dataesr/scientific-paragraphs-categorization huggingface dataset:
First, from TLS datasets: the quality of the waveforms obtained by summing up simulations of ALS data allows to be optimistic about the capacity to accurately simulate large footprint waveforms with DART in forest environments. Second, simultaneously simulating ALS data and large footprint waveforms could be used to validate and optimize simulators based on the conversion of discrete-return ALS data to a large-footprint full-waveform signal, such as the one developed for the calibration of algorithms and assessment of the GEDI mission accuracy  [62]. For example, it would be of high interest to better model the contribution of multiple scattering when simulating large footprint waveforms from small footprint signals, for which the contribution of multiple scattering is negligible. In addition, it could help to get a better handle on a possible change in wavelength between both airborne and spaceborne systems.
Third, generating forest scenes from ALS data using voxelization could also be considered to simulate large footprint waveforms with DART. Independently of the way they are produced, the accurate simulation of such waveforms can already serve numerous applications such as the refinement of specifications for new spaceborne sensors, as well as training and testing algorithms and their adaptation between sensors with different specifications to keep continuity in product quality. These applications could be extended to smaller scale and resolution with the enhancement of the 3-D scene accuracy and the improvement of the ALS sensor model. Combined with the speed-up of the models  [9] ,  [10] ,  [32] , a simulator such as DART could become a key element to transfer information from one scale to another (local to global, terrain to spatial), and to enhance the simulation and thus the analysis of other sources (multi/hyperspectral, radar).
VI. CONCLUSION In this article, we investigated the capacities of the DART model to simulate ALS full-waveform acquisitions. The simultaneous acquisition of ALS and imaging spectroscopy over a calibration plot with ground targets allowed to evaluate the calibration factor linking the energy at the entrance of sensor aperture to the intensity values delivered by the sensor. The calibration factor was determined with a limited variability (CV 12% to 7. 5% when taking into account effects stemming from the scan angle) and then used with the DART model to simulate full-waveform ALS acquisitions. Compared with the real ALS data, the simulations confirmed the accuracy of the DART model over a simple scene as well as a highly complex 3-D scene derived from TLS data acquired in a dense tropical forest.
In order to limit the size of the dataset, the reconstruction was made only in the interval ±5σ around each echo. The GB-WF waveform reconstruction method is fast to compute and benefits from RIEGL signal filtering (background noise, ringing effect, misleading signal due to sensor sensitivity, …). However, as small echoes can be difficult to separate from the noise or the ringing effect, they are usually censored by the sensor detection limit either during the acquisition (i. e. , not recorded) or during the data postprocessing stage, as data providers usually prefer to avoid having noise in the final dataset at the cost of a small loss of information. In the current study, the difference between the SDF raw waveforms and GB-WF was used to evaluated the magnitude of the background noise, which showed a median value of 2 DN.
This work was carried out in the frameworks of the  LEAF-EXPEVAL  and the  HyperTropik/HyperBIO  projects, supported by  TOSCA Continental Surface Program  of the  Centre National d'Études Spatiales (CNES). This work was also supported by the  Agence Nationale de la Recherche (BioCop project)  under Grant  ANR-17-CE32-0001.
TurboGenius is an open-source Python package designed to fully control ab initio quantum Monte Carlo (QMC) jobs using a Python script, which allows one to perform high-throughput calculations combined with TurboRVB [K. Nakano et al. J. Phys. Chem. 152, 204121 (2020)]. This paper provides an overview of the TurboGenius package and showcases several results obtained in a high-throughput mode. For the purpose of performing high-throughput calculations with TurboGenius, we implemented another open-source Python package, TurboWorkflows, that enables one to construct simple workflows using TurboGenius. We demonstrate its effectiveness by performing (1) validations of density functional theory (DFT) and QMC drivers as implemented in the TurboRVB package and (2) benchmarks of Diffusion Monte Carlo (DMC) calculations for several data sets.
For (1), we checked inter-package consistencies between TurboRVB and other established quantum chemistry packages. By doing so, we confirmed that DFT energies obtained by PySCF are consistent with those obtained by TurboRVB within the local density approximation (LDA), and that Hartree-Fock (HF) energies obtained by PySCF and Quantum Package are consistent with variational Monte Carlo energies obtained by TurboRVB with the HF wavefunctions. These validation tests constitute a further reliability check of the TurboRVB package. For (2), we benchmarked atomization energies of the Gaussian-2 set, binding energies of the S22, A24, and SCAI sets, and equilibrium lattice parameters of 12 cubic crystals using DMC calculations. We found that, for all compounds analyzed here, the DMC calculations with the LDA nodal surface give satisfactory results, i.
