dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated kilovoltage X-rays The X-ray spectra used in this work were calculated by the program SpekCalc  [59]  simulating an X-ray tube with a tungsten target. The parameters used to produce the X-ray spectra are as follows: • Peak voltage (kVp): 50 and 100 • Energy bin (keV): 0. 5 • Angle theta (degree): 20 • Air thickness (mm): 470 • Beryllium thickness (mm): 0. 8 • Aluminum thickness (mm): 3. 9 • Nf: 0. 68 • P: 0. 33 The model parameters 'Nf' and 'P' in the GUI interface take the default values of 0. 68 and 0. 33  [60]. The former normalizes the overall fluence and can be used to match the output prediction to that of a particular X-ray tube, if desired. The latter is the ratio of the number of characteristic X-rays produced via electron impact ionization to that generated by bremsstrahlung interaction with the atomic nucleus; this ratio should not be changed without justification  [59].
Uncertainty of DER and energy spectra of secondary electrons The MC codes used in this exercise were: PENELOPE, MDM, PARTRAC, MCNP6, NASIC and Geant4/Geant4-DNA and TOPAS/ TOPAS-nBio. Detailed information of each code was presented in Appendix. This information can help to understand the variation of the results. The simulated results delivered by different participants showed a variation on the DER and the energy spectra of secondary electrons escaping from the GNP. A quantitative uncertainty analysis revealed a larger uncertainty factor ranging from 2. 9 up to 8. 8 (see Table  2 ). A very large uncertainty was found at energy 100 eV for the scenario of 100 nm irradiated by 100 kVp X-ray spectra. The reason of this large variation of electron spectra among the participants can be potentially further investigated and searched from the Table  1.
In order to satisfy the Heisenberg uncertainty principle, uncertainties of 5% must be assigned to position and momentum for 1 keV electrons in water; at 100 eV, these uncertainties are 17-20% and are even larger at lower energies  [68]. Most MC simulation codes used in this work provide event-by-event transports of particles in water and gold down to 50 eV, such as in PENEL-OPE, and even down to several eV, e. g. in the combination of Geant4/ Geant4-DNA, PARTRAC and other codes. However, in Geant4 track structure is not calculated for interaction of electrons in gold, in contrast to PARTRAC. As these low-energy electrons deposit their energy on the nanometer scale, a large uncertainty of the dose enhancement effects on the nanometer range should be expected. The present exercise demonstrates that, besides fixing input parameters of the geometry setup and source spectra in the MC codes, physical models and code versions should also be fixed among participants.
By doing so, systematic uncertainties could be better identified, for example, between Geant4 and TOPAS. For the exercise reported in this paper, the requirement of fixing the same code versions and physical models was not imposed on the users and will be implemented in the envisaged next EURADOS exercise of inter-code comparisons on multiple GNPs distributed around a cell nucleus. Furthermore, the content of this exercise should be kept as simple as possible and should avoid in-between data transfer and conversion. Quantities calculated for comparison must be clearly defined. A mid-term deadline for delivering results should be given to all participants. If possible, a format of presenting the results will be defined and provided.
A. 2. TOPAS/TOPAS-nBio code The TOPAS (TOol for PArticle Simulation) project, launched in 2009, is a software project to simplify the use of MC simulations for cancer research and clinical applications  [78]. TOPAS provides a flexible framework to design and share simulation settings across research groups and radiotherapy centers that use similar equipment. This is achieved via a parameter control system that defines all aspects of the simulation without requiring compilation of code. Parameters are specified in one or more text files to define the properties of the simulation. An extension system was developed for users who need additional features specific to their own application, such as custom scorers or geometries. The extension manager allows users to add new features by writing a short section of simple, C++ code based on templates and C++ helper functions provided within TOPAS  [79].
TOPAS is now part of the US National Cancer Institute's (NCI) Informatics Technology for Cancer Research (ITCR) Initiative and free of charge for any user conducting education or research in medical physics and radiation biology for non-profit organizations. The code is available on the TOPAS webpages  [80]. The simulations presented here were performed using the TOPAS-nBio, an extension of TOPAS version 3. 1. p3 which is based on Geant4 version 10. 3. p1. TOPAS-nBio  [81]  is an extension aiming to provide the ease of use provided by TOPAS to simulations at the nanometer scale for cellular and sub-cellular radiobiology using Geant4-DNA physics and chemistry models. The default Geant4-DNA (option 0) was used for the simulation of particle transport within water but Geant4-DNA currently does not include cross sections for gold.
