dataesr/scientific-paragraphs-categorization huggingface dataset:
The growth of machine-learning (ML) technologies and databases have extended the applicability of these simulation methods. The FP-database was, for example, used to identify acid-stable oxides in the cathode environment  [218] , and a convolutional neural network model was used to accelerate the screening of ternary Pt alloy catalysts  [219]. ML-based image recognition technologies also enabled accurate reconstructions of CL structures from microscopy images, which are required for the continuum theory simulations  [220]. There are, however, a lot of challenges in simulations of complex practical materials. Difficulties mainly arise in computations of distribution functions, transport coefficients, potential-dependent reaction rates and interatomic potentials, which need to be transferred from one scale to the other in the multi-scale simulation framework.
Advances in science and technology to meet challenges Emerging ML technologies are the key to surpass the previous limitations of the modelling and simulations toward accurate and robust bottom-up approaches starting from FP methods. ML interatomic potentials  (MLPs) were shown to realize orders of magnitude faster computations of interatomic interactions retaining near FP-accuracy  [221] , and state-of-the-art active-learning schemes accelerated the time-consuming trainings containing preparations of FP datasets  [222]. Several MD simulations using MLPs were conducted on anhydrous electrolytes  [223]  and water/Pt interfaces  [224, 225]  and exhibited promising results. ML-algorithms were also demonstrated to systematically construct interparticle interactions of coarsegrained model that can accurately compute free energies of solvated molecules  [226].
Because MLPs for complex multi-element systems need large FP datasets, their applications are still limited to simple systems. In this rapidly growing field, however, many advanced MLP algorithms are investigated, and accuracy and efficiency continue to improve  [227]. In addition to the MLP algorithms, efficient and robust statistical sampling algorithms combined with MLPs are also necessary in order to accurately predict the thermodynamics and kinetics of electrolyte and electrode materials. Free energies of reaction intermediates, for example, are essential descriptors of the catalytic activity, but most of FP-based methods compute the free energies by assuming harmonic vibrations of atoms  [216]  although interfacial solvent molecular motions are anharmonic. The same difficulties also appear if one tries to compute potential-dependent activation free energies beyond the descriptor-based approach  [228].
In recent years, several practical realizations of GCE-DFT have been implemented in the GPAW code  [237]. GPAW is an open-source, Python-based DFT code using the projector augmented wave (PAW) method and allows calculations within real-space grid (FD), LCAO  [238]  and plane wave (PW) basis sets. The Python environment together with Numpy [239] and Scipy  [240]  facilitates implementation, while numerical performance is guaranteed by external C/C++/Fortran libraries. Unlike PW-based GCE-DFT implementations  [241] , GPAW works in real-space within FD and LCAO bases; this allows direct utilization of natural 2D periodic boundary conditions (PBCs) for the electrode and electrostatic potential  [188, 233, 242]  difficult to achieve with PW codes  [243]. The 2D PBCs effectively build a reference electrode within the simulation cell  [188, 242]  provide correct decay of the electrostatic potential needed for treating continuum electrolytes, and allow the simulation of asymmetric slabs/electrodes.
Advances in science and technology to meet challenges The combination of (GCE-)DFT and classical liquid theories is well-documented but cur-rent implementations [251-253] have typically been hard-coded in the DFT packages and require significant implementation work. On the other hand, it was recently demonstrated  [245]  that GPAW can be directly interfaced with an external classical DFT code with minimal changes to both codes. We therefore propose that GPAW could be rather naturally interfaced with already existing, open-source RISM  [254]  or classical DFT implementations [255] rather than re-programming them; this task should be greatly facilitated by GPAW's flexible and modular Pythonic structure. The Pythonic structure is also expected to facilitate interfacing GPAW with machine learning methods such as on-the-fly generation  [256]  of data-driven force fields.
Acknowledgments This project has received funding from the  European Research Council (ERC)  under the  European Unions Horizon 2020 research and innovation programme  (Grant Agreement No. 949012 ). This work was partially supported by the  Wallenberg Initiative Materials Science for Sustainability (WISE)  funded by the  Knut and Alice Wallenberg Foundation  (KAW). J C is grateful for the funding support from the  National Natural Science Foundation of China  (Grant Nos. 21861132015 ,  21991151 ,  21991150  and  22021001 ).
Acknowledgments This work is supported by the  U. S. Department of Energy (DOE) Office of Science Scientific User Facilities  project titled ' Integrated Platform for Multimodal Data Capture, Exploration and Discovery Driven by AI Tools '. M C acknowledges the support from the  BES SUFD Early Career award. Work performed at the  Center for Nanoscale Materials , a  U. S. Department of Energy Office of Science User Facility , was supported by the  U. S. DOE, Office of Basic Energy Sciences , under Contract No. DE-AC02-06CH11357.
Acknowledgments This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreement No. 771294 ). It was supported by the  French National Research Agency  ( Labex STORE-EX , Grant No. ANR-10-LABX-0076 ). Acknowledgments P J acknowledges the support from the  Swedish Energy Agency  (Grant  P50638-1 ) to M A, the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement  957189  ( BIG-MAP ), a part of  Battery 2030+ , his  Swedish Research Council 's  Distinguished Professor Grant , and  Sweden's Innovation Agency (VINNOVA)  through  Battery Alliance Sweden  (BASE). T M acknowledges financial support from the  NEXT Center of Innovation Program  ( COI-NEXT , Grant Number  JPMJPF2016 ) of the  Japan Science and Technology