dataesr/scientific-paragraphs-categorization huggingface dataset:
of their electron densities. In the present paper, we extend the full 4-component relativistic Dirac-Kohn-Sham (DKS) method, as implemented in the BERTHA code, to include environmental and confinement effects with the FDE scheme (DKS-in-DFT FDE). This implementation has been enormously facilitated by BERTHA's python API (PyBERTHA), which provides a flexible framework of development by using all Python advantages in terms of code re-usability, portability while facilitating the interoperability with other FDE implementations available through the PyADF framework. The accuracy and numerical stability of this new implementation, also using different auxiliary fitting basis sets, has been demonstrated on the simple NH 3 -H 2 O system in comparison with a reference non relativistic implementation.
66 Prototyping techniques also based on Python are very useful to build reference implementations, as for instance the Psi4-rt-PyEmbed code,  31, 67  where the Python interface of Psi4Numpy and PyADF  30, 68  (including its PyEmbed module  69, 70  and XCFun library  71, 72  to evaluate non-additive exchange-correlation and kinetic energy contributions) has been used by some of us to build real time non-relativistic TDDFTin-DFT FDE  31  and projection-based embedding  73  implementations. In this work we extend the Dirac-Kohn-Sham (DKS) method implemented in the BERTHA code (with its new Python API, PyBERTHA)  74, 75  to the FDE scheme to include environmental/confinement effects in the DKS calculations (DKS-in-DFT FDE). The implementation takes advantages of the DKS formulation implemented in BERTHA, including the density fitting algorithms at the core of the computation (i.
e. in the evaluation of the embedding potential and of its matrix representation is relativistic G-spinor functions), and the FDE implementation already available in the PyEmbed module of the PyADF framework. The outline of the paper is as follows. In Section 2 we present the basic theory of FDE and a brief description of the DKS method as implemented in BERTHA. In section 3, we then describe in detail our implementation. In section 4 we present some numerical results, including the computational burden and scalability of this new implementation with respect to the size of the active system as well as of the embedding one. We will also present an application to a series of Heavy (Rn) and Super-Heavy elements (Cn, Fl, Og) confined into a C 60 cage. Finally, concluding remarks are given in section 5.
Theory In this section, we briefly review the basic formalism of the FDE scheme and its extension to use the DKS theory for the active system (DKS-in-DFT FDE). We will remark also some details of the DKS implementation in BERTHA, mainly focusing on those aspects (including density fitting techniques), which are relevant for an efficient implementation of the FDE scheme. Finally, we will illustrate the basic characteristics of the our recent BERTHA Python API, PyBERTHA (and the related pyberthamod module available under GPLv3 license at Ref. 67, for additional and technical details see Refs. 74-76) which is a key tool here to devise a simple work-flow for the DKS-in-DFT FDE scheme.
93  Implementation and Computational Details Before addressing the FDE implementation, we present a brief outline the new Python 66 5] [76]  All these new features have been extensively employed in the work-flow design and implementation of the DKS-in-DFT FDE method (see next section). In Figure  1   all those quantities necessary for the DKS-in-DFT FDE implementation (e. g. , the method bertha_get_density_on_grid() used to extract the values of the fitted electron density on a grid). All the new methods have been efficiently parallelized using OpenMP. 100 All details and computational efficiency will be given in the next sections.
The PyBerthaEmbed DKS-in-DFT FDE implementation In this section we outline the computational strategy we adopted to implement the DKSin-DFT FDE scheme. The developed Python program pyberthaemb. py and the related module (pyembmod) are freely available under GPLv3 license at Ref. 102. A data set collection of computational results including numerical data, parameters and job input instructions used to obtain the results of Section 4, is available and can be freely accessed at the Zenodo repository, see Ref. 103.
