dataesr/scientific-paragraphs-categorization huggingface dataset:
Author Contributions: A. L. V. and C. C. wrote the paper; F. D. , C. C. , F. C. , L. P. -R. and F. E. gathered and compiled the data, A. L. V. developed the model. All authors contributed equally to the scientific discussion. Funding: The authors thank the European communities (FEDER), the "Région Nouvelle Aquitaine" and ANR (INCH project) for financial support. Acknowledgments: Authors thank S. Baranton and C. Coutanceau (IC2MP Poitiers, France) for scientific discussions.
Conformers with lower energy give better results  [27]. COSMO-RS calculations were carried out at the lower energy state of the conformers of each IL anion and IL cation. The COSMO-RS cation and anion files were generated using BP functional with a tripleξ valence polarized with diffuse functions basis set (TZVPD) and the resolution of identity standard (RI) approximation using the TURBOMOLE 7 program package  [28]. The subsequent calculations like predicting the hydrogen bonding energy, Vander Waals energy, hydrogen bond accepting moment and hydrogen bond donating moments were calculated using the COSMOtherm employing the parameter file BP_TZVPD_FINE_C30_1701. ctd  [29].
Bulk X-ray scattering Bulk X-ray scattering was measured in the transmission geometry on a self constructed instrument using Cu K a radiation (l Ka ¼ 0. 154 nm, Rigaku MicroMax 007 microfocus rotating anode X-ray generator, Osmic Max-Flux confocal multilayer optics). 46 A 1 mm thick [bmpy] + [FAP] À sample was contained between two diamond windows to suppress background scattering. Diffraction patterns were obtained by azimuthal integration of the data recorded on a 2D image plate detector (Mar345, MarResearch) at a sample-detector distance of 342 mm and applying standard correction factors. 47.
Subsequent Gaussians, centered at a distance z n from the electrode, are equidistantly spaced by 1/2d I (eqn (7c)). Their width s n is composed of the intrinsic sizes of anions and cations s a,c plus incremental broadening s b , and potential dependent broadening s p (U) (eqn (7d)). Mass conservation is taken into account by eqn (7b). XRR curves. Reectivity curves were numerically calculated using the Parratt formalism aer dividing the prole into 0. 02 nm slabs of constant density. 53, 54 ispersion effects were included using X-ray form factors from the NIST database. 55 Density proles were extracted by simultaneous tting of all three XRR data sets using a simulated annealing algorithm. 56 For large separations z from the solid/liquid interface, the interfacial density prole in the IL r e (z) can be approximated  [57] [58] [59]  by the generic form r e ðzÞ ¼ ðr c þ r a Þ 1 þ A exp À z x I cos 2p z d I þ 4 ! : (8) Here, the oscillatory prole adjacent to the solid/liquid interface is solely characterized by its amplitude A, phase 4, periodicity d I , and decay length x I.
Acknowledgements The expert advice and assistance of  Veijo Honkimäki  at beamline ID15,  ESRF , is gratefully acknowledged. This work was supported by the  German-Israeli Foundation for Scientic Research and Development  (No. 2306-2310. 14/2011 ). PR and JM are recipients of a fellowship funded through the  Excellence Initiative  ( DFG/ GSC 266 ). KSK, TBvD, and MMN acknowledge support from the  Danish National Research Foundation , the  Danish Strategic Research Council  through.
The parallel-plate device consists of two plates of diameter 29 mm covered with the same sandpaper as the rough rotor and separated by a gap of 1 mm. The data in Fig. 18  reveal the same phenomenology as that found with the smooth Taylor-Couette geometry of gap 2 mm. Both flow curves show the distinctive vertical portion characteristic of DST. Within the DST regime, the shear rate measured under an imposed stress becomes unsteady. The critical shear rate and shear stress at DST onset inferred from stress sweeps are slightly less than those in Fig. 2a , which could indicate some degree of sensitivity to the geometry and/or sample preparation. The fact that, in the parallel plates, long experiments under constant stress record shear rates that are increasingly larger than in the preceding, rather fast stress ramp is most probably due to humidity absorption by the suspension.