A. 3. PENELOPE code The computer code system PENELOPE  [84]  simulates coupled electron-photon transport in arbitrary material systems consisting of a number of homogeneous regions (bodies) limited by sharp (and passive) interfaces. The name is an acronym that stands PENetration and Energy LOss of Positrons and Electrons in matter, photons were introduced later. The adopted interaction models, and the associated databases, allow the simulation of electron/positron and photon transport in the energy range from 50 eV to 1 GeV. Photon transport is simulated by means of the standard, detailed simulation scheme. Electron and positron histories are generated on the basis of a mixed procedure, which combines detailed simulation of hard events with condensed simulation of soft interactions. A geometry package called PENGEOM permits the generation of random electron-photon showers in material systems consisting of homogeneous bodies limited by quadric surfaces, i.
In version of PENELOPE-2014, there are some changes in global parameters, geometry input and other modifications, like, (1) ionizations of inner shells by electron and positron impact are described as proper inelastic collisions, this ensures that the net deposited energy in each interaction event is positive; (2) cross sections for ionization of N subshells calculated from the relativistic distorted wave Born approximation have been included in the database. However, for this simulation, no essential changes on the energy deposition and dose distribution will be expected.
A. 4. PARTRAC code PARTRAC has been developed from a track structure simulation code, MOCA, for electrons in the water vapor  [85]. During the last decades, several functional modules have been added making it now a powerful tool for simulating initial DNA damages  [86]  and damage responses  [87]  at the cellular level in addition to radiation track structures and their micro-or nanodosimetric analysis. An overview of PARTRAC capabilities is given elsewhere  [88]. Owing to its modular structure and flexibility, PARTRAC code can be adapted for special investigations. This enables PARTRAC code to calculate and simulate the impact of radiation doses and effects of GNPs from molecular to cellular levels  [25]. The track structure module of PARTRAC can transport different types of ionizing particles including photons, electrons, protons, alpha particles and heavier ions.
In the present context, only the physical stage is of concern which includes the transport of ionizing particles, together with the generation of ionized and excited atoms and molecules in the medium in an event-by-event way. Within the defined tracking volume, full information of both primary and secondary particles can be recorded, e. g. for electron spectra analysis, or evaluated on-the-fly from a corresponding data stream, as done here for dose enhancement analysis. Cross sections for photon interactions are taken from the EPDL-97 dataset  [72]  considering the elemental composition of the (usually biological) materials, water and gold in the present work. Relaxation processes after the removal of inner shell electrons by photoionization due to radiative (fluorescence) or non-radiative (Auger or C-K) transitions have been considered based on the EADL dataset  [89].
The transport of electrons in water was simulated with the corresponding cross sections  [53] , while the electron impacting ionization cross sections for gold were calculated with the method of Seltzer  [90] , based on the theory of Weizsäcker  [91]  and Williams  [92]. An additional module was implemented in PARTRAC for this specific exercise work. They contained two parts: (1) the close collision part that described collisions between two electrons associated with high energy transfers and small impact parameters; and (2) the distant collision part that considered interactions of a virtual photon field emitted by the projectile electron with orbital electrons. Input parameters include the mean kinetic energies of orbital electrons, the expectation value of the electron orbital radius and the binding energies.
8 database was developed for electron transport. This new method allows a direct sampling of microscopic data distributions and consequently an accurate low-energy transport from 1 keV down to 10 eV. The ENDF/B-VI. 8 database contains cross sections for atomic excitation, electron elastic scattering, subshell electron-impact ionization and bremsstrahlung processes  [120]. All cross sections currently used for photon and electron interactions apply to neutral atoms in their ground state and therefore do not consider molecular effects or material structure in condensed (solid or liquid) states  [120]. Especially for electron energies less than 1 keV aforementioned effects could lead to significant variations in elastic and inelastic collisions  [58]. Detailed discussions about models used to obtain the ENDF/B-VI.