Implementation strategy The newly developed code is composed of two main modules: the pyembmod one, that allows to manage all the important quantities for the FDE implementation, and the pyberthamod module. 67 Specifically, the pyemb class inside the pyembmod module allows to well isolate all the FDE data and operations increasing the level of abstraction. The module is used to manage all the required quantities for the generation of the embedding potential, that is v emb [ ρI , ρ II (r)]. It has been engineered in a such manner that all details of the FDE lowlying implementation will be completely transparent from the PyBERTHA side. This has the advantage that all future developments and/or integration of the FDE scheme (g. e. using DKS theory also for the environment DKS-in-DKS FDE) will not affect the PyBERTHAembed code, i.
e. it will remain completely unchanged. In particular in this first version the pyembmod module can handle the basic procedures previously implemented in the Psi4rt-PyEmbed software which are based on the use of PyADF,  30, 68  PyEmbed module,  69, 70  and the XCFun library  71, 72  to evaluate non-additive exchange-correlation and kinetic energy contributions on a user-defined integration grids. This approach gave us both the advantage of the code re-usability and, even more importantly, a DFT-in-DFT FDE reference implementation in which we can have the precise control over all those details and parameters from which a FDE scheme depends on (i. e. , algorithms, numerical grid definition, quantum chemistry packages used to determine electronic density and Coulomb potential of the environment, basis sets, exchange-correlation functionals, etc.
). This has clearly made the debugging phase in the development of PyBERTHAembed software straightforward. Algorithm 1 reports the most important part of the pyberthaemb. py code, and it well illustrates how we can gain a relatively simple work-flow to implement FDE using the DKS level of theory for the active system using PyBERTHA and the new pyemb class for the environmental system. The pybertha class is instantiated (line 4) with the shared object bertha_wrapper. so specified as an input. The SO contains the cited c_wrapper and bertha_wrapper code which are based on the core FORTRAN libraries, namely: libbertha. so and libberthaserial. so (see Figure  1 above ). After the initialization, the full DKS calculation is worked out (line 10) using the bertha. run() method. At line 14 the pyemb class is also instantiated specifying the files (specified in xyz format) for the geometries of both the active and embedding systems.
The actual calculation is done within the FOR-TRAN layer. At this level, ρI (r) is represented in terms of the expansion coefficients of auxiliary fitting functions (d, see Eq. 19) and is stored as a FORTRAN array (of dimension N aux ). However, this representation is not useful itself for the evaluation of the embedding potential. Indeed, its evaluation and in particular the non-additive contribution requires that ρI (r) is represented on a grid. Thus, within pyberthaemb. py, the numerical grid (GRID) evaluated within PyADF (see panel init) is made available as numpy. array and via the bertha. get_density_on_grid() method is made accessible to the FORTRAN layer (bertha_wrapper module), and stored as a FORTRAN array of dimension npoints. The calculation of the numerical representation of ρI (r) on the grid is done efficiently in FOR-TRAN (see in Figure  2 , panel a) and the latter is accessible within pyberthaemb.
Initial validation and numerical stability: H 2 O-NH 3 As already mentioned, in this first version of the pyembmod module we include the basic procedures previously implemented in the Psi4-rt-PyEmbed code which are based on the use of PyADF  30, 68  and of the PyEmbed module. 69, 105 is poses us in an ideal framework of having a reference non relativistic DFT-in-DFT FDE implementation where we can have the precise control over all those details, and parameters, from which a FDE calculation depends on. Thus, for the sake of a direct comparison, we selected a simple molecular complex, namely the H 2 O-NH 3 adduct, for which the relativistic effects are expected to be negligible. For this system we can safely compare directly the numerical results of the DKS-in-DFT FDE method, implemented here, with respect to those obtained using the DFT-in-DFT FDE scheme in the Psi4-rt-PyEmbed code.
In addition to the limit auxiliary fitting basis set employed above we generated five fitting basis sets (A2 s , A2 sp , A2 spd , A2 spdf g and A3 spdf g ) of increasing accuracy. We have adopted a procedure which is strictly related with that proposed by Köster et al. and employed in Demon2K code (see appendix of Ref. 114). All the fitting basis sets are explicitly reported in SI, while the results are reported in Table  2. In the Table we also show the absolute error in the Coulomb energy (∆E J ), which is the quantity that is variationally optimized in the fitting procedure and typically regarded as its quality index. This numerical test shows that the use of density fitting does not introduce any significant instability in the DKS calculation of the active system, also in presence of the embedding potential.