So far, many workflow management packages have been developed to achieve a more productive research activity and high-throughput calculations. Some representatives are Ai-iDA  18  , AFLOW  19  , Fireworks  20  , and atomate  21  , which have been widely used for generating and/or managing material science database such as NOMAD  22  and Materials Projects  23. One could immediately exploit these established workflow systems also in QMC calculations, but the combination of a QMC code with these workflow packages is not straightforward due to the complexity of the QMC calculations. Thus, to manage them, we need to implement interfaces, if possible in Python, because most of the established workflow packages are also implemented based on Python, due to its appealing features. In fact, several packages for high-throughput QMC calculations, such as Nexus  24  and QMC-SW  25  , are Python implementations.
Wheeler    26. PyQMC is an all-Python package; thus, it enables one to develop algorithms and complex workflows more flexibly and user-friendly. TurboGenius is an open-source Python package meant to manage TurboRVB calculations using a python script. In this paper, we explain the basic concepts, designs, functionalities, implemented classes, user interfaces, command-line tools of the package. TurboGenius provides Python classes and command-line tools that fully control TurboRVB jobs, which allow one to realize high-throughput QMC calculations. TurboGenius includes PyTurbo as a sub-package for providing users with more fundamental but more flexible building blocks to control TurboRVB jobs. For demonstrating high-throughput QMC calculations, we also implemented another open-source python package, TurboWorkflows.
The demonstrations contain: (1) validations of DFT and QMC implementations of the TurboRVB package and (2) benchmarks of Diffusion Monte Carlo (DMC) calculations for several data sets. As per (1), we confirmed that DFT energies obtained by PySCF  27, 28  are consistent with those obtained by Tur-boRVB within the local density approximation (LDA), and Hartree-Fock (HF) energies obtained by PySCF and Quantum Package  29  are consistent with variational Monte Carlo (VMC) energies obtained by TurboRVB computed with the HF WFs. The validation tests constitute a further reliability check of the TurboRVB package. As far as point (  2 ) is concerned, we benchmarked atomization energies of the Gaussian-2 set  30  , binding energies of the S22  31  , A24  32  , and SCAI  33  sets, and equilibrium lattice parameters of 12 cubic crystals using the lattice regularized diffusion Monte Carlo calculations (LRDMC)  34, 35.
We found that, for all compounds analyzed in this study, the LRDMC calculations with the LDA nodal surface give satisfactory results, i. e. , consistent either with computational or with experimental reference values. This paper is organized as follows: in Sec. II, we provide an overview of the TurboGenius program structure; in Sec. III, we provide an overview of the PyTurbo program structure; in Sec. IV, we describe the command-line tool and user interface implemented in TurboGenius; in Sec. V, we introduce TurboWorkflows, a python package for realizing highthroughput calculations using TurboGenius; and in Sec. VI, we showcase several validation and benchmark results obtained using TurboGenius and TurboWorkflows.
II. TURBOGENIUS: PROGRAM OVERVIEW TurboGenius is implemented in Python 3 (the minimal requirement is Python 3. 7). Python was chosen because it allows seamless integration with other major workflow frameworks. The main classes of TurboGenius are mainly composed of two types of classes. One is Wavefunction that stores and manipulates a WF information including nuclear positions and pseudopotentials. The others are classes inheriting an abstract class (genius class). The classes enable one to control TurboRVB tasks, such as generating input files, launching jobs, and analyzing outcomes. The major classes of TurboGenius are listed in Table  I. Hereafter, we will describe the functionalities of the two main classes. A typical workflow of a QMC calculation using Tur-boRVB; (1) Preparation of a JAGP(s/u) ansatz file from a chosen basis set, pseudo potentials, and a structure using makefort10.
