dataesr/scientific-paragraphs-categorization huggingface dataset:
Background and current status SPARC  [135, 136]  (https://github. com/SPARC-X/SPARC) is an open-source, real-space DFT code that accommodates Dirichlet and Bloch-periodic boundary conditions, enabling the treatment of finite, semi-infinite, and charged systems, as well as bulk 3D systems. SPARC employs the finite difference method, wherein quantities of interest are discretized on a uniform realspace grid and convergence is controlled by a single parameter. The finite-difference method's simplicity, locality, and freedom from communication-intensive transforms enables efficient implementation on large-scale parallel computers. Because the representation is maximally local in real space, modern O(N)-scaling as well as traditional O(N 3 )-scaling methods are readily implemented. Current features of SPARC include: • Applicable to isolated systems such as molecules as well as extended systems such as crystals, surfaces, and wires.
• Local, semilocal, and nonlocal (including hybrid) exchange-correlation functionals. • Standard ONCV pseudopotentials, including complete SPMS database  [137]. • Calculation of ground state energy, atomic forces, and stress tensor. • Structural relaxation and ab initio MD (NVE, NVT, and NPT). • Spin polarized and unpolarized calculations. • Spin-orbit coupling. • Dispersion interactions through DFT-D3, vdW-DF1, and vdW-DF2 methods. • Linear-scaling spectral quadrature (SQ) method  [138]. • Discrete discontinuous basis projection (DDBP) method  [139]. • Symmetry adaption for cyclic and helical symmetries  [140]. • Orbital-free DFT with Thomas-Fermi-Weizsäcker, Wang-Teter, and Wang-Govind-Carter kinetic energy functionals. • MATLAB version available for rapid prototyping: M-SPARC  [141]. SPARC is portable and straightforward to install, use, and modify, with external dependencies limited to industry standard BLAS, LAPACK/ScaLAPACK, and MPI.
In general, calculations based on MBPT methods are computationally more demanding than those using DFT. Conventional implementations of GW and BSE, for instance, exhibit a computational complexity that scales as O(N 4 ) and O(N 6 ), respectively, where N is the number of electrons in the system, posing difficulties in carrying out MBPT calculations for complex, heterogeneous materials. We have implemented MBPT in an open-source software package named WEST (Without Empty States, http://west-code. org)  [147]  that is interfaced with the QE (https://quantumespresso. org) and Qbox (http://qboxcode. org) plane-wave pseudopotential codes. The key functionalities of WEST are summarized in figure  28. WEST adopts distinctive algorithms to circumvent computational bottlenecks commonly encountered in large-scale MBPT calculations.
(iii) Code interoperability. Workflows used in electronic structure calculations are increasing in complexity, as theoretical methods advance and the computational power grows. It is therefore of great importance to develop flexible and extensible workflows where one or more codes cooperate through in-vivo or ex-vivo coupling [152], possibly operating on different architectures, including both classical (CPUs and GPUs)  [151]  and quantum (QPUs)  [153]. We have recently developed several coupling schemes to improve the efficiency of MBPTbased calculations and facilitate the development of coupled codes, as shown in figure  28. For example, we developed a coupling scheme between WEST and the FPMD code Qbox to improve the efficiency of the solution of the BSE by performing electronic structure calculations in finite electric field, while taking advantage of electronic orbital localization.
In addition, QDET calculations are performed by coupling a DFT engine (QE or Qbox), a MBPT solver (WEST), and diagonalization codes for the effective Hamiltonian. The latter are based on quantum chemistry methods and may be run on classical or quantum architectures. As the next generation of supercomputers will be more modular and heterogeneous, and the size and fidelity of QPUs continue to improve, code coupling represents an interesting avenue to combine the strengths of diverse types of computing paradigms  [153]. (iv) Introducing machine learning protocols in EST. Advances in machine learning and deep learning techniques have substantially improved the efficiency of several electronic structure methods. We recently applied data-driven approaches to the calculation of the dielectric screening, a key ingredient required to compute absorption spectra using the BSE  [154].