The molecular structure of all the adducts are available in Ref. 103. All the calculations have been performed on a Dual Intel(R) Xeon(R) CPU E5-2684 v4 running at 2. 10GHz, equipped with 251 GiB of RAM. We used the Intel Parallel Studio XE 2018  118  to compile the FORTRAN code and Python 3. 8. 5 (from Anaconda, Inc. ) and NumPy version 1. 19. 2 for the Python code. We used PyADF  30, 68  as recently ported to Python3,  65  ADF(version 2019. 307) for the core DFT calculations of the environment and XCFun library (version 1. 99). 71, 72, 119 e results are reported in Table  3  and in Table  4 , where, together with the total elapsed time (t d ) for each SCF iteration including the FDE contribution, we also partition between different tasks related with the FDE implementation, namely: a) numerical representation of active system fitted density on grid, ρ(r); b) calculation of the non-additive terms of embedding potential by PyADF (with the PyEmbed class); c) projection of the embedding potential onto fitting basis functions.
Conclusions and perspectives Including environmental effects based on first-principles is of paramount importance in order to obtain an accurate description of molecular species in solution and in confined spaces. Among others, the Frozen Density Embedding (FDE) density functional theory represents a embedding scheme in which environmental effects are included by considering explicitly the environmental system by means of its "frozen" electron density. In the present paper, we reported our extension of the full 4-component relativistic Dirac-Kohn-Sham method, as implemented in the BERTHA code, to include environmental and confined effects with the FDE scheme (DKS-in-DFT FDE) using the PyADF framework. We described how its complex workflow associated with its implementation can be enormously facilitated by the fact that both BERTHA (PyBERTHA) and PyADF, with their Python API, they gave us an ideal framework of development.
The recent development of the Psi4-rt-Embed code,  31  which is also based on PyADF for FDE while uses Psi4Numpy code for the active system, represented an ideal reference implementation to assesses the correctness of our new DKS-in-DFT FDE implementation. PyBERTHAembed uses the density fitting technique at the key points of the interface between PyBERTHA and PyADF. We showed that this results both in a very efficient numerical representation of the electron density of the active system and in a straightforward evaluation of the matrix representation in the relativistic G-spinor basis of the embedding potential. The accuracy and numerical stability of this approach, also using different auxiliary fitting basis sets, has been demonstrated on the simple NH 3 -H 2 O system. We compared the dipole moment components and diagonal elements of the polarizability tensor of the isolated water molecule with respect to the embedded water (i.
Initially we took advantage of the capability of PyADF to dump the Embedding Potential into a file. Subsequently the file is processed via a simple Python script (i. e. gridtodx. py S2 ) that, using a Nearest neighbor interpolation, as implemented in the SciPy,  S3  transforms the potential, originally represented on a non homogeneous grid to a potential evaluated on a homogeneous grid, producing a DX file via the gridDataFormats package. S4  Once the EMBP has been dumped in a DX file, using an homogeneously spaced grid, quite easily the Spherical overage can be evaluated (i. e. avgalongr. py S2 ) simply considering the average of the Embedding Potential values contained in spherical shells, always centered in the A atom, of increasing radius and constant thickness dr. : Differences in orbital energies with respect to the isolated Rn atom for the Rn-C 60 system using the frozen density embedding (FDE) scheme (where Rn is used as active system while C 60 as frozen environment) and the supramolecular calculation (Supramolecular).
