uvažovaní kontaktu se třením a s předpětím. Pro validaci získaných výsledků posloužil komerční konečnoprvkový software ANSYS,&#13;\nkterý se standardně používá pro řešení problematiky tohoto typu. Z porovnání vypočtených deformací lopatek v kapitole 5 plyne, že prezentovaná metoda je použitelná pro řešení výpočtově složitých kontaktních úloh. Její výsledky dosahují vysoké shody s výsledky získanými přímou numerickou integrací v čase řešených v softwaru ANSYS, avšak řádově za kratší dobu. Theology ; Philosophy Textile materials and evaluations ; Computer Graphics and Visualization Techniques ; Material Properties and Processing
Authors: Dorothée Liebschner | Pavel V. Afonine | Matthew L. Baker | Gàbor Bunkòczi | Vincent B. Chen | Tristan I. Croll | Bradley J. Hintze | Li‐Wei Hung | Swati Jain | Airlie J. McCoy | Nigel W. Moriarty | Robert D. Oeffner | Billy K. Poon | Michael G. Prisant | Randy J. Read | Jane S. Richardson | David Richardson | Massimo Sammito | Oleg V. Sobolev | Duncan H. Stockwell | Thomas C. Terwilliger | Alexandre Urzhumtsev | Lizbeth L. Videau | Christopher J. Williams | Paul D. Adams
Source: Acta Crystallographica Section D Structural Biology (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in <i>Phenix</i> Diffraction (X-ray, neutron and electron) and electron cryo-microscopy are powerful methods to determine three-dimensional macromolecular structures, which are required to understand biological processes and to develop new therapeutics against diseases. The overall structure-solution workflow is similar for these techniques, but nuances exist because the properties of the reduced experimental data are different. Software tools for structure determination should therefore be tailored for each method. Phenix is a comprehensive software package for macromolecular structure determination that handles data from any of these techniques. Tasks performed with Phenix include data-quality assessment, map improvement, model building, the validation/rebuilding/refinement cycle and deposition. Each tool caters to the type of experimental data. The design of Phenix emphasizes the automation of procedures, where possible, to minimize repetitive and time-consuming manual tasks, while default parameters are chosen to encourage best practice. A graphical user interface provides access to many command-line features of Phenix and streamlines the transition between programs, project tracking and re-running of previous tasks. Software ; Workflow ; Computer science ; Neutron ; Graphical user interface ; Automation ; Computational science ; Physics ; Nuclear physics ; Engineering ; Mechanical engineering ; Database Enzyme Structure and Function ; Biochemical and Molecular Research ; Advanced Electron Microscopy Techniques and Applications
Authors: Judith Langer | Dorleta Jiménez de Aberasturi | Javier Aizpurua | Ramón A. Álvarez‐Puebla | Baptiste Auguié | Jeremy J. Baumberg | Guillermo C. Bazan | Steven E. J. Bell | Anja Boisen | Alexandre G. Brolo | Jaebum Choo | Dana Cialla‐May | Volker Deckert | Laura Fabris | Karen Faulds | F. Javier Garcı́a de Abajo | Royston Goodacre | Duncan Graham | Amanda J. Haes | Christy L. Haynes | Christian Huck | Tamitake Itoh | Mikael Käll | Janina Kneipp | Nicholas A. Kotov | Hua Kuang | Eric C. Le Ru | Hiang Kwee Lee | Jian‐Feng Li | Xing Yi Ling | Stefan A. Maier | Thomas G. Mayerhöfer | Martin Moskovits | Kei Murakoshi | Jwa‐Min Nam | Shuming Nie | Yukihiro Ozaki | Isabel Pastoriza‐Santos | Jorge Pérez‐Juste | Jürgen Popp | Annemarie Pucci | Stephanie Reich | Bin Ren | George C. Schatz | Timur Shegai | Sebastian Schlücker | Li‐Lin Tay | K. George Thomas | Zhong‐Qun Tian | Richard P. Van Duyne | Tuan Vo‐Dinh | Yue Wang | Katherine A. Willets | Chuanlai Xu | Hongxing Xu | Yikai Xu | Yuko S. Yamamoto | Bing Zhao | Luis M. Liz‐Marzán
Source: ACS Nano (article, 2019-09-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Present and Future of Surface-Enhanced Raman Scattering The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article. Raman scattering ; Nanotechnology ; Materials science ; Variety (cybernetics) ; Scattering ; Raman spectroscopy ; Engineering physics ; Computer science ; Physics ; Optics ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Jonathan Schmidt | Mário R. G. Marques | Silvana Botti | Miguel A. L. Marques
Source: npj Computational Materials (article, 2019-08-08)
Field: Materials Science — Materials Chemistry