ACKNOWLEDGEMENTS BSM thanks Solvay for funding. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958  through the  KITP program  on the  Physics of Dense Suspensions. The authors are grateful to  Laurent Chevillard  for fruitful advice on signal analysis. We deeply thank  Romain Mari  and  Raul Chacko  for enlightening discussions on models and simulations of shear-thickening systems, as well as  Mike Cates  and  Suzanne Fielding  for sharing their latest results with us. We also wish to acknowledge insightful discussions with  D. Blair ,  M. Bourgoin ,  A. Colin ,  J. Comtet ,  M. Denn ,  T. Divoux ,  E. Han ,  H. Jaeger ,  G. McKinley ,  P. Olmsted ,  G. Ovarlez ,  A. Sood  and  M. Wyart  at various stages of this work.
ACKNOWLEDGMENTS This work was performed, in partnership with the SAS PIVERT, within the frame of the  French Institute for the Energy Transition (Institut pour la Transition Energétique -ITE) P. I. V. E. R. T. (www. institut-pivert. com)selected as an  Investment for the Future ("Investissements d'Avenir "). This work was supported, as part of the Investments for the Future, by the  French Government  under the reference  ANR-001-01. The authors thank  Equipex  Xyloforest  ANR-10-EQPX-16. The financial support from the  CPER   CAMPUSB  project funded by the  French state  and the  Région Nouvelle Aquitaine  is gratefully acknowledged.
Acknowledgements We thank the  University of Malta  for financial support. Prof. Robert M. Borg  is acknowledged for NMR assistance and  Maria Szaciłowska  for assistance processing the DFT data. We extend our appreciation to the reviewers for their helpful comments. DFT calculations were performed at the  Academic Computer Centre CYFRONET AGH  within the computational grant " GRAPHENE " with funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  664786. The  Embassy of France  to Malta, the  CNRS  and the  MCST (Malta Council for Science and Technology ) are thanked for financial aid.
A significant effort is underway to improve the code for exascale architectures with a single common code base. QMC methods are particularly attractive for these future systems due to the relatively low data movement required. This combination of capabilities and activities helps to distinguish QMCPACK from other QMC codes such as QWALK  [38] , CASINO  [39] , CHAMP  [40] , and TurboRVB  [41]. In this article, we give an overview of the features and capabilities of the QMCPACK package. To indicate the future development pathways, we outline a number of challenges for QMC methods, including development of consistent many-body pseudopotentials, the addition of spin-orbit interactions to QMC Hamiltonians, and the challenge of exascale computing.
Open source and open development QMCPACK is open source and distributed under the Open Source Initiative  [42]  approved University of Illinois/NCSA Open Source license. The main project website http: //qmcpack. org links to versioned releases and the development source code. This also includes a substantial manual detailing installation instructions, examples for workstation through to supercomputer installations, and detailed methodology and input parameter descriptions. The source code includes a substantial test framework (> 300 tests) including unit and integration tests that are used to help validate the implementation and test new installations. QMCPACK is also openly developed. The latest source code and updates are coordinated via GitHub, https://github. com/QMCPACK/qmcpack. This site provides version controlled source code, a wiki describing development practices, issue (ticket) tracking, and a contribution review framework (pull request reviews).
The project follows the "git flow" branching and development model. Contributions from new developers are encouraged and follow exactly the same mechanism as for established developers. For example, the "finite difference linear response" (FDLR) method  [43]  was recently contributed and underwent several updates to maximize compatibility with the existing source code. The full discussion history of contributions is also available. The open development process, with full change history available, allows contributions to be clearly identified and credit accurately assigned. The source code change history can be tracked to the earliest days of QMCPACK. All proposed changes to QMCPACK automatically undergo continuous integration testing which allows the contributions to be run on different architectures and with different software versions than might have been used for development, e.
Trial Moves QMCPACK supports VMC trial moves with and without drift. This means that the move r → r is drawn from the transition probability distribution given by: T (r → r , τ ) = 1 (4πλτ ) 3N/2 exp - (r -r -2λτ F(r)) 2 4λτ ( 11 ) For drift based moves, F(r) is taken to be the same wavefunction force as appears in equation 4. For moves without drift, F(r) = 0. In the absence of pathologies in the trial wavefunction, the use of the drift term is almost always more efficient. In addition to drift or no-drift based moves, the code supports particle-by-particle or all-electron moves. All-electron moves are conceptually the simplest. One proposes the move r → r = r+∆ by drawing the 3N e dimensional vector ∆ from the distribution in equation 11. This move is then accepted or rejected with probability: A(r → r ) = min 1.
Orbitals The single-particle orbitals in the Slater determinants are generally determined by another electronic structure code and imported into QMCPACK for calculations. QMCPACK has an easily extensible mechanism for adding new ways of representing single-particle orbitals. This can be particularly useful when addressing model systems or performing specialized tests. For example, QMCPACK supports specialty homogeneous electron gas and plane-wave based wavefunctions, and for work on spherical quantum dots, radial numerical functions  [53]. However, by far the most common sources of orbitals are plane-wave based from Quantum Espresso (QE)  [54, 55] , and Gaussian based from the GAMESS code  [56]. Converters from these codes are provided and can straightforwardly be extended to other methodologically related codes.
Gaussian basis sets For molecular systems, one typically uses a Gaussian basis set to represent the single-particle orbitals. QMCPACK supports standard quantum chemical basis sets including contractions and for arbitrary angular momenta. Atomic or natural orbitals can therefore be directly imported from standard quantum chemistry codes. Interfaces currently exist to GAMESS  [56] , quantum package[59], and for packages supporting the MOLDEN format. Interfacing requires converting the output of the intended package to QMCPACK 's XML or HDF5 format. For all-electron calculations, a cusp correction scheme is implemented to enforce the electron-nuclear cusp.
Specialized basis sets Besides the B-spline and Gaussian basis sets described above, QMCPACK implements several additional specialized basis sets for specific problems. This includes Slater Trial Orbitals (STOs), the homogeneous electron gas, and radial numerical functions for atomic calculations. Due to the flexible internal architecture, orbitals can be expressed in any combination of these functions. For example, in  [60] , it was proposed to save memory by storing orbitals on different sets of B-spline tables based on their kinetic energy. This scheme did not require any source code modifications.
Similar to the algorithm of Zhang and Krakauer, we use importance sampling and force-bias to improve the sampling efficiency of the algorithm. For a complete description of the implemented algorithm, see the lecture notes on AFQMC by S. Zhang in the open-access book  [28]  and  [29]. The AFQMC implementation in QMCPACK attempts to minimize the memory requirements of the calculation, while increasing the performance of the associated computations. This is done by a combination of: (1) distributed sparse representations of large data structures (e. g. 2-electron integrals), (  2 ) efficient use of shared-memory on multi-core architectures, (3) combination of efficient BLAS and sparse-BLAS routines for all major computations, and (4) an efficient distributed algorithm for walker propagation. Notice that the code is able to distribute the work associated with the propagation of a walker over many nodes, enabling access to systems with thousands of basis functions with a full ab initio representation.
During each iteration, the current wavefunction is used in conjunction with the Hamiltonian to find important contributions that will be added to the wavefunction in the next iteration. In most Selected CI approaches, the importance of a contribution is determined from a many body perturbation theory estimate. A full description of CIPSI, its algorithms, and results on various systems can be found in Refs. [98, 119, 120]. A description of new improvements to selected CI techniques that have been demonstrated with ASCI and HBCI can be found in Refs. [99, 100]. The CIPSI method  [98, 121, 119, 120]  is implemented in the Quantum Package (QP) code[59] developed by the Caffarel group. QMCPACK does not implement CIPSI, but is able to use output from the QP code via tight integration. In the following we use the C 2 O 2 H 3 N molecule (figure  5 ) to illustrate the use of CIPSI to obtain an improved trial wavefunction.
The C 2 O 2 H 3 N molecule is part of the cycloreversion of heterocyclic rings database  [122] , for which the geometry was optimized with DFT using the B3LYP function in a 6-31G basis set. Orbitals are represented within the aug-ccpVTZ basis set. The energetics of this molecule are known to have a strong dependence on the choice of functional in DFT simulations  [122]. Diagnostics based on coupled cluster theory (CC) with single, double, and peturbative triple excitations (CCSD(T))  [123]  suggest a multireference character  [124] , a known problem for these techniques  [125]. The multireference capability of DMC-CIPSI makes it an ideal tool for treating difficult systems with large static correlations. The FCI space for C 2 O 2 H 3 N in aug-ccpVTZ is approximately 10 88 determinants. Fortunately, using all of these determinants is not necessary to converge a QMC calculation to chemical accuracy.
Averaging quantities in the MC data QMCPACK includes the qmca Python-based tool to average quantities in the output files and aid in performing statistical analysis. Given the name of an output file, qmca will compute the average of each quantity in the file. Results of separate simulations can also be aggregated, such as for different twists (twist averaging), multiple steps (autocorrelation analysis) or multiple Jastrow parameters (Jastrow optimization). In addition to all the quantities computed by QMCPACK , qmca computes the data variance and efficiency. qmca also allows visualizing the evolution of MC quantities over the course of the simulation by a trace offering a quick picture of whether the random walk had expected behavior as in example figure  7.
Wavefunction converters An important step before running a QMC calculation is to obtain the trial wavefunction from another electronic structure or quantum chemical code and convert it into a format readable by QMCPACK. In addition to the large set of converters available through Nexus, QMCPACK comes with 2 converters upon compilation. Connections to other codes will be developed on request. convert4qmc: When compiling QMCPACK , an extra binary called convert4qmc is also created. convert4qmc manages gaussian trial wavefunctions from codes such as GAMESS  [127] , VSVB  [128]  or Quantum Package  [59]. convert4qmc handles the conversion of single determinant, multideterminants (CASSCF, CI, CIPSI), numerical and Gaussian basis sets. The output file generated can be either in an XML or HDF5 format.
convert4qmc allows the user to add multiple option to the wavefunction such as a Jastrow function (2-body, 3-body or Pade), cusp conditions, or limit the number of determinants to include. pw2qmcpack : When using a plane wave trial wavefunction from the PWSCF code in the QE suite  [54, 55] , pw2qmcpack. x is used. Source code patches are included with QMCPACK to produce the pw2qmcpack. x binary for specific QE versions, necessary to collect and write the wavefunction in the correct format for QMCPACK.
As a further complication, the appropriate computational environment -or host computer -can vary with the stage in the chain from small clusters for DFT work, mid-size machines for wavefunction optimization, and sometimes very large supercomputing resources for DMC or AFQMC. Simplifying the management of these processes is of key importance to minimize the full time to solution for QMC. Scientific workflow automation tools have been used with much success in the electronic structure community to reduce both the burden on the researcher and to reduce the propagation of human error with improved systematization. Packaged with QMCPACK is an automation tool, called Nexus  [129] , which has been tailored to the computational workflows of QMC. The system handles several steps in the simulation process typically requiring human involvement such as atomic structure manipulation, input file and job submission script generation, batch job monitoring and error detection, selection of optimized wavefunctions, and post-processing of statistical data.
Nexus also handles the flow of information between simulations in a workflow chain, such as passing on the relaxed atomic structure, orbital file information, and optimal Jastrow parameters to subsequent simulations that require them. The system is suitable for both exploratory and production QMC calculations spanning multiple machines, including those approaching a high-throughput style. Nexus is written in Python following an object-oriented approach to allow extensibility to multiple simulation codes and host execution environments. Nexus currently has interfaces to QE  [54, 55] , GAMESS  [127, 56] , VASP  [130, 131, 103, 104] , QMCPACK , and a number of associated post-processing and file conversion tools. Nexus does not require access to the internet or to an installed database to run, instead operating only via the filesystem.
Nexus is therefore suitable for the widest range of computer environments. Supported machine environments include standard Linux workstations as well as high performance computers. Explicit support exists for systems at the National Energy Research Scientific Computing Center (NERSC), the Oak Ridge Leadership Computing Facility (OLCF), the Argonne Leadership Computing Facility (ALCF), Sandia National Laboratories high-performance computing resources, the National Center for Supercomputing Applications (NCSA), the Texas Advanced Computing Center (TACC), the Center for Computational Innovations (CCI) at Rensselaer Polytechnic Institute, and the Leibniz Supercomputing Centre (LRZ). Variations in the job submission and monitoring environments at each institution necessitate specific extensions to ensure operability across this wide range of resources.
Users interact with Nexus by writing short Python scripts that generally resemble input files. Use of such "input files" allow the user to spend more time on project design rather than execution and naturally comprise both a record of calculations performed and a means to fully reproduce them. Nexus has been used extensively in summer schools on QMCPACK and recent research papers. An additional benefit of the workflows is the greatly improved ability to reproduce the calculations at a later date, and to speed up related research projects through reuse.
This study took advantage of QMCPACK 's ability to handle mixed boundary conditions (section 9). For BP monolayers and bilayers, we used periodic boundary conditions parallel to the layers, but open boundary conditions perpendicular to them. Also, we found the interaction energies were very sensitive to finite size effects, and the ability to perform calculation on many different supercells of the material was crucial to determine their exact form. The B-spline representation was used for the orbitals in the trial wavefunctions (section 6. 2. 1). Finally, the ability of the code to evaluate the charge density from the calculation was crucial.
[129]  were used jointly to validate a collection of newly developed pseudopotentials for the early-row transition metals (Sc-Zn)  [190]. DMC calculations were performed for five atomic charge states (ranging from neutral to 4+) and at nine transition metal-oxygen dimer bond lengths for each species. Orbitals were generated with QE using the experimental spin multiplicity within LDA  [5, 191]  or HSE  [192, 193]  for atoms and LDA for dimers. Two-body Jastrow functions were optimized at each atomic charge state and at the equilibrium geometry for dimers. Subsequent DMC calculations were performed with both T-moves and the locality approximation to assess the affect of pseudopotential localization errors. This large number of calculations is best handled using workflow software such as Nexus.
g. conventional processors, GPUs, and potentially even FPGAs. QMCPACK is being actively developed to adopt to the changing computational landscape. In particular, to (i) run efficiently on systems with fewer "fat" nodes with deeper heterogeneous compute capabilities as well as systems with a larger number of more homogeneous "thin" nodes by supporting multiple granularities of computation, (ii) accommodating multiple types of fine-grained multi-threading and vectorization to fully utilize the processors and compute accelerators, and (iii) make effective use of the increasingly complex memory hierarchy. To facilitate this task, simplified "miniapps" have been developed for real space QMC and AFQMC. These are distributed via https://github. com/QMCPACK. The miniapps encapsulate the main operations of each method while avoiding the full complexity of the main application and enabling use as testbeds for different programming models and middleware layers that help treat the above complexities.
Acknowledgments Major support for QMCPACK is currently provided by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences ,  Materials Sciences  and Engineering Division, as part of the  Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials. Exascale and performance portability efforts (GL, MD) were supported by the  Exascale Computing Project  ( 17-SC-20-SC ), a collaborative effort of the  U. S. Department of Energy Office of Science  and the  National Nuclear Security Administration. Previous support has included: the  Predictive Theory and Modeling for Materials and Chemical Science program  by the  U. S. Department of Energy Office of Science, Basic Energy Sciences ; " QMC Endstation " supported by  Accelerating Delivery of Petascale Computing Environment  at the  DOE Leadership Computing Facility at ORNL ;  Argonne Leadership Computing Facility , which is a  U.
The opening of this valve is controlled by a 0-5V signal. A Hall effect position sensor powered with 5V allows for the control of its position. For the transient measurements, a Producer-Consumer type loop is developed with the Labview software in order to be able to simultaneously control the valve and acquire signals. An example of control signal (red line) and of the position signal (blue line) is presented in Fig. 3. The valve is fully opened for 4. 5V and fully closed for 0V. The control is generated by a PID module in Labview. In the experimental measurements presented in this paper, the voltage values of the control are given in the form of a table, see Fig. 3. The position follows the control for 0. 2s delays. Below this value, a time lag is created.