; Carter, E. A. Self-consistent embedding theory for locally correlated con- (102) REL 1. 0. 0 URL: https://github. com/BERTHA-4c-DKS/pybertha/releases/tag/ rel_1_0_0 within the PyBertha project: https://github. com/BERTHA-4c-DKS/ pybertha/ written by: L. Storchi, M. De Santis, L. Belpassi. (103) Santis, M. D. ; Sorbelli, D. ; Vallet, V. ; Gomes, A. S. P. ; Storchi, L. ; Belpassi, L. Data related to "Frozen-Density Embedding for including environmental effects in the Dirac-Kohn-Sham theory: an implementation based on density fitting and prototyping techniques". 2022; https://doi. org/10. 5281/zenodo. 6343894. (104) Dułak, M. ; Kamiński, J. W. ; Wesołowski, T. A. Linearized orbital-free embedding potential in self-consistent calculations. Int. J. Quantum Chem. 2009, 109, 1886-1897, DOI: 10.
We have developed and validated a score that combines all these possibilities into a single rule: 4PEPS (four-level pulmonary embolism probability score)  [15]. Based on ISTH recommendations and depending on the prevalence of VTE in the population, the acceptable number of false negatives has been set to 2%  [16]. Using 12 variables, 4PEPS defines four levels of CP that rule out a PE, namely (1) based only on clinical data (very low CP: PEPS < 0), (2) based on a D-dimer level < 1000 µg/L (low CP: PEPS ≥ 0 et < 5), (3) based on a D-dimer level with an age-adjusted cut-off value (moderate CP: PEPS ≥ 5 and <12), or (4) the diagnosis cannot reliably be ruled out based on a D-dimer test (high CP: PEPS ≥ 12) (Table  1 ). Assessed retrospectively based on large databases, the 4PEPS strategy seems to be more efficient and just as safe as the other strategies  [15, 17, 18].
Comparison Strategy: Control Group The investigating physicians of the participating centers in the control group will be free to provide care as they see fit. However, a reminder of national and European guidelines for PE management will be given to them and they will have the recommendation to apply a validated strategy  [3] [4] [5]. To make it easier, the different scores will be included in the clinical help-decision support software called SPEED  [23]. Investigators will be asked to enter data about the included patients directly into SPEED, which will act as the study's electronic case report form (eCRF). A paper version of the CRF will also be available.
Strategy under Study: Interventional Group Physicians of the participating centers in the intervention group will have the recommendation to apply the 4PEPS strategy. To make it easier to apply, the 4PEPS score will be included in SPEED  [23]. Investigators will be asked to enter the information relating to patients included in the study directly into SPEED before performing any testing. Entering these data will enable the 4PEPS score to be calculated automatically and specific recommendations to be provided. A paper version of the CRF will also be available.
Study Participant Follow-Up Data relating to care at the emergency department and the eventual subsequent hospitalization (characteristics, comorbidities, clinical and paraclinical data, confirmed diagnosis or diagnoses, possible adverse events, and treatment) will be compiled and entered into the eCRF. At discharge from the hospital, patients will be instructed to return to the ED or to contact their family physician in the event of any symptoms that may suggest a possible thromboembolic event or bleeding. Patients and/or their relatives and family physician(s) will be contacted by phone and/or post or email at the end of the 3-month follow-up period. Using a standardized questionnaire, they will be asked about whether a thromboembolic event or bleeding has occurred; what examinations, consultations, or hospitalizations have taken place; and whether anticoagulant treatment has been introduced.
General Considerations Population characteristics will be described using their mean/standard deviation, median/interquartile range, or percent/95% confidence interval, as appropriate. Univariate comparisons will be made using Student's, Mann-Whitney, or Fisher's exact tests, as appropriate. The alpha risk will be set at 5%. All tests will be one-sided. Given that some proportions are expected to be small, confidence intervals will be calculated using the Clopper and Pearson exact method. The analysis will be performed using R software (R Foundation for Statistical Computing; 2008. http://www. R-project. org (accessed on 1 November 2022)) and its PropCIs and MASS add-ons (http://CRAN. R-project. org/package=PropCIs (accessed on 1 November 2022)). All objectives will be analyzed on the overall included population (intention-totreat).
Primary Statistical Analysis The two main endpoints will be analyzed in a hierarchical manner. Firstly, noninferiority will be analyzed on the safety endpoint with a non-inferiority limit set at 1. 5%. If and only if confirmed, superiority analysis on the efficacy endpoint will be performed. Analyses will be performed using a mixed-effects logistic regression in line with recommendations for this type of study  [26]. Missing data will be analyzed in order to determine whether they are informative and whether they are likely to cause a potential screening or information bias.
