Anomalous roughening of wood fractured surfaces

Scaling properties of wood fractured surfaces are obtained from samples of three different sizes. Two different woods are studied: Norway spruce and Maritime pine. Fracture surfaces are shown to display an anomalous dynamic scaling of the crack roughness. This anomalous scaling behavior involves the existence of two different and independent roughness exponents. We determine the local roughness exponents ${\zeta}_{loc}$ to be 0.87 for spruce and 0.88 for pine. These results are consistent with the conjecture of a universal local roughness exponent. The global roughness exponent is different for both woods, $\zeta$ = 1.60 for spruce and $\zeta$ = 1.35 for pine. We argue that the global roughness exponent $\zeta$ is a good index for material characterization.Comment: 7 two columns pages plus 8 ps figures, uses psfig. To appear in Physical Review

ABINIT: Overview and focus on selected capabilities

Paper published as part of the special topic on Electronic Structure SoftwareABINIT is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe–Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the “temperaturedependent effective potential” approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which ABINIT relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The ABINIT DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library LIBPAW. ABINIT has strong links with many other software projects that are briefly mentioned.This work (A.H.R.) was supported by the DMREF-NSF Grant No. 1434897, National Science Foundation OAC-1740111, and U.S. Department of Energy DE-SC0016176 and DE-SC0019491 projects. N.A.P. and M.J.V. gratefully acknowledge funding from the Belgian Fonds National de la Recherche Scientifique (FNRS) under Grant No. PDR T.1077.15-1/7. M.J.V. also acknowledges a sabbatical “OUT” grant at ICN2 Barcelona as well as ULiège and the Communauté Française de Belgique (Grant No. ARC AIMED G.A. 15/19-09). X.G. and M.J.V. acknowledge funding from the FNRS under Grant No. T.0103.19-ALPS. X.G. and G.-M. R. acknowledge support from the Communauté française de Belgique through the SURFASCOPE Project (No. ARC 19/24-057). X.G. acknowledges the hospitality of the CEA DAM-DIF during the year 2017. G.H. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05-CH11231 (Materials Project Program No. KC23MP). The Belgian authors acknowledge computational resources from supercomputing facilities of the University of Liège, the Consortium des Equipements de Calcul Intensif (Grant No. FRS-FNRS G.A. 2.5020.11), and Zenobe/CENAERO funded by the Walloon Region under Grant No. G.A. 1117545. M.C. and O.G. acknowledge support from the Fonds de Recherche du Québec Nature et Technologie (FRQ-NT), Canada, and the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant No. RGPIN-2016-06666. The implementation of the libpaw library (M.T., T.R., and D.C.) was supported by the ANR NEWCASTLE project (Grant No. ANR-2010-COSI-005-01) of the French National Research Agency. M.R. and M.S. acknowledge funding from Ministerio de Economia, Industria y Competitividad (MINECO-Spain) (Grants Nos. MAT2016-77100-C2-2-P and SEV-2015-0496) and Generalitat de Catalunya (Grant No. 2017 SGR1506). This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation program (Grant Agreement No. 724529). P.G. acknowledges support from FNRS Belgium through PDR (Grant No. HiT4FiT), ULiège and the Communauté française de Belgique through the ARC project AIMED, the EU and FNRS through M.ERA.NET project SIOX, and the European Funds for Regional Developments (FEDER) and the Walloon Region in the framework of the operational program “Wallonie-2020.EU” through the project Multifunctional thin films/LoCoTED. The Flatiron Institute is a division of the Simons Foundation. A large part of the data presented in this paper is available directly from the Abinit Web page www.abinit.org. Any other data not appearing in this web page can be provided by the corresponding author upon reasonable request.Peer reviewe

Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties

To make efficient use of image-based rock physics workflow, it is necessary to optimize different criteria, among which: quantity, representativeness, size and resolution. Advances in artificial intelligence give insights of databases potential. Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties. In this study we prepare a set of thousands high-resolution 3D images captured in a set of four reservoir rock samples as a base for learning and training. The Voxilon software computes numerical petrophysical analysis. We identify different descriptors directly from 3D images used as inputs. We use convolutional neural network modelling with supervised training using TensorFlow framework. Using approximately fifteen thousand 2D images to drive the classification network, the test on thousand unseen images shows any error of rock type misclassification. The porosity trend provides good fit between digital benchmark datasets and machine learning tests. In a few minutes, database screening classifies carbonates and sandstones images and associates the porosity values and distribution. This work aims at conveying the potential of deep learning method in reservoir characterization to petroleum research, to illustrate how a smart image-based rock physics database at industrial scale can swiftly give access to rock properties

ABINIT: Overview and focus on selected capabilities

Abinit is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe–Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the “temperature-dependent effective potential” approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which abinit relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The abinit DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library libpaw. abinit has strong links with many other software projects that are briefly mentioned

The Abinit project: Impact, environment and recent developments

International audienceAbinit is a material- and nanostructure-oriented package that implements density-functional theory (DFT) and many-bodyperturbation theory (MBPT) to find, from first principles, numerous properties including total energy, electronic structure, vibrational and thermodynamic properties, different dielectric and non-linear optical properties, and related spectra. In the special issue to celebrate the 40th anniversary of CPC, published in 2009, a detailed account of Abinit was included [Gonze et al, Comput. Phys. Comm. 180, 2582 (2009)], and has been amply cited. The present article comes as a follow-up to this 2009 publication. It i ncludes an analysis of the impact that Abinit has had, through for example the bibliometric indicators of the 2009 publication. Links with several other computational materials science projects are described. This article also covers the new capabilities of Abinit that have been implemented during the last three years, complementing a recent update of the 2009 article published in 2016. Physical and technical developments inside the abinit application are covered, as well as developments provided with the Abinit package, such as the multibinit and a-tdep projects, and related Abinit organization developments such as AbiPy . The new developments are described with relevant references, input variables, tests, and tutorials