The generated WF and PP files are fort. 10 and pseudo. dat, respectively. Notice that Wavefunction currently has pre-defined keywords for correlation-consistent basis sets implemented in Basis Set Exchange 41 for all-electron calculations, and correlation-consistent effective core potentials (ccECPs)  [37] [38] [39] [40]  and Burkatzki-Filippi-Dolg (BFD)  42, 43  basis sets for pseudopotential calculations. One can also use different basis set and pseudopotentials by providing them as a text list. The generated WF is a Slater Determinant WF with randomized MO coefficients. Indeed, a user should initialize it using the built-in DFT (prep) code before doing QMC calculations. The Wavefunction class also allows one to generate a WF file from a TREXIO file  44. The TREXIO library has a standard format for storing WFs, together with a C-compatible API such that it can be easily used in any programming language, which is being developed in TREX (Targeting Real Chemical Accuracy at the Exascale) project  45.
The listing 2 shows a script to convert a TREXIO file. Thus, instead of using the built-in DFT program, prep, one can use standard quantum chemistry packages such as GAMESS  46  and PySCF  27, 28  , then convert it to TurboRVB WF format. The class supports both all-electron and pseudopotential WF with/without periodic boundary conditions (i. e. , for both molecules and crystals), and both restricted (ROHF) and unrestricted (UHF) open-shell WFs. Notice that a restricted WF is converted to the symmetric antisymmetrized geminal power (AGPs) (i. e. , singlet correlation in the pairing function)  36  , while an unrestricted WF is converted to the broken symmetry antisymmetrized geminal power (AGPu), (i. e. , singlet + triplet correlations in the pairing function)  36.
B. GeniusIO classes GeniusIO is a parent class of the wrappers to manage complex QMC procedures. Several classes inheriting GeniusIO are implemented in TurboGenius, as shown in Table  I. Here, to make the explanation simple, we focus on one of the classes, VMC genius. VMC genius is a class to control VMC job of TurboRVB. The listing 3 shows a Python script to compute VMC energy of the hydrogen dimer. # generate the input file and run vmc_genius. generate_input(input_name="datasvmc. input") vmc_genius. run(input_name="datasvmc. input", output_name="out_vmc") # reblock the MCMC samples vmc_genius. compute_energy_and_forces(bin_block=10, warmupblocks=5) # VMC energy energy, error = vmc_genius. energy, vmc_genius. energy_error print(f"VMC-JDFT energy = {energy:. 5f} +-{error:3f} Ha").
III. PYTURBO: PROGRAM OVERVIEW PyTurbo is a sub-package of the TurboGenius package, which contains fundamental but flexible functionalities. The reason for implementing several classes in an independent sub-package is that there is a trade-off between flexibility and complexity (i. e. , the availability of more sophisticated functionality). Indeed, we suppose advanced users will develop and exploit more elaborated procedures than the ones we expect at present. TurboGenius in its present status may not be flexible enough to support all of them. Therefore, we leave PyTurbo as an independent sub-package and keep its implementation as simple as possible, so that advanced users can be provided with the fundamental building blocks to fully control TurboRVB jobs in a Python environment. The major classes of PyTurbo are shown in shown in Table.
II. fort. 10 is the most fundamental file containing all the WF information except for that of pseudo potentials. The information of pseudo potential is stored in a separate file, pseudo. dat. IO fort10 and Pseudopotentials are classes for manipulating a WF file and PP files, respectively. PyTurbo contains several classes inheriting the abstract class fortranIO. They are essentially Fortran90 wrappers, i. e, in a one-to-one correspondence between a PyTurbo class and a TurboRVB Fortran binary (e. g. , Makefort10 class in PyTurbo corresponds to makefort10. x in TurboRVB). The corresponding Tur-boRVB modules are listed in Table  III.
Other information, such as lattice vectors, atomic positions, MO coefficients, AGP and Jastrow matrix elements, are stored in the corresponding attributes implemented in IO fort10. The Atomic Simulation Environment (ASE)  50  package is used to read-/write molecule and crystal structures for supporting various file formats. This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. Generates a JSD/JAGP WF using a given basis set and structure. convertfort10mol. x Adds molecular orbitals. If the number of molecular orbitals is equal to (larger than) the half of the number of electrons in a system, the resultant WF becomes JSD (JAGPn). convertfort10. x Converts a JSD/JAGP/JAGPn WF to a JAGP one.