To ensure that patients are managed according to good medical practice throughout the study, the physicians will be reminded of relevant recommendations and will have the possibility to use the clinical help-decision support software SPEED. SPEED has been shown to improve the diagnostic management of suspected PE in a prospective randomized multi-center study that compared SPEED to the wide distribution of recommendations in paper form  [23]. As part of a de-escalation approach, aimed at limiting the overuse of diagnostic testing, analysis of the efficacy on high-risk and/or costly diagnostic investigations (in this case, CTPA or scintigraphy) must be combined with a safety analysis to ensure that the reduction in investigations is not linked to an increase in the number of serious clinical events related to the diagnostic approach.
We implement a three-body potential to model associative bond swaps, and release it as part of the HOOMD-blue software. The use of a three-body potential to model swaps has been proven to be effective and has recently provided useful insights into the mechanics and dynamics of adaptive network materials such as vitrimers. It is elegant because it can be used in plain molecular dynamics simulations without the need for topology-altering Monte Carlo steps, and naturally represents typical physical features such as slip-bond behavior. It is easily tunable with a single parameter to control the average swap rate. Here, we show how associative bond swaps can be used to speed up the equilibration of systems that self-assemble by avoiding traps and pitfalls, corresponding to long-lived metastable configurations.
Data availability The data and the codes to reproduce the results of this paper are available at: gitlab. tue. nl/SCiarella/Associative_bond_swaps_in _MD. FIG. 7 : Average number of swaps ns measured by dividing the simulations of sec. III A into sub-boxes, and their standard deviation σs. We compare the numbers calculated in the simulations to values expected from the binomial distributions (bin). The similarity between those quantities proves that swaps events are homogeneously distributed in space. bour lists and they benefit even more of the GPU acceleration. In particular, the simulations in Ref. [20]  benefited from a speed up factor of 50 when evaluated on a Tesla P100-PCIE-16GB GPU. It follows that a more complex model that aims to include a sub-set of swapping moieties can then capitalize on the full speed up factor provided by the GPU implementation, which is usually larger than 15  [27].
Models and reanalysis data The reference data used in this study are the reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5  (Hersbach 2016). Daily surface temperature (TAS, in K) and precipitation (PR, in mm/day) data have been extracted for the period 1979-2019 over the entire globe. This work is also based on the 20 CMIP5 models listed in Table  1. For each model, we extracted the same variables as in ERA5: TAS and PR. For the 1850-2005 period, data are extracted from the historical simulations and for the 2006-2100 period, from the projections made under the Representative Concentration Pathway 8. 5 (RCP8. 5). The RCP8. 5 is one of the four greenhouse gas concentration trajectories considered by the Intergovernmental Panel on Climate Change in their Fifth Assessment Report.
Hence, the whole procedure can be repeated a certain number of times with different initializations and orders of models and the outcome with the lowest energy can be retained. In practice, for our datasets and the few sensitivity tests we made, results were very similar (not shown) and the order of models in the α -β swap algorithm did not matter much. For this reason, we have chosen in this paper to simply run the α-β swap algo- rithm once and initialize it with the labeling that minimizes the data energy.
Combination approaches In this paper, we compare the performance of different multi-model combination approaches, either based on MMMs or on GC. They are evaluated based on out-ofsample testing: when needed, the approaches are tuned on a calibration period (learning dataset) and their performances are evaluated on a projection period (test dataset). This way, the robustness and generalization capability of the combination approaches can be assessed. We have selected three approaches from the MMM family and four from the GC one: • multi-model mean (mmm): each model is given the same weight to compute the average. Since it is the most commonly used approach in the literature, the multi-model is used as a baseline in this study. • om_present: a weighted multi-model mean where the weight of each model is optimized on the calibration period in order to minimize the cost function: where the weights = (w f ) f ∈F are positive and sum up to 1.