dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, integrated software environments provide guidance when it comes to use of the underlying computer codes and optimal use of the computational resources. Finally, these software packages lend themselves to broad exploration by allowing, for example, efficient scanning of compounds, crystal structures and their properties, providing thereby insights into structure-property relationships. The present review article serves a twofold goal: First, by means of several examples from recent research performed with industrial partners we demonstrate the capabilities and strengths of modern computational materials research based on highly efficient computer codes such as the Vienna Ab initio Simulation Package (VASP) and their integration into comprehensive software environments such as the MedeA software.
Second, we aim at showing the reader ways to translate such real-life industrial problems into computational tasks, which are accessible to the simulation tools at hand and thereby to arrive at solutions. Hence, the following sections provide recent applications of the prediction of materials properties using ab initio computations during materials engineering research and development. These applications include published as well as unpublished work. All investigations made use of the wide range of capabilities offered by the modern MedeA software environment  [1] , which range from building tools for crystalline, amorphous and disordered systems as well as for surfaces, interfaces and defects to analysis tools facilitating the display of vibrational, electronic, magnetic, optical and transport properties and include simulation techniques such as VASP, the Molecular Orbital PACkage (MOPAC), the Large-scale Atomic/Molecular Massive Parallel Simulator (LAMMPS), Gaussian, GIBBS, PHONON and UNCLE.
The elastic coefficients are computed using a fully automated, symmetry general approach [39] as implemented in MedeA  [1]. The results as summarized in Table  3  reveal considerable stiffening of the material with increasing C concentration between B 9 C and B 4 C as expected with the lowering of the enthalpy of formation within this sequence. Finally, the computed phonon dispersions for B 4 C confirm the thermodynamic stability of this compound and reveal an isolated high-frequency mode, which originates from bond-stretching vibrations of the B atoms in the C-B-C linear rods as illustrated in Figure  14 , which is consistent with the analysis of  Lazzari et al. [35]. In summary, using the UNiversal CLuster Expansion (UNCLE) code as well as calculations of the mechanical and vibrational properties we were able to identify the ground-state structures of boron carbides with boron concentrations varying between xB = 0.
High-Throughput Calculations Computational screening calculations, searching composition, condition, or configuration space, are becoming a routine component of CME and are supported in the MedeA high-throughput (MedeA HT) environment based on Flowcharts. An illustration of such a calculation is provided in Figure  15. Here a database search of materials containing magnesium results in a database 'hit-list' of 2886 systems based on search criteria including constraints on the total maximum number of elements present in the compound and the size of the unit cell for the material. For each of these systems, the strength of binding of magnesium within the system was evaluated automatically by computing the energy of the complete system, removing a magnesium atom, recomputing the system energy and computing the energy difference for both systems.
Acknowledgments: This article is based on published work performed as a contract research project for  Toyota Motors Europe and Infineon Technologies Germany. We are especially grateful to  F. Rosciano  of  Toyota Motors Europe  for initiating the project described in Section 3. 1, for proposing to focus on spinel-type compounds with Mn as a transition metal, and for carrying out the synthesis and characterization of the proposed compounds. We are likewise indebted to  M. Schneegans  and  W. Robl  of  Infineon Technologies Germany  for initiating the project described in Section 3. 2 as well as to all coauthors of Ref. [29] for their collaboration. All calculations described in this work were performed at the computer centre of  Materials Design. The continuous assistance of the computer department is gratefully acknowledged.
Most of the previous studies did not focus on quantifying the amount of global and local energy absorption during the impact process. A common problem in FEM is the excessive element distortions encountered in dynamic loading simulations  [19]. Element deletion approach could be used to erode highly distorted elements but presents inconsistencies and no physical fundamentals  [20]. In order to minimize this problem, several authors  [21] [22]  described the advantage of using adaptive meshing algorithm as an alternative technique for the analysis of plate-impact events. The scheme of the adaptive mesh available in some commercial FE software (e. g. , ABAQUS  [23] ) combines the features of Lagrangian and Eulerian analyses which allows for obtaining a high mesh quality during the whole simulation.
Mesh definition and boundary conditions used in numerical simulations The numerical simulations of the impact problem were carried out using the commercial software ABAQUS 6. 12/Explicit. The geometry of the targets and projectiles is the same as used in experiments. The mesh of the target plate included a total number of 264100 nodes and 242500 elements, see Figure  9. A total number of 12 elements were defined along the 4 mm thickness as recommended by Rodríguez-Martínez and co-authors  [9]. The mesh was divided into three different zones as follows (see Figure  9 ): • Zone A covers the contact region between the projectile and the target. The diameter of this region is similar to the diameter of the projectile. A mesh with 34900 eight-node brick hexahedral elements with one integration point, C3D8R in ABAQUS notation  [23] , was defined.
This analysis showed that the major phases of loess are quartz (~30 wt%) and feldspars (~70 wt%). The main components of calcrete are calcite (~95 wt%) and quartz (~5 wt%). Scanning electron microscopy with energy dispersive X-ray microanalysis (SEM/EDXS) was used to provide detailed information about the chemical composition of the powder samples, revealing the presence of traces of minerals like halite, barite and fluorapatite, which were not detected by XRD. The kinetic code KINDIS was used to carry out simulations using the minerals identified previously in their relative proportions to identify the signature of those geochemical phases on water chemistry. Experimental data from batch dissolution experiments were compared to simulated data. This investigation showed that water reached pseudo steady state concentrations due to the presence of fast dissolving phases like halite, barite, gypsum, plant phytolith.
These phases appeared to be of major importance in controlling the chemical composition of the Pampean groundwater. Furthermore, this work showed that the KINDIS software can be used on all kinds of aquifers as it is very easy to modify the parameters of the simulation to adapt it to numerous situations. The modeling is a very important tool for thermodynamic and kinetic studies of groundwater chemistry; it enables the prediction of water quality and can help to understand the impact of anthropic or natural contamination on the groundwater.
X-ray diffraction and Rietveld refinement The mineralogical composition of calcrete and loess samples was characterized by X-ray diffraction (XRD). Sample preparation procedure consisted of grinding the rocks finely enough to make sure that the powder could be mounted so that there were as many random grain orientations as possible. Mineralogical analyses were carried out on each sample that was subjected directly to powder XRD study. Diffraction data were collected using a Bragg-Brentano diffractometer model D5000, from Bruker AXS, equipped with a copper anode (Cu Kα radiation). The conditions for generating the X-ray beam were 30 mA and 40 kV. Scans were taken for 2θ ranges from 3 to 65°with 0. 03°/s steps, and a counting time of 25 s per step. Rietveld refinements were carried out with the program Fullprof  (Rodríguez-Carvajal, 2001) , following a standard procedure described in  Daval et al.
Stability diagrams The software KINDIS (KINetic of DISsolution)  (Madé et al. , 1994 ) was developed at the LHYGES (Laboratory of Hydrology and Geochemistry) in Strasbourg, France. It integrates kinetic rate laws for dissolution reactions and thermodynamic laws applied to aqueous chemical speciation calculations during water-rock interaction simulations. The chemical composition of the water samples measured in batch experiments was used for geochemical modeling with KINDIS. Using the cation [Na + ] and [Ca 2 + ] to proton activity [H + ] ratios, the measured compositions were plotted in stability diagrams  (Khorzinskii, 1965). The construction of these diagrams was done by plotting the concentration of calcium or sodium as a function of the measured orthosilicic acid concentration using the equilibrium conditions at a given temperature against the formation of various secondary phases for precipitating from solution.
Kinetic modeling Simulations of dissolution kinetics were carried out with KINDIS. As described in  Madé et al. (1994) , the simplified equation used to simulate the dissolution rate of a mineral m in the KINDIS geochemical code can be written as: = - + v K S a ( 1 Q K ) S dm pH dm eff m n H m m where k dm pH is the constant of the apparent dissolution rate intrinsic to mineral m at a given pH [mol/m 2 /year]; S m eff is the effective or reactive surface area at the mineral/aqueous solution interface [m 2 /kg H2O]; a H + n is the activity of the [H +] ions in the aqueous solution, where n is a real exponent with a generally positive value in acidic solutions, zero in neutral solutions and negative in basic solutions. (1 -Qm/Km) relates the dissolution rate to the saturation index of mineral m in an aqueous solution, where Qm is the ionic activity product and Km is the thermodynamic equilibrium constant known for the given temperature and pressure conditions from the database THERMODDEM (Thermochemical and Mineralogical Tables for Geochemical Modeling), from the Bureau de Recherches Géologiques et Minières (BRGM).
The simulations named Simulation 2 were run with the minerals detected by XRD and SEM/EDXS in loess and calcrete. In a third simulation, the amount of each mineral was adjusted to fit the experimental data. Attempts of adjusting the results of the model to the experimental concentrations of silica were realized first by adding phases like biotite or plant phytolith, because  Osterrieth and Martínez (1993)  demonstrated that the latter phases are more altered than volcanic glasses and are probably responsible for the high concentration of silica in the Pampean aquifer. A second step to adjust the concentration of fluoride consisted of adding F-biotite or fluorite into the simulations and of modifying the nominal composition of calcite in the thermodynamic database of KINDIS, by including 0.
1% of fluoride impurity. This value corresponds to the proportion of fluoride that could be present in calcite as an impurity  (Kitano and Okumura, 1973; Garcia et al. , 2006). An ultimate set of simulations was run over 60 years, which is considered to be an average of the residence time of the Pampean aquifer  (Martínez et al. , 2016). Simulations with only the major minerals (detected by XRD) were compared to simulations including all the minerals and their proportions that were needed to fit the experimental data from batch experiments. The software INFOSTAT (Di Rienzo et al. , 2011) was used to calculate the Root Mean Square Deviation. This represents the sample standard deviation of the differences between calculated values and observed values. The best fit between the experimental data and the simulated data was obtained when the RMSD is close to 0.
Stability diagrams The aqueous speciation was calculated with the KINDIS software in order to plot the results of the chemical composition of the water samples in contact with non-rinsed sediments, and estimate the stability of minerals against the solution composition. In general kaolinite and montmorillonite are the stable minerals pointing out the dissolution of plagioclase feldspars during batch experiments (Fig. 4 ), for which anorthite and albite represent the end-member feldspars. The horizontal line indicates the saturation with respect to calcite. The position of the line depends on the pCO 2. Here the pCO 2 has been considered as that of atmospheric levels (10 -3. 5 ) which defines Log (Ca + 2 )/(H + ) 2 = 13. 02 at equilibrium with calcite. Water samples of the batch experiment are generally very close to calcite saturation, showing that the solution reaches apparent steady-state values very quickly.
3. 2. 2. 5. Fitting fluoride concentrations. Several tests were done to fit the outputs of the simulation to the measured concentrations of fluoride. A first step consisted of adding 2% of minerals like fluorite or F-rich biotite. Another way consisted of modifying the chemical composition of calcite in the thermodynamic database of KINDIS by adding a minor proportion of fluorine. Neither F-biotite and fluorite, nor the addition of calcite impurity could improve the agreement between modeled and the measured concentrations.
Dissolution kinetics of the Pampean aquifer As recent studies based on 3 H/ 3 He and CFCs monitoring estimated that the residence time for groundwater in the area is between 3 and up to more than 60 years  (Martínez et al. , 2016) , batch simulations were performed with the KINDIS software over a 60 year-period. The first set of reactant phases included in the models was carried out on the major minerals detected in Simulation 1. Subsequently, all the minerals and their proportions needed to adjust the simulated data were used to perform additional simulations over 60 years. The modeled steady-state values for pH and concentrations were compared to the measured concentrations from wells taking water from the Pampean aquifer  (Martinez et al. , 2012). The results are shown in Table  5. If only major minerals are used, even after 60 years of simulation, the concentration measured in the Pampean aquifer could not be reached.
Dissolution kinetics in batch experiments The comparison of batch experiments realized with previous rinsing and no rinsing evidenced that this treatment removes the most reactive phases, such as halite and gypsum, resulting in a lower pH of the solution, a lower sodium concentration, and a lower calcium concentration for experiments conducted on loess. The batch experiments and the simulations performed with the KINDIS software, whilst considering a kinetic approach of dissolution processes, demonstrate that the observed compositions cannot be derived by considering a thermodynamic equilibrium with only the major minerals. Instead, accessory minerals detected by SEM/EDXS had to be included in the models to reach similar concentrations of anions and cations. The studies using KINDIS of the dissolution of loess showed that the pH of the solution increased as a function of time when reacting with quartz and feldspars.
In calcrete, calcite and quartz were found with trace amounts of halite and fluorapatite. To adjust the simulated data to the concentrations measured in batch experiments, it was necessary to add the minerals detected by SEM/EDXS in the KINDIS simulations, and also gypsum. The comparison between rinsed and nonrinsed sample experiments showed that the precipitation of halite and gypsum from dry and wet depositions in the non-saturated zone, and subsequent leaching to the aquifer, can explain most of the chloride, sodium and sulfate sources. Nevertheless, barite is an additional source for sulfur, as it was recognized in the SEM/EDXS determinations. Regarding the usually high silica concentrations observed in the Pampean aquifer, the observed concentrations during batch experiments can be obtained from KINDIS simulations by including biogenic silica (phytoliths) as a silica source.
The high concentration of fluoride could not be explained by dissolution of fluoroapatite, F-rich biotite, or trace amounts of fluoride incorporated in calcite. One of the possible sources of fluoride in water could be the desorption of this ion from the surface of other minerals. The calibration of this geochemical kinetic code using the results of batch experiments demonstrated the importance of accessory minerals in the determination of water chemistry. Even if the solution reached a steady state composition rapidly and the use of the equilibrium concept in groundwater is correct, this kinetic approach is a valuable way to unravel the origin of the water chemistry. The KINDIS software is easy to use as the parameters can be modified easily and can be applied in the study of various types of aquifers in Argentina and in the world.