Acknowledgments We thank the numerous contributors to the ABINIT project over the years, many of which are the authors of [1,2]. We also thank  Matthieu Verstraete ,  Gian-Marco Rignanese , and  Guillaume Colin de Verdière  for careful reading of the manuscript, and associated suggestions. The development of the HPC aspects of ABINIT was made possible thanks to access to the resources of the french  Très Grand Centre de Calcul du CEA. We gratefully thank the computing center teams for their availability and expert advice. The work has received partial support from the  European Union 's  Horizon 2020 research and innovation program , Grant Agreement  N • 951786  through the  Center of Excellence NOMAD (NOMAD CoE).
Acknowledgments L E R acknowledges support from an  EPSRC   Early Career Research Fellowship  ( EP/P033253/1 ). L G and W D acknowledge the joint  CEA-RIKEN  collaborative action. This work used computational resources of the supercomputer Fugaku provided by  RIKEN  through the  HPCI System Research Project  (Project ID:  hp200179 ). L G acknowledges support from the  MaX EU center of Excellence , and from  French National computing resources  (Projects  spe0011  and  gen12049 ). We would like to thank  Damien Caliste ,  Giuseppe Fisicaro ,  Louis Beal ,  Martina Stella , and  Takahito Nakajima  for discussions related to this manuscript, and everyone who has contributed to the BigDFT code over the years.
Acknowledgments The authors would like to acknowledge the many contributions of  Professor Mike Gillan , who founded and led the  CONQUEST  project; we are also grateful to the other CONQUEST developers and users for all their input. The study relied on computational support from the  UK Materials and Molecular Modelling Hub , which is partially funded by  EPSRC  ( EP/P020194 ), for which access was obtained via the UKCP consortium and funded by  EPSRC  Grant Ref. No. EP/P022561/1. This work also used the  ARCHER UK National Supercomputing  Service funded by the  UKCP  consortium  EPSRC  Grant Ref. No. EP/P022561/1. This work is also partially supported by the projects  JSPS Grant-in-Aid for Transformative Research Areas (A ) ' Hyper-Ordered Structures Science'  (Grant Nos. 20H05883  and  20H05878 ) and  JSPS Grant-in-Aid for Scientific Research  (Grant Nos.
Acknowledgments DFT-FE has been the outcome of the efforts of many principal developers and contributors, and we refer to 'Authors' on the DFT-FE GitHub page [46] for the complete list of contributors. Various aspects of the code development efforts of DFT-FE are being supported by the  U. S. Department of Energy Basic Energy Sciences , the  Toyota Research Institute , and the  Department of Science and Technology, India. The algorithmic developments that form the core of the DFT-FE code have been supported by the  U. S. Air Force Office of Scientific Research  and the  U. S. Army Research Office. This work used the resources of the  Oak Ridge Leadership Computational Facility , which is a  DoE Office of Science User Facility  supported by the  Office of Science of the U. S. Department of Energy.
Acknowledgments This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  through the  NOMAD Center of Excellence , Grant Agreement No. 951786. We acknowledge partial support by the  German Research Foundation (DFG)  through the  CRC 1404  ( FONDA ), Project  414984028 , and the  NFDI  consortium  FAIRmat , Project  460197019. AG acknowledges funding provided by  European Regional Development Fund  via the  Central Finance and Contracting Agency of Republic of Latvia  under the grant agreement  1. 1. 1. 5/21/A/004 , project  Machine-LEarning Pseudopotentials  (MLEAP). We thank  Ignacio Gonzales Oliva  for providing figure 10.
Acknowledgments Foremost we thank the wide community of FHI-aims users and developers; the latter are listed at https://fhi-aims. org/who-we-are. We do not have space to list everyone individually, but we would like to particularly thank the  Max Planck Computing and Data Facility (Garching, Germany)  for their continued support. We thank  Bernard Delley  for several discussions that started well before the FHI-aims developments began and continued afterwards. Especially regarding the inception phase of FHI-aims, we thank  Martin Fuchs ,  Jörg Behler  and  Karsten Reuter  for key discussions and support. This work received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  (Grant Agreement No. 951786 , the  NOMAD CoE) , and the  ERC Advanced Grant   TEC1P  (No.