8 data library can be found in reference  [89]. Even if this single-event mode is a relatively new feature of MCNP6, several works in literature reported on the reliability of the MCNP6 single-event option  [121, 122]. For photons, the traditional photon-atomic interaction processes are considered: coherent and incoherent scattering, photoelectric absorption, and electron/positron pair production. In addition to extending some pre-existing data to lower energies the ENDF/B-VI. 8 database includes also subshell photoelectric cross sections  [120]. A very significant addition to the photon/electron data in MCNP6 is a detailed compilation of information about atomic electron subshells. In contrast to the previous model, which considered only relaxations to the K shell and to a weighted average of the L shells and allowed a maximum of five distinct transitions to these shells, the new data can consider as many as 29 subshells and can include almost 3000 distinct transitions among them.
program " Investissements d'Avenir " operated by the  French National Research Agency (ANR) , and by the Foundation ARC pour la recherche sur le cancer. The work done by  A. Ipatov  received financial support from  INCa-ITMO  (No. PC201307-BIOHYDRA ) within the  Programme Plan Cancer 2009-2013 (Inserm ). The work of  A. Belchior  was supported by " Fundação para a Ciência e Tecnologia " (grant  SFRH/BPD/ 112654/2015 ). The work of  S. Di Maria  was supported by " Fundação para Ciencia e Tecnologia"  (project  UID/Multi/04349/2019 ). The work by  J. Schuemann  and  B. Rudek  was supported by the  NIH/NCI  with Grant No. R01CA187003  ( TOPAS-nBio: A Monte Carlo tool for radiation biology research ) and  German DFG  with Grant No. RU 2197/1-1. The authors thank  Maria Antonia López ,  Bastian Breustedt ,  Rick Tanner.
Machine learning (ML) and related artificial intelligence algorithms are expected to play a central role in this area. ML techniques can assist the high-throughput search for suitable materials for given applications, by harvesting the massive amount of raw data generated and stored by the computational materials science community. ML is particularly useful to identify patterns within large amounts of complex data, and many groups started to explore these methodologies for extracting knowledge and insight from materials databases  [28]. ML is also seen as a powerful tool to advance the identification of quantum phase transitions and to deepen our understanding of many-body physics and quantum topological matter  [25]. This nascent field holds great promise and is expected to play a fundamental role in progressing towards new materials solutions for next-generation quantum technologies.
Advances in Science and Technology to Meet Challenges Topological insulator physics and technology has not reached the readiness level to envision the building up of a genuine "topotronics" foundry. If narrow-bandgap semiconducting TIs (CdHgTe, PbSnTe) are produced at the wafer scale with high mobility, it is not yet the case of medium bandgap vdW-TIs. In the near future, the latter will largely benefit from the intense efforts worldwide to realize large scale, high-quality films and heterostructures with controlled chemical doping. On the device side, a better understanding of the specific field effect in TIs is needed because of the coexistence of trivial and topological carriers. It would deserve a new compendium on the physics of topological semiconducting devices. It is also very desirable to build a comprehensive database on TI-material excitations, in complement to the recent catalogue of high-quality topological crystals [10], as they play a crucial role in limiting finite-bias properties of topological devices.
These can be broadly classified into learning properties of three types (Fig. 1b ): i) pre-Schrödinger equation, such as learning the electronic density  [2] , to be then used as input for DFT or ML; ii) replacing-or accelerating-Schrödinger equation, thus creating machine-learned approximations to solving the quantum problem; and iii) post-Schrödinger equation, such as directly learning the outputs of the problem, as structures, energies, and materials properties. Examples include the discovery of novel 2D materials and properties  [3] , learning and predicting novel topological materials  [4] , and creating a twistronics database for identifying strongly correlated behavior in two-dimensional systems  [5]. [2] ; ii) replacing-  [7]  or accelerating-Schrödinger equation, e. g. Bayesian optimization for geometry relaxation, nudged-elastic band (NEB) calculations, or global optimization  [8] ; or iii) post-Schrödinger equation, such as directly learning materials properties  [3].