D. Fortran90 wrappers PyTurbo has several classes inheriting the fortranIO class, which are basically Fortran90 wrappers for the corresponding TurboRVB fortran binaries. The Listing 5 shows an example to compute the VMC energy of the Hydrogen This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. PLEASE CITE THIS ARTICLE AS DOI: 10. 1063/5. 0179003 dimer (assuming the optimized WF is given by a user) using PyTurbo. The input variable of VMC class is namelist instance that is an encapsulated dictionary with keys=fortran keyword and value=value of each parameter, as shown in Listing 5. Indeed, the input parameters used in the Python script contains all parameters given to TurboRVB; thus, one can fully control TurboRVB jobs via PyTurbo.
V. TURBOWORKFLOWS: A PYTHON PACKAGE FOR REALIZING HIGH-THROUGHPUT CALCULATIONS USING TURBOGENIUS Since TurboGenius is able to fully control TurboRVB jobs, one can implement workflows by combining it with a file/job managing package. To demonstrate it, as a proof of concept, we developed an open-source python package, TurboWorkflows, that enables one to compose simple workflows by combining TurboGenius with a job managing python package, TurboFilemanager. We notice that one can exploit other established workflow packages such as AiiDA  18  and Fireworks  20  as job-managing systems. Combining established workflow managers with TurboGenius is an intriguing future work. The main classes of TurboWorkflows are summarized in Table  IV. Each workflow class inherits the parent Workflow class with options useful for a QMC calculation.
The Launcher allows one to pass values and files obtained by a workflow to another workflow by using the Variable class, as shown in the listing 8. As shown in the listing 8, the Launcher class accepts workflows as a list, solves the dependencies of the workflows, and submits independent sequential jobs simultaneously. Launcher realizes this feature by the so-called topological ordering of a Directed Acyclic Graph (DAG) and the built-in python module, asyncio. The listing 8 shows a TurboWorkflows workflow script to perform a sequential job, PySCF → TREXIO → TurboRVB WF (JSD ansatz) → VMC optimization (Jastrow factor optimization) → VMC → LRDMC (lattice space → 0). Finally, we get the extrapolated LRDMC energy of the water dimer. From the practical point of view, the step where a user should be particularly careful is the WF optimization, which corresponds to VMCopt workflow in the listing 8.
VI. DEMONSTRATIONS OF HIGH-THROUGHPUT CALCULATIONS In this section, we will show several results obtained using TurboGenius and TurboWorkflows. Earlier versions of TurboGenius was used for computing phonon dispersion calculations  59  , equation of state calculations  59  , potential energy surfaces of dimers  60, 61  , binding energies of molecules  [60] [61] [62]  , hydrogen liquids  63  , and hydrogen solids  14. However, they were not fully performed using a python script. The current versions of TurboGenius and TurboWorkflows allow one to fully control TurboRVB calculations using a python script. In this paper, we show two types of demonstrations, validations of the TurboRVB package and benchmarking QMC calculations using TurboRVB. The summary of the demonstrations are shown in Tables V and VI.
A. Validations of QMC implementations Validation of scientific softwares, such as checking consistency among softwares that implement the same theory as employed in this study (i. e. , inter-software test), is an important step to ensure one's software reliability. The widespread use of validation tests is also important to ensure the trustability of numerical simulations in general. Such validation tests have been performed not-so-widely in the ab initio QMC community, since QMC requires complex computational procedures, as mentioned in the introduction. TurboGenius and Tur-boWorkflows enable one to do the tests much more easily and efficiently. In this paper, we report the results of inter-software tests for the TurboRVB package (v1. 0. 0). We checked interpackage consistencies between TurboRVB and other established quantum chemistry packages, such as PySCF  27, 28  and Quantum Package  29.
PLEASE CITE THIS ARTICLE AS DOI: 10. 1063/5. 0179003 both orthorhombic and non-orthorhombic cells are included, and (3) 4 metallic crystals. For the crystals, we employed the k = 4 × 4 × 4 (i. e. , twisted average) grid such that the reciprocal grid includes both real and complex points. For the real-space grid, which is needed for the numerical integration employed in the built-in DFT module (prep), we employed 0. 05 Bohr for the molecules and insulating crystals, while 0. 03 Bohr for the metallic crystals. We employed the Fermi-Dirac smearing method with 0. 01 Ha for the metals. Figures. S-3, S-4, and S-5 show the consistencies between PySCF and TurboRVBprep LDA calculations for all the above cases. The very slight differences come from the implementations (i. e. , TurboRVBprep module employs the numerical integration to compute the overlap and Hamiltonian matrix elements).
Validation of TurboRVB QMC module: HF ( PySCF) v. s. VMC ( TurboRVB w/o Jastrow) One of the most prominent features of TurboGenius is the functionality to convert a TREXIO file to a TurboRVB WF because it allows one to use any quantum chemistry or DFT packages to generate trial WFs as long as they employ localized basis sets. We have carefully verified the implementation of the converter by confirming the consistency between HF and VMC energies without Jastrow factor, both for molecules (open systems) and crystals (periodic systems). More specifically, we computed the HF energies of 100 molecules and 9 crystals (such that both orthorhombic and non-orthorhombic cells are included) by PySCF v2. 0. 1, where the ccECP  [37] [38] [39] [40]  with accompanied basis sets were employed. The obtained PySCF checkpoint files were converted to TurboRVB WFs via TREXIO files using the converter implemented in Tur-boGenius package.
Then, we computed VMC energies of the TurboRVB WFs without Jastrow factor. The PySCF to TurboRVB conversion via TREXIO supports both restricted (i. e. , ROHF) and unrestricted (i. e. , UHF) open-shell WFs. We tested the former implementation using the 100 molecules, while the latter using the 49 molecules. For the 100 molecules, the same consistency checks were done between HF calculations by Quantum Package (v2. 1. 2) and TurboRVB. For the crystals, we tested both single-k and multi-k (i. e. , twisted average) calculations. For the single-k tests, k=(0. 00, 0. 00, 0. 00) and (0. 25, 0. 25, 0. 25) were used. For the twisted average tests, we employed k = 4 × 4 × 4 such that the grid includes both real and complex points. Notice that, in the twisted average case, we compared the VMC energies with the averages of the HF energies obtained at each k-point independently, since the VMC is independently done for each k point (i.
e. , MCMC is done for each k. ) We did not use metals for the VMC validation tests. This is because, for metals, the HF energy obtained with the smearing technique is not consistent with the VMC ones due to the fact that the orbital contributions above the Fermi energy are truncated when converting the DFT orbitals to the single Slater-Determinant ansatz. The HF and VMC energies should be consistent within the statistical errors (i. e. , within 3σ) as long as the conversions are done correctly. The results of the validation tests are shown in Figs. S-6-S-10. The corresponding values are shown in Tables S-IV-S-VIII. The results show that the HF and VMC energies are consistent within the statistical errors (i. e. , within 3σ), implying that the implementations of the WF converter and VMC calculations in the TurboRVB package are correctly done.
Indeed, the workflow was fully automatic and fully reproducible since the determinant part which determines the nodal surface was obtained deterministically, and the Jastrow factor, which was obtained by stochastic optimization, did not affect the extrapolated FN energy. The geometries of the G2 set were taken from previous benchmark studies  [66] [67] [68]  , while those of the other dataset were taken from Benchmark Energy and Geometry DataBase (BEGDB)  69  Figure. 2 shows the benchmark result for the G2-set  30. The corresponding numbers are shown in Table S-IX. We computed the atomization energies of the 55 molecules included in the G2-set by pseudo-potential LRDMC calculations with the JDFT ansatz. We employed the cc-pVQZ basis set with the This is the author's peer reviewed, accepted manuscript.
PLEASE CITE THIS ARTICLE AS PLEASE CITE THIS ARTICLE AS DOI: 10. 1063/5. 0179003 accompanied ccECP  [37] [38] [39] [40]  pseudo potentials. For the DFT calculations, we used the PySCF package (v2. 0. 1)  27, 28  with PZ-LDA  64  exchange-correlation functional. The obtained Mean absolute deviation (MAD) with the JDFT ansatz is 3. 242 kcal/mol, which is consistent with previous studies. Nemec et al. 70  used all-electron DMC on Slater determinant (SD) WF to obtain the binding energies of the G2 set to an MAD of 3. 2 kcal/mol. Grossman  71  reported a similar accuracy, a MAD of 2. 9 kcal/mol, in binding energies by DMC pseudopotential calculations. Raghav et al. 62  reported a MAD of 3. 2 kcal/mol by all-electron LRDMC calculations with the JDFT ansatz. So far, we have discussed the atomization energies obtained using the Jastrow Slater determinant ansatz with the LDA-PZ nodal surface.