Recent advances and applications of machine learning in solid-state materials science Abstract One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science. Interpretability ; Toolbox ; Machine learning ; Artificial intelligence ; Computer science ; Process (computing) ; Property (philosophy) ; Point (geometry) ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Giovanni Pizzi | Valerio Vitale | Ryotaro Arita | Stefan Blügel | Frank Freimuth | Guillaume Géranton | Marco Gibertini | Dominik Gresch | Charles Johnson | Takashi Koretsune | Julen Ibañez-Azpiroz | Hyungjun Lee | Jae-Mo Lihm | Daniel Marchand | Antimo Marrazzo | Yuriy Mokrousov | Jamal I Mustafa | Yoshiro Nohara | Yusuke Nomura | Lorenzo Paulatto | Samuel Poncé | Thomas Ponweiser | Junfeng Qiao | Florian Thöle | Stepan S Tsirkin | Małgorzata Wierzbowska | Nicola Marzari | David Vanderbilt | Ivo Souza | Arash A Mostofi | Jonathan R Yates
Source: Journal of Physics Condensed Matter (article, 2019-10-28)
Field: Materials Science — Materials Chemistry

Wannier90 as a community code: new features and applications Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states. It is interfaced to many widely used electronic-structure codes thanks to its independence from the basis sets representing these Bloch states. In the past few years the development of Wannier90 has transitioned to a community-driven model; this has resulted in a number of new developments that have been recently released in Wannier90 v3.0. In this article we describe these new functionalities, that include the implementation of new features for wannierisation and disentanglement (symmetry-adapted Wannier functions, selectively-localised Wannier functions, selected columns of the density matrix) and the ability to calculate new properties (shift currents and Berry-curvature dipole, and a new interface to many-body perturbation theory); performance improvements, including parallelisation of the core code; enhancements in functionality (support for spinor-valued Wannier functions, more accurate methods to interpolate quantities in the Brillouin zone); improved usability (improved plotting routines, integration with high-throughput automation frameworks), as well as the implementation of modern software engineering practices (unit testing, continuous integration, and automatic source-code documentation). These new features, capabilities, and code development model aim to further sustain and expand the community uptake and range of applicability, that nowadays spans complex and accurate dielectric, electronic, magnetic, optical, topological and transport properties of materials. Wannier function ; Brillouin zone ; Bridging (networking) ; Usability ; Software ; Automation ; Pseudopotential ; Independence (probability theory) 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Chi Chen | Weike Ye | Yunxing Zuo | Zheng Chen | Shyue Ping Ong
Source: Chemistry of Materials (article, 2019-04-10)
Field: Materials Science — Materials Chemistry

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in 11 out of 13 properties of the QM9 molecule data set. Similarly, we show that MEGNet models trained on ∼60 000 crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps, and elastic moduli of crystals, achieving better than density functional theory accuracy over a much larger data set. We present two new strategies to address data limitations common in materials science and chemistry. First, we demonstrate a physically intuitive approach to unify four separate molecular MEGNet models for the internal energy at 0 K and room temperature, enthalpy, and Gibbs free energy into a single free energy MEGNet model by incorporating the temperature, pressure, and entropy as global state inputs. Second, we show that the learned element embeddings in MEGNet models encode periodic chemical trends and can be transfer-learned from a property model trained on a larger data set (formation energies) to improve property models with smaller amounts of data (band gaps and elastic moduli). Generalization ; Computer science ; Moduli ; Graph ; Enthalpy ; Molecule ; Property (philosophy) ; Gibbs free energy ; Statistical physics ; Molecular graph ; Theoretical computer science ; Machine learning ; Artificial intelligence ; Thermodynamics ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Vahe Tshitoyan | John Dagdelen | Leigh Weston | Alexander Dunn | Ziqin Rong | Olga Kononova | Kristin A. Persson | Gerbrand Ceder | Anubhav Jain
Source: Nature (article, 2019-07-03)
Field: Materials Science — Materials Chemistry

Unsupervised word embeddings capture latent knowledge from materials science literature Computer science ; Property (philosophy) ; Identification (biology) ; Data science ; Sociology of scientific knowledge ; Knowledge extraction ; Point (geometry) ; Scientific literature ; Word (group theory) ; Table (database) ; Natural language processing ; Information retrieval ; Artificial intelligence ; Linguistics ; Data mining ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts