dataesr/scientific-paragraphs-categorization huggingface dataset:
Note, however, that with main memory sizes commonly available today even on lowend computers (a few GiB) the MUMPS-based solver is able to tackle systems of more than 10 6 sites. To be able to handle even larger systems RGF has been implemented as well. (It is not yet part of the public release of Kwant as of version 1. 0. ) The shown memory consumption is the additional memory required for the computation on top of a basic constant requirement (71 MiB for Kwant, 35 MiB for the RGF solver) that is small by today's standards but whose inclusion would have obstructed the power-law character of the curves. It was measured as the increase of the maximum "resident set size" taken up by a given computation over that reported for an empty run of the respective software.
This paper describes version 1. 0 of Kwant, the first version released to the public. Kwant is a work in progress and there are many ideas for improvements collected in a to-do-list that is available with the source code. For instance, more solvers could be added and Kwant's low-level system format could be modified to allow general symmetries. Kwant is free (open source) software  [30]  (distributed under the liberal "simplified BSD license"), and we hope that the project's website http://kwant-project. org/ and mailing list http://kwant-project. org/community will develop into a hub for a community of users and developers. Contribu-tions to Kwant as well as the sharing of related code modules are welcome. In a completely similar fashion, a 3-d cubic lattice may be defined as cubic = kwant.
A. 3. Values of sites and hoppings In the previous subsection the onsite Hamiltonians and hopping values were just scalar constants. While this allows to define any tight-binding system by introducing a sufficient number of sublattices, in many cases it is useful to also allow these values to be matrices. Thanks to the flexibility of Python, this works in the most natural way in Kwant. If instead of a scalar value of the Hamiltonian one needs to use a matrix value, one may just write.
sys[lat( , )] = numpy. array([[ , -1j], [1j, ]]) sys[lat( , 1)] = tinyarray. array([[1, ], [ , -1]]) Here we set the Hamiltonian of two sites to be equal to the Pauli matrices σ y and σ z. We used two different ways to define a matrix-like object: as a NumPy array, and a tinyarray. NumPy  [28]  is the standard Python array library, and tinyarray is a library developed for Kwant, specifically optimized to be fast with small arrays, as discussed in Appendix C. In this application, the only difference between these two is performance, which is better for tinyarray arrays. The number of orbitals may be different for different sites as long as the sizes of all the value matrices are consistent: the hopping integral H i j from site j to i must have the size n i × n j , with n i and n j the sizes of onsite Hamiltonian of these two sites.
A. 4. Acting on multiple sites/hoppings at once The features of builders that have been introduced so far are in principle sufficient to define (site-by-site and hoppingby-hopping) systems with an arbitrarily complex geometry. In order to facilitate a higher level of abstraction, in addition to the simple keys (single sites and single hoppings) builders can also be indexed by composite keys that combine multiple simple keys. This feature has been inspired by the "slicing" of some Python containers and by NumPy's "fancy indexing". The most basic kind of these advanced keys is a list of simple keys. With such a list it is possible to assign a value to multiple sites or hoppings in one go. We now create a list of sites of the lattice lat belonging to a disk. This list of sites can be added to the system in one step: sys[sites] =.
builder. HoppingKind((1, ), lat, lat)] = 1 sys[kwant. builder. HoppingKind(( , 1), lat, lat)] = 1 To go even further, Kwant can calculate a list of all the n-th nearest neighbor hopping kinds on any lattice. That list, returned by the method neighbors can be used directly as a key: sys[lat. neighbors(1)] = 1 (The argument 1 to lat. neighbors means that nearest neighbors are to be found and could have been omitted since it is the default value. ) This high-level key allows to access the n-th nearest neighbors of any regular lattice in a single line of code. The complete yet very compact construction script for the circular quantum dot is listed in Appendix D. 2 and its output is shown in Fig. 9. For further information about keys we refer to the documentation of Kwant, specifically the documentation of the method expand of Builder.
B. 3. Exact diagonalization of a finite system Hamiltonian One of the design objectives of Kwant was to make it easy to perform arbitrary user-specified computations with complex tight-binding systems. When the included solvers do not provide the desired calculation it can be often implemented in just a few lines of Python. (In fact, this is nothing else than writing a solver. ) As an example, we apply the ARPACK library  [19]  to solve a large scale eigenvalue problem and calculate the lowest eigenenergies of a finite system. ARPACK is easily accessible from Python thanks to SciPy. Kwant's hamiltonian_submatrix can return the Hamiltonian matrix in a sparse format understood by SciPy, so there remains almost nothing to be done: H = sys. hamiltonian_submatrix(sparse=True) print scipy. sparse.
C. 2. Programming languages used Kwant is written primarily in Python. While some parts of it are implemented in other languages, all these pieces are held together by Python code. Care has been taken to optimize all the asymptotically most expensive components. For the less critical parts pragmatic choices were made: If some component was measured to be too inefficient for typical work loads, it was optimized gradually until a satisfactory performance was obtained. Overall, all of the following approaches are present in Kwant: 1. Usage of pure Python (often delegating low-level operations to libraries like NumPy and SciPy). 2. Wrapping of existing (but previously unavailable for Python) libraries via Cython  [39]. 3. Direct implementation in Cython or C/C++. For the quantum transport solvers, a combination of approaches 1 and 2 is used.
There are currently two solvers, both based on solving the sparse linear system defined by Eqs. (4, 8) : One utilizes the libraries UMFPACK  [40, 41]  or SuperLU  [42, 43]  that are wrapped by SciPy. The other uses the more efficient MUMPS library  [17, 18]  that, however, is not included in SciPy and needs to be installed separately. The MUMPS-based solver makes use of the nested dissection orderings provided by Metis  [44]  and Scotch  [45]. The part of Kwant that calculates modes and self-energies of leads employs the same approach: Pure Python code uses the services of the LAPACK library  [46]  that was specially wrapped with Cython as NumPy and SciPy do not provide all the necessary LAPACK functions. Because highly optimized libraries perform the "heavy lifting" in these asymptotically most expensive parts of Kwant, the fraction of total running time spent in these libraries slowly approaches 100% for all of Kwant as system size grows.
In practice more than half of all time is spent in such libraries for all but small systems which means that even if all of Kwant would have been implemented in highly optimized C or Fortran, the additional speed-up one could hope for would be less than a factor of two. Given that this would be much more work, and that it would prevent one from profiting from the dynamic features of Python, we believe that such optimization would not be reasonable. The routine hamiltonian_submatrix that creates a representation of a (sub)matrix of the Hamiltonian of a Kwant system is an example of a component that has been optimized using Cython even though its execution time is only linear in terms of the number of sites (for sparse matrices). The fact that this routine is called each time a system is solved for a given set of parameters makes it more performance-relevant than the construction and finalization a system that need to be performed only once for a given geometry.
The most low-level optimization of Kwant is embodied by the module tinyarray that has been implemented in pure C++ and is available as a stand-alone library for Python. Kwant uses many instances of small vectors and matrices that cannot be collected into larger arrays, the most frequently used example being the tags and coordinates of lattice sites. These small vectors and matrices could be represented by NumPy arrays or by Python tuples but both solutions are unsatisfactory: NumPy arrays were not designed for this application and are hence too resource-hungry when used in great numbers. Additionally, they cannot be used as keys for Python dictionaries. Python tuples, on the other hand, do not provide mathematical operations and have a high memory-overhead as well, since each element of the tuple exists as an individual Python object.
Acknowledgements We gratefully acknowledge contributions by  D. Jaschke , and  J. Weston , as well as discussions with  P. Carmier ,  M. Diez ,  C. Fulga ,  B. Gaury ,  B. van Heck ,  D. Nurgaliev , and  D. Wecker. We thank  C. Gohlke  for the creation of Windows installers. ARA and MW were supported by the  Dutch Science Foundation NWO/FOM  and by the  ERC   Advanced Investigator Grant  of  C. W. J. Beenakker  who enthusiastically supported this project. ARA was partially supported by a  Lawrence Golub  fellowship. CWG and XW were supported by the  ERC  Consolidator Grant MesoQMC as well as by the E. U. flagship graphene. XW also acknowledges support from the  STREP ConceptGraphene.
5 A T(LIESST) database was initially developed to study the light-induced metastable state of Fe II N 6 SCO complexes. 5b] The studied complexes include mononuclear FeN 6 , Fe II phosphorus, and dinuclear Fe II complexes, Prussian blue analogues, as well as Fe III compounds. These complexes follow a general relationship: T(LIESST) = T 0 -0. 3T 1/2 , where T 0 is an empirical parameter that depends on the chemical nature of the ligands. T 0 is 100, 120, 150, and 200 K for monodentate ligands, bidentate ligands, tridentate ligands, and solid 3D networks, respectively. In addition, one more group has been established for the compound studied in this paper, which has a macrocyclic ligand and T 0 = 180 K. Until now, [Fe(L 222 N 3 O 2 )(CN) 2 ]•H 2 O is the only compound belonging to this T 0 = 180 K line.
These data are represented in the form of χ M T-vs-T plots, where χ M is the molar magnetic susceptibility corrected for the diamagnetism of the closed-shell core and T is the temperature. In situ dehydration was performed inside the SQUID chamber at 380 K with continuous purging of with helium gas for 90 min. Thermaltrapping measurements were performed by following a published procedure. Samples were rapidly cooled to 10 K within one minute. Then, the temperature was increased at a rate of 0. 3 K min -1. The T(TIESST) temperature was estimated from the minimum of the derivative curve. Powder X-ray Diffraction: Variable-temperature X-ray diffraction patterns were recorded with a horizontally mounted INEL cylindrical position-sensitive detector (CPS 120) operating by gas ionization (Argon + C 2 H 6 ) by using Debye-Scherrer geometry (angular step ca.
Acknowledgements The authors gratefully acknowledge the support of the  MIND (Multifunctional and Integrated Piezoelectric devices) European Network of Excellence  ( NoE 515757-2 of the 6th Framework Program ) and the  Region Centre  who supports the  CEZnO  project ( Convertisseur Electromécanique  à base de nanofils ZnO, 2011 to 2014). The authors also thank  Drs. D. Valente  and  V. Grimal  for their technical assistance in material characterization experiments.
ACKNOWLEDGMENTS We thank  S. T. Bramwell  for discussions concerning the simulated neutron-scattering plots and issues related to Ref. [22]. It is also a pleasure to thank  O. Benton ,  M. Faulkner ,  T. Fennell ,  M. J. P. Gingras ,  J. Gardner ,  V. Kaiser ,  A. C. Maggs ,  P. McClarty ,  R. Moessner , and  K. Penc  for useful discussions. P. C. W. H. thanks the  Institut Universitaire de France  for financial support and is also grateful to the  Yukawa Institute for Theoretical Physics, Kyoto , for hospitality during the workshop  NQS2011.
The maximum pressure level was set at 10 bars for each experiment and (3) it allows the input of the optical fibre to monitor and record the temperature throughout the synthesis. The reaction temperature was set at 150 °C ± 0. 3 °C using the Labview Ò software to which the pressure sensor and optical fibre are connected. The reactor was regularly manually agitated as done under conventional heating in order to respect identical experimental conditions. Different synthesis times of ZSM-11 zeolites under microwave were studied. In order to study the influence of dielectric heating in the different steps of the zeolite synthesis (induction, nucleation and crystal growth), both kind of heating, dielectric and conventional, were combined. Thus, the reactor was first heated under microwave irradiation at 150 °C and then it was transferred into an oven under conventional heating at the same temperature.
Verification of the crystalline structure of ZSM-11 zeolites The verification of the crystalline structure of microwaveassisted ZSM-11 zeolites was performed by X-ray diffraction technique (XRD) of the ZSM-11 zeolite powders in a Siemens D5000 diffractometer. In all measurements, CoKa radiation having a wave length of 0. 1789 nm was used. The diffraction data of the ZSM-11 zeolites were collected between 2H ¼ 5-60 in intervals of 0. 1°a nd speed of 2°per min. The samples' volume and mass were kept constant for each X-ray diffraction run.
The milled powders were consolidated by SPS in order to produce compacts of 20 mm diameter and 6 mm thickness. The maximum temperature and compressive stress applied during the SPS process were 1200uC and 64 MPa respectively. These parameters are maintained during 15 s, the time of the complete SPS cycle was 35 min. The densities of the consolidated samples were measured by Helium pycnometer (AccuPyc II 1340). The interstitial elements content (O, N and C) were quantified using a combustion analyser model LECO TC500 for O and N content and LECO CS230 for C. The crystallite size and microstrain data have been obtained by Scherrer Method using the X'pert Highscore software through X-ray diffraction patterns collected on an X'pert Phillips using CuKa radiation. The microstructure of alloys were investigated using SEM (Phillips XL-30), EBSD detector (FEI Nova NanoSEM 450 SEM/EBSD) and TEM (Tecnai F20 electronic transmission electron microscope).
Magnetic measurements SQUID magnetometer and PPMS. The magnetic data shown in all gures except S51 and S52 were acquired on a Quantum-Design MPMS-XL SQUID magnetometer and a Quantum Design physical property measurement system (PPMS). Magnetisation data were obtained with selected elds from 0 to 5 T and ac data obtained with frequencies 1-1500 Hz (MPMS) and up to 10 kHz (PPMS) with an ac eld amplitude of 3. 0-3. 8 Oe (MPMS) and 10 Oe (PPMS) with or without applications of static eld. Single crystals were checked by single-crystal X-ray diffraction for phase purity before each measurement. Single-crystal magnetic data were obtained with a horizontal rotator setup. Prior to all measurements the response of the rotator was determined and subtracted from the response in the real experiment.
Inelastic neutron scattering INS spectra were obtained on the time-of-ight spectrometers IN4 and IN5 located at Institut Laue-Langevin, Grenoble, France. Several grams of non-deuterated samples were measured in double-wall Al/Mg sample cans or as wrapped in Al foil at selected temperatures and incoming neutron wavelengths. The Y III analogues 1 0 , 2 0 and 3 0 were systematically measured with the same settings to facilitate a precise assignment of the phonon spectra. The data were reduced and analysed using the Large Array Manipulation Program (LAMP). 17 Magnetic (de)excitations were localised by their characteristic |Q| (linear momentum transfer) dependence which follows the magnetic form factor, their temperature variation and the comparison with the spectra of the diamagnetic Y III analogues.
t has the properties of a spin-1/2 angular momentum mapped on to the lowest Kramers doublet. The coordinate frame is chosen to be identical with that of the zfs Hamiltonian eqn (1) (vide infra), i. e. , the z-axis coincides with the molecular C 3 axis. All spin-Hamiltonian calculations shown in this work are based on full diagonalisation. The matrices of the extended Stevens operators were generated with the help of the stev function from the EasySpin package written by Stefan Stoll. 21 he powder average of the magnetisation for large elds was generated using a 110-point Lebedev-Laikov grid. 22 b initio calculations. The ab initio calculations were carried out using the Molcas 7. 8 package program. 23 The calculations for 1 were performed using the measured crystal structures at 122 and 293 K.
The spin-orbit interaction was considered within the Douglas-Kroll-Hess Hamiltonian in the mean-eld approximation. The spin-orbit coupling was taken into account within the restricted active space state interaction (RASSI) method,  25  by mixing all spin-quartet states (35) and all spin-doublet states (112). On the basis of the resulting spinorbital multiplets, the SINGLE_ANISO  26  program was used to compute the magnetic properties and the LF parameters. Further, we tested the effect of the crystal environment (Madelung potential) on the low-lying energy states. To this end, the Madelung potential was approximated by ve layers of point charges. Every point charge was placed at the Cartesian position of each atom belonging to the crystal environment. The charge value assigned to each atom was the calculated Mulliken charge of the corresponding atom in the ground state, taken from the previous CASSCF calculation on the individual molecule.
Acknowledgements We thank  Dr M. Medarde-Barragan  for help with the PPMS measurements. J. D. acknowledges funding by the  Ambizione program of the Swiss National Science Foundation. K. S. P. thanks the  Danish Ministry of Science, Innovation and Higher Education  for an EliteForsk travel scholarship. J. B. thanks the  Carlsberg Foundation  and the  Villum Foundation  for support for diffraction equipment. H. W. and J. B. acknowledge support from the  Danish Research Council FNU  ( 12-125226 ). L. U. is a postdoc of the  FWO-Vlaanderen  ( Flemish Science Foundation ). Methusalem and INPAC projects of the  KU Leuven  are also gratefully acknowledged.
According to the standard normalization procedure  [15] , we plot (dI/dV )/(I/V ) to obtain the local density of states (LDOS). I/V is obtained by gaussian convolution, that does not affect the peak positions. High-quality single crystals of BiTeI, in the form of platelets, were grown by chemical vapor transport and by the Bridgman technique. Transport measurement show a typical n-type degenerate semiconducting behavior. Cleaves were performed at room temperature, using scotch tape, at a pressure of ≈ 10 -9 mbar. The cleaved samples were quickly transferred to the STM head (P ≈ 10 -10 mbar) where they can be measured for several hours. Firstprinciples electronic structure calculations were performed within the density functional theory (DFT) framework employing the generalized gradient approximation (GGA) as implemented in the QUANTUM-ESPRESSO package  [16].
Acknowledgments This research was supported by the  US Department of Energy  under contracts  DE-AC02-76SF00515  and  DE-SC0009937. DAKs research is supported by the  European Research Council  under  Advanced Investigator Grant   ERC-AdG-228301. DM's work was supported by the  Research Executive Agency (REA)  of the  European Union  under the Grant Agreement number  PITN-GA-2010-264564  (LHCPhenoNet). SH's work was partly supported by a grant from the  US LHC Theory Initiative  through  NSF  contract  PHY-0705682. This research used resources of  Academic Technology Services at UCLA.
ACKNOWLEDGMENTS A. D. , B. D. , L. T. , and F. G. acknowledge the  SEMIRAMIS  staff for carrying out ion irradiations at high temperature. XRD measurements on the Panalytical diffractometer have been performed at the nanocenter CTU-IEF-Minerve that is partially funded by the  "Conseil G en eral de l'Essonne. "This work was partially funded by the  NEEDS-Mat eriaux program. J. C. is grateful to  R egion Limousin  for partial funding of this study.
Characterization To study the surface morphology and the size of electrospun fibers, a scanning electron microscope (SEM; JEOL JSM-6390, JEOL Ltd. , Akishima-shi, Japan) and a transmission electron microscope (TEM; HITACHI H-9000, Hitachi, Ltd. , Chiyoda-ku, Japan) were used. The crystalline phase or phases present in the composited fibers were identified by Fourier transform infrared (FTIR) spectroscopy using a Thermo Scientific Nicolet iN10 spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), and absorbance data were processed for the wave number ranging from 600 to 1,600 cm -1. The X-ray diffraction patterns were recorded by a Bruker D8 Advance X-ray diffractometer (XRD; Bruker AXS, Inc. , Madison, WI, USA). An electronic tensile testing machine (Jinan Hengrui machine Co. Ltd. , Jinan, China) was used for the mechanical characterization of aligned electrospun fibrous membranes, and the electrical properties of the fibers were tested using a Keithley 6485 high-resistance meter system (Keithley Instruments, Inc.
Acknowledgements The authors gratefully acknowledge the financial support provided by the  National Research Council of Argentina (CONICET) , grant  PIP 0648 , the  Science and Technology National Promotion Agency (ANPCyT) , grant  PICT-2010-1791 , the  Science, Technology and Productive Innovation Ministry (MINCyT) , grant ECOS-SUD and the  National University of Mar del Plata  (Project #  15/G312 ).
Methods The experiment involved testing 320 samples. Over the course of the long-lasting experiment, the electrical input and electromechanical output were recorded for each sample. To handle the number of samples, we designed original equipment to automatically perform the testing procedure upon many actuators concurrently. A comprehensive overview of the equipment as well as the methodology used for testing is presented elsewhere  31. This setup excludes human errors and guarantees that all samples are tested under exactly similar conditions. For that reason, the experiment was setup considering the trade-up between the obtained data and the automation options. The recorded mechanical outputs were the force gauge output and visual information on the bending. The actuator behaviour was recorded by a camera, which was equipped with a long-focal-length lens.
Acknowledgments The UT team was supported by the  Estonian Research Council  and  European Space Agency. The UNLV team acknowledges partial financial support from  NASA  (# NNX13AN15A ) and the  US Office of Naval Research  (# N000141310274  and # N000141210387 ). The  LPPI  team acknowledges  ANR  for its financial support (Grant No. ANR09BLAN0110 ) and the  European Network for Artificial Muscles (ESNAM). The  UOW  team acknowledges funding received from the  ARC Centre of Excellence for Electromaterials Science  (Grant No. CE0561616 ).
Acknowledgments We thank  T. Phou  and  G. Prévot  for help with instrumentation,  M. George  for the roughness measurements,  S. Aime  for help in microscopy experiments and  L. Berthier  and  J. -L. Barrat  for discussions. This work has been supported by  ANR  under Contract No. ANR-09-BLAN-0198  (COMET).