High-throughput approaches present challenges regarding the construction of consistent databases for expensive calculations, unfeasible to be performed exhaustively. Although successful, machine learning approaches require four interdependent components (Fig. 2a ), each demanding careful examination and presenting its own challenges: 1) problem definition, 2) data (inputs), 3) representation, and 4) algorithms and model training. Current ML areas for materials science. Atomistic ML allows structural exploration (global search) and properties, while neglecting quantum effects. Model Hamiltonian ML allows exploration of quantum and many-body effects, while hardly applicable to real material systems. Materials or DFT ML must bridge the gap between those areas, possible steps forward are extracting realistic Hamiltonians from DFT calculations  [9]  and generating wavefunctions from atomistic ML  [7].
Regarding input data, quantum materials generally allow for the creation of smaller databases, in contrast to big-data datasets. Accordingly, the representation used and the algorithm to be employed must also be carefully selected. Merging experimental and literature-extracted data can be valuable, with the additional benefit of improving agreement to experiments, as also using multifidelity, data augmentation, and transfer learning techniques. Certainly, the representation, also called descriptor or fingerprint, is the central element in ML for quantum materials, where nonlocal, collective, and many-body effects are essential. The challenge is distilling the fundamental information from the systems into efficient numerical descriptions (Fig  2b ). Insights can be taken from two more mature ML areas (despite having larger datasets): atomistic potential energy surface (PES) ML, and many-body Hamiltonian ML (see Roadmap section #).
The first usually uses additive descriptions based on local atomic environments, while the latter succeeded by translating Hamiltonians into image-like vectors or tensors commonly used with deep neural networks. Developments for quantum materials lie between these two areas, i. e. describing quantum phenomena in realistic systems, with ML as a driver for advances (Fig  2c ). Lastly, the algorithm choice must suit the problem data size and representation, for instance, deep learning can have hundreds to millions of trainable coefficients which is unreasonable for small datasets. Finally, exciting advances are expected in the area of distilling the data-driven correlation models for driving theoretical understanding. Interpretability of generated ML models is a first step in this direction, but progress toward physics-enhanced surrogate models  [3] , and the broader goal of machine-assisted discovery of underlying fundamental physics  [10]  is essential.
Figure2. Schematic of new materials with potential for spin orbit torque technologies, and their possible use as large spin orbit coupling (SOC) layer (orange arrows). Additionally, 2D materials and antiferromagnets can also be embedded in the MTJ stacks (blue arrows), both as the insulating barrier or the magnetic element. The authors have used VESTA[12]  and Cyrstallography Open Databases for the sketch of 2D crystal structures.
Acknowledgements We acknowledge financial support by the  Deutsche Forschungs-gemeinschaft  through Grant  VA117/15-1. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958  Acknowledgements YWS was supported by the  NRF of Korea  (Grant No. 2017R1A5A1014862 , SRC program: vdWMRC Center) and by  KIAS  individual grant ( CG031509 ). ACKNOWLEDGEMENTS L. T. acknowledges support from the  Canadian Institute for Advanced Research  as a  CIFAR Fellow of its Quantum Materials Program. superconductivity, the observation and manipulation of Majorana zero modes, and possibly new high-Tc superconductors, to name just a few examples, along this highly fascinating road of research. Acknowledgements C. H. acknowledges support from the  Austrian Science Fund (FWF)  Project No.
Acknowledgements [Please include any acknowledgements and funding information as appropriate. ] Authors express their deep gratitude to the medical care personnel during the Covid-19 pandemic. B. P. wishes to thank  E. Bocquillon  for fruitful discussions and suggestions. A. I. F. and B. P. acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme  under respectively the  Marie Skłodowska-Curie  grant agreement No. 796925 , and the grant agreement No.
Acknowledgements EB has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (TOCINA , grant agreement No. [834290]). JN acknowledges funding from the  Danish National Research Foundation Center for Quantum Devices  and the  Carlsberg Foundation. 5 Acknowledgement P. F. -D. acknowledges support of a  Junior leader fellowship  from  "la Caixa" Foundation  ( ID100010434 ) with code  LCF/BQ/PR19/11700009 , funding from the  Ministry of Economy and Competitiveness , through contracts  FIS2017-89860-P  and  Severo Ochoa   SEV-2016-0588 , by the  QuantERA  grant SiUCs by the  Agencia Estatal de Investigación  with project code  PCI2019-111838-2 , and by the  "la Caixa" Foundation , under  MISTI program  through agreement  LCF/PR/MIT17/11820008.