740233 ). Work on ELSI,  ELPA-GPU  and the  CECAM  ESL was partially supported by the  National Science Foundation  under Award Number  1450 280. Acknowledgments We thank the full  NWChemEx development  team and collaborators for all of their efforts in bringing NWChemEx to life. You have made this work possible. This research was supported by the  U. S. DoE 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. Acknowledgments We acknowledge support from a subaward from the  Center for Computational Study of   Excited-State Phenomena in Energy Materials (C2SEPEM)  at LBNL, which is funded by the  U. S. DOE, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division  under Contract No.
DE-AC02-05CH11231 , as part of the  Computational Materials Sciences Program. Computational resources are provided by the  National Energy Research Scientific Computing Center (NERSC)  as well as  Texas Advanced Computing Center (TACC). M D acknowledges support from the ' Characteristic   Science Applications for the Leadership   Class Computing Facility ' project, which is supported by  National Science Foundation  Award # 2139536. Acknowledgments This work is supported by the  Midwest Center for Computational Materials (MICCoM)  as part of the  Computational Materials Sciences Program  funded by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences, and Engineering Division through Argonne National Laboratory , under Contract No. DE-AC02-06CH11357.
Acknowledgments Work by the author on fragment-based quantum chemistry has been supported by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences , under Award  DE-SC0008550. The author serves on the board of directors of  Q-Chem Inc. Acknowledgments This work was partially funded by the  EU  through the  MaX Centre of Excellence for HPC applications  (Project No. 824143 ). Financial and administrative support from the  Quantum ESPRESSO Foundation  is also gratefully acknowledged. Acknowledgments This work was supported by Grant  DE-SC0019410  funded by the  U. S. Department of Energy, Office of Science. This work was performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344 , with support from the  LLNL ASC/PEM and Multiprogrammatic and Institutional Computing programs.
A conclusion will summarize the approach and propose some perspectives. Ii. Analysis Of The State Of The Art Of Supporting And Support Machining. Selective Laser melting (SLM) is one of the metal additive manufacturing main technology. It has been developed by  Meiners et al. in 1997 [7] , and also by  Das et al. in 2004 [8]. The SLM process starts with a 3D numerical model, usually a STL le, through a so-called slicer software  [9] , to de ne the printing strategy, i. e. layer by layer, the lasing parameters and the adding of supports structures. Supports have to be added for several reasons  [10]  : Isolated surfaces: metal powder has a liquid behavior, it is necessary to tie the lased surfaces that are not attached to a solid part, to avoid any movement during recoating. Overhanging: thin layers of fused powder are very prone to warping, thus supports are necessary on most of quasi-horizon-tal surfaces.
Removal, including machining time There are a lot of possible support structures in slicer software and scienti c literature is also abundant about supporting struc-tures  [11]. But in practice lattices and supports structures are mainly chosen according to the trial and error method. A large number of studies have explored the mechanical behavior of lattice structures in SLM,  [12] ,  [13] ,  [14] , which are often geometrically similar to support structures. But support structures are built with speci c lasing parameters, and Bobbio et al. [15]  have shown on Ti-6Al-4V that structural strength of the support structure regions are ranged from 14-32% of the continuous, monolithic material. They have also identi ed that their slicing software would discreetly reduce the heat input at the interface supportpart, probably to aid in the removal of supports.
A. Cutting forces numerical modeling In order to investigate the behavior of supports during milling, numerical models have been conducted on Abaqus® software. The considered supports are grid supports, which are one of the most used in the industry, and according to the literature analysis grid are well suited for machining. The supports are deformable solids, while the milling tool a rigid body. The literature provides the material parameters for powder bed fusion manufactured Ti6Al4V, see Table  1 , from  [25]. The material behavior is represented according to Johnson-Cook model, Equ. (1), from [26], de ning the relationship between strain, strain rate and temperature. The damage law needed for milling simulation is de ned equ. (  2 ), following the Johnson-Cook failure model  [27]  de nes the strain at fracture as described in (Equ.
2): A B n C m To the knowledge of the authors all the coe cients of this fracture model have not been identi ed yet for SLM additively produced titanium. Only Alexander et al. [28] have published a partial identi cation, i. e. coe cients D1, D2, D3 and at very low strain rate (10^-4 s^-1). Considering that machining additively produced titanium is practically very similar to forged titanium, we have used a forged titanium Johnson-Cook fracture model, already correlated with machining tests, from Zhang Y,  [29] , in 2015, see Table  2. Arbitrary Lagrangian Eulerian (ALE) formulation,  [30] , has been chosen for this model. Finally, a Coulomb friction law is used, µ = 0. 2, to model the contact zone between the chip and the tool, as a rst approximation of available data in the literature  [31][32].
B. Machining testing It exists almost in nite possibilities of possible supports due to the number of parameters which can be modi ed for each support typology. As an example, in the printing preparation software 3DXpert®, it exists approximately 120 different printing parameters just for the supports. According to the litera-ture and the common use of few supports, we will only test here very common supports, with default settings of the software. The most used support used is probably the so-called «grid» or «block» supports. It consists typically in squared or honey-comb and line pattern (Fig. 2 ). The main parameters of those supports are the following: Parameters Square Honeycomb Line In addition to the well-known grid supports it is proposed a new kind of support, called « porous support ».
Six traction samples have been printed, with 6 different hatching distances, thus porosity. The working section is 10 mm x 10 mm, the height 7. 5 mm. The porous sample is xed at the bottom on the plate and at the top on a cuboid of full dense material. Two typical samples are represented Fig. 3  and also the mounting in the tensile test machine. Alternatively to strain gauges or extensometers a bidimensional digital image correlation has been used to measure full eld displacement and strain. The digital image correlation software used is GOM®, with two Phoron Fastcams SA61. 1. A National Instrument 6211 data acquisition module has been used in order to synchronize the image acquisitions with the load. The results are represented in the following result section, Fig. 6  and Fig. 7. The support removal machining process was performed on Huron KX 10 three axis milling machine, under dry cutting condition, using climb milling, radial engagement 40 mm.
The cutting tools, Table  5 , and cutting conditions, Table  6 , have been chosen with the assistance of Horn® tool manufacturer research department. During the machining, the dynamic behavior has been recorded using a piezoelectric accelerometer, xed on the build plate in the feed direction. The recording and the analysis has been performed with the open source Audacity software, using Kistler 8452A accelerometer and Plug. n. DAQ-lite Roga USB sensor interface. After each cutting pass, pictures were taken of the machined surface, using Nikon D7200 and AF-S Nikkor 18-105, and pictures of the cutting inserts, using a Dino-Lite digital micro-scope.
B. Porous support mechanical behavior In the present study, Digital Image Correlation (DIC) is used on porous samples to determine its mechanical behavior, as illus-trated Fig. 6. The GOM Correlate® software has been used to obtain local and averaged strain, for each tensile force increment. Stress-strain curves have been obtained for various percentages of porous structure, Fig. 7. For sake of simplicity a rough estimation of the fusioned fraction of the porous samples can be de ned as the ratio of nominal full density hatching distance 50 µm over the extended hatching distance, see Table  8. The relative resistance of a porous sample can be de ned as the ratio of the maximum tensile stress of the sample over the fully dense reference and we can de ne this ratio as an « apparent density », see Table  7.
These studies were based on data sampled in 2008 in Germany covering six months at 70 wind turbine nacelles and 35 different sites. To our knowledge, no peer-reviewed study has examined simultaneously and on a spatiotemporally extensive dataset all drivers of bat exposure (i. e. landscape features, weather conditions, date, and wind turbine characteristics), nor assessed the possibility to use them in guild specific algorithms to inform wind turbine curtailment. To assess the potential of multicriteria curtailment algorithms, we compiled bat acoustic data recorded at wind turbine nacelles in France by wind energy developers in a context of post-construction regulatory studies, while homogeneously re-analysing acoustic data (i. e. using the same automated bat call identification software). Reprocessed bat acoustic data allowed us to build a standardised bat activity metric at nacelle height known to be a good predictor of fatality risk  (Peterson et al.
Acoustic data collection and processing We compiled existing raw acoustic data (i. e. sound files in raw or wav format) of 14,937 complete recording nights at 59 wind turbine nacelles (including 20 models) located on 55 wind farms in France (Fig. 1 ; Table  S1 ). These data were provided by nine wind farm developers and produced by 12 consulting firms and non-governmental organizations as part of regulatory post-implementation impact monitoring studies. Each of the 59 wind turbines was monitored on average for 251 nights (min: 103; max: 514). The monitored nights covered all months of the year and spanned four years between 2017 and 2020; 10 % of the sites were monitored for more than one year. The year 2017 represents 2 % of nights, 2018 18 % of nights, 2019 79 % of nights and 2020 0. 4 % of nights (Fig.
S1 ). Depending on the analyses conducted, the complete set or subset of these data were used (see Statistical analysis section). Three types of recorders were used: Batcorder at 18 wind turbines (versions 1, 2 and 3; ecoObs), Batmode S+ at 34 wind turbines (bat bioacoustics technology GmbH), and Song Meters at eight wind turbines (SM3BAT and SM4BAT; Wildlife Acoustics). All recorders were positioned at the bottom of the nacelle. Each was associated with one to three triggering thresholds, i. e. a built-in recording control algorithm which started the recording when a sound event exceeded a given sound level (see Supporting information S1 for more details). Acoustic monitoring was always performed throughout the night, from sunset to sunrise. We used the number of bat passes (hereafter referred to as "activity") or the presence/absence (hereafter referred to as "occurrence") recorded during a night as a measure of bat visits with exposure (see Section 2.
3 for more details). We defined a bat pass as one or more echolocation calls within a five-second interval  (Kerbiriou et al. , 2019). All 731,717 bat passes were automatically classified to the closest taxonomic level using the Tadarida software  (Bas et al. , 2017). Since most bat species had very low occurrence (Table  S1 ), we pooled together species into three guilds based on their similar echolocation call structures and therefore similar foraging strategies: long-range echolocators (LRE), mid-range echolocators (MRE) and short-range echolocators (SRE)  (Frey-Ehrenbold et al. , 2013) , see Table  S2  for species composition. Long-range echolocators are especially sensitive to fatality risks with wind turbines due to the great part of the time they spend at height (i. e. 20 to 45 m above ground level), followed by the mid-range echolocators (Table  S2 ;  Roemer et al.
, 2017). Although grouping species into these three guilds prevented misidentification problems between cryptic species, noise in the nacelle due to wind turbine functioning generated many false positives, especially at very high blade speeds. We followed the approach of  Barré et al. (2019)  and applied a maximum false positive tolerance of 50 % to discard these interferences (see  Barré et al. (2019)  for more details), which reduced the dataset to 98,627 bat passes. This reduction led to discard 6. 55 to 9. 93 % fewer false positives for Batmode data compared to other recorders.
, 2015 , see Statistical Analysis section for more details). We also calculated the Euclidean distance to the nearest impervious surfaces, forests and water bodies. Moreover, we computed landscape metrics depicting landscape configurational and compositional heterogeneity  (Monck-Whipp et al. , 2017) , including edge density (i. e. the density of ecotones in m/ha), conditional entropy (i. e. an increasing index with increasing landscape complexity), patch richness density (i. e. the number of patch types standardised by the surface), and Shannon diversity index of habitat patches. These landscape metrics were computed using the R package landscapemetrics  (Hesselbarth et al. , 2019) , for the ten radius sizes presented above. All landscape variables were extracted from the high-resolution CES OSO land cover map 2018 available at https:// www.
Weather variables We collected the average wind speed (m/s) and temperature (°C) recorded by wind turbine nacelle weather stations in 10-min intervals and averaged them on a nightly scale at each wind turbine (i. e. on the same scale as acoustic data). Since the amount of rainfall was not recorded by the nacelle weather stations, we collected the daily cumulated rain (mm) (i. e. over the 24-h period from midnight of the day when the recording night started) using the weather database from E-OBS (https://surfobs. climate. copernicus. eu/dataaccess/access_eobs. php#datafiles).