Acknowledgements The authors are thankful to the  National Agency for Science and Technology Promotion (ANPCyT ,  PICT 2014 N°1529 ) which supported this study financially and to the  National Scientific and Technical Research Council (CONICET)   PIP2011-0392. We also acknowledge the  Mincyt-ECOS project for supporting international collaboration between the ICGyC/IIMyC in Argentina  and the  LHyGeS in France. The authors are also thankful to  Mr. G. Bernava  for chemical analysis,  Amelie Aubert  ( LHyGES ) for X-ray diffraction analyses and  Gilles Morvan  for SEM/EDXS analyses. The careful reviews and constructive comments by anonymous reviewers were also much appreciated for helping to improve the manuscript.
Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. In order to cover the surface area captured by today's experimental techniques and to achieve sufficient self-consistency, such models contain millions of nerve cells. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity).
It is for example essential to get a basic understanding of the datasets to better decide what statistical methods to use for more elaborate analysis. Furthermore, interactive visualization allows for explorative data analysis, including dimensionality reduction of complex datasets, highlighting of data points, and direct changes to visualization parameters. For the development of supportive visual analytics tools,  Shneiderman (1996)  introduced the so-called 'information-seeking mantra'. It describes the steps of common visual analysis workflows: "overview first, zoom and filter, details on demand". The first step provides a superficial 'overview' of the data. In the second step, 'zooming' into the dataset allows the user to get a more detailed view on a chosen data subset. Application of 'filters' implies a change in dimensionality of the data or the extraction of particular features.
, 2009). The software itself offers the possibility of drawing multiple concurrent windows with dynamic and interactive plots of voltages, currents, morphology shapes and phase planes that are updated while simulations of single-neuron models or neuron networks are running. 3D visualization is not directly supported, but NEURON's Python bindings also allows running simulations to interact with modern visualization software, as for example incorporated by NeuronVisio  (Mattioni et al. , 2012)  that relies on the OpenGL-accelerated Mayavi visualization toolset  (Ramachandran and Varoquaux, 2011). The simulation software for large-scale neuronal network models NEST 2  (NEural Simulation Tool,  Gewaltig and Diesmann, 2007)  does not provide built-in interactive visualization. The original authors state this in their first report  (Diesmann et al.
, 1995)  as a design decision based on two considerations. First, in 1995 the life time of graphics frameworks and libraries appeared much shorter than the envisioned period of relevance of a simulation code. Thus, only a software stack with a strict separation of levels would ensure platform independence and sustainability of NEST. Second, a basic idea of the project is to contribute to a software environment for 'in virtu' now often called 'in silico' experiments (restated in  Diesmann and Gewaltig, 2002). In this concept, the authors state, simulated data and experimental data should be analyzed with the same analysis tools to maximize comparability and reproducibility. At the same time researchers at the department of Physiology and the Center for Neural Computation of the Hebrew University in Jerusalem started to work on an integrated analysis and visualization platform based on Open Inventor 3.
VIOLA called Neural Data Analysis (NDA) but the project was abandoned with the advent of MATLAB  (Vaadia, 2017). Recently Nowke et al. took on the challenge to develop a simulator independent visualization platform for brain-scale neuronal networks. The VisNEST  (Nowke et al. , 2013 (Nowke et al. , , 2015) )  framework visualizes the spiking activity of multi-area network models (using as an example  Schmidt et al. , 2017)  in a virtual environment. The time-resolved activity data is mapped onto a 3D brain model. This enables the researcher to interact with the model in 3D to expose otherwise occluded parts of the brain and to relate brain activity to anatomy. In a different view, a dynamic 3D graph represents time course of spike exchange between different cortical areas. These representation of spatial information can be combined with classic charts such as spike raster plots.
Unlike the visualization concepts along which these tools have been developed, we here focus on concepts that expose the spatial organization of neuronal activity in layered networks and scale to signals from several square millimeters of brain surface. Beside the aforementioned softwares applicable with spike data, general-purpose multi-view frameworks exist with different design goals and contexts of use  (Roberts, 2007). One generic high-level example is GLUE 5  , a Python and OpenGL-based multi-view framework. Another powerful framework is the now neglected OpenDX 6. In contrast to the GLUE toolset, we here aim at web-based visualization. Easy access to libraries of common plotting functions and methods (scatter, line, surface plots etc. ) is provided for most common programming languages (C++, Python, MATLAB, etc.
). Nevertheless, a large amount of time and resources is still required to construct fully interactive visualization tools adhering to the principles outlined by  Shneiderman (1996)  and Wang  Baldonado et al. (2000). Including interactivity and time synchronization between different visualizations may be demanding in terms of software design and development time, however, existing plotting libraries can be used to realize the individual visualizations. As a reference implementation of our conceptual study, we introduce the interactive visualization tool VIOLA, an open-source, platform-independent and lightweight web-browser application. The tool is designed for initial visual inspection of massively parallel data generated primarily by simulations of spiking neuronal networks similar to the example network illustrated in Figure  1.