S. D. acknowledges support from the  European Union , through the  Horizon 2020 research and innovation program  (Grant Agreement No. 810504 ), and from the  Agence Nationale de la Recherche , through the  CMOSQSPIN  project ( ANR-17-CE24-0009 ). Acknowledgements We acknowledge support from the  H2020 European Research Council PoC  under Grant Agreement 899896 SOTMEM and from  MINECO  with grant  FJCI-2016-28645. Acknowledgements M. V. C. acknowledges discussion with  Nicolas Reyren  and support from the  Spanish Ministry of Economy and Competitiveness ,  MINECO  (under contracts  FIS2015-62641-ERC ,  MAT2016-75952-R  and  SEV-2017-0706 Severo Ochoa ). Acknowledgements Eun-Ah Kim was supported by  DOE  basic energy sciences through Award  DE-SC0018946  and by  NSF ,  Institutes for Data-Intensive Research in Science and Engineering -Frameworks  through award OAC-1934714; by the  Cornell Center for Materials Research  with funding from the  NSF   MRSEC program  ( DMR-1719875 ).
Acknowledgements GRS and AF acknowledge financial support from the  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) , project numbers  2017/18139-6  and  17/02317-2. ORCID iDs Gabriel R. Schleder: https://orcid. org/0000-0003-3129-8682Adalberto Fazzio: https://orcid. org/ 0000-0001-5384-7676.
Characterization of Ca(HB) 2 Two XRD patterns among those collected were selected to be solved and indexed. The first pattern was that obtained at 19°C for 1 and consists in a mixture of CaH2 and HB. It was refined using the Rietveld method to confirm the proportion of the two phases (cf. SI). The second pattern was the one collected at 62°C; it belongs to 2 that was found to consist in a mixture of Ca(HB)2 and CaH2. The unit cell parameters of Ca(HB)2 were determined using DICVOL06  [24]. Starting from the determined parameters, the structural model of Ca(HB)2 was obtained by using FOX  [25]. The as-obtained model was refined using the JANA2006 software package  [26]. The Rietveld refinement was carried out considering the two phases, Ca(HB)2 and CaH2, while for Ca(HB)2 the fragments N-N-B were considered as being two different molecular fragments and as being rigid.
Acknowledgments The authors thank: the  French State  via the  ANR (Agence Nationale de la Recherche ) and the program " Investissements d'Avenir " with the reference  ANR-10-LABX-05-01 ; the  Région Languedoc-Roussillon  and the program " Chercheur(se)s d'Avenir 2013"  (project  C3 / 2013 008555 ); and the  Institut Carnot Chimie Balard Cirimat  (project  408-15AC1 ). The authors also thank  Dr. Jean-Fabien Petit  ( Univ Montpellier ) who contributed to the first experiments towards 22 the synthesis of 2,  Dr. Fabrice Salles  ( CNRS ) who was of help during the synchrotron X-ray thermodiffraction experiments, and  Dr. Danielle Laurencin  ( CNRS ) for her help in the 43 Ca MAS NMR analyses.
EXPERIMENTAL TECHNIQUES 2. 1. Neutron Powder Diffraction. The NPD experiment was performed at the high-resolution two-axis diffractomer D2B at the Institut Laue Langevin (ILL), France. Data were collected on 6. 3 g of polycrystalline BaZrO 3 powder, placed in a cylindrical sample holder of Al with a diameter of 8 mm inside a standard cryostat. The wavelength was set to λ = 1. 051 Å using the (557) reflection of a vertically focusing germanium monochromator. Diffractograms were collected at T = 5, 100, and 300 K. The data were analyzed using the standard Rietveld refinement method as implemented in the FullProf software. 32. 2. Inelastic Neutron Scattering. The INS experiment was performed on the thermal neutron three-axis spectrometer IN8 at the ILL, France. The sample, the same as used in the NPD experiment, was placed in an open cylindrical sample holder of Nba purely coherent scatterer, chosen to minimize the incoherent contribution of the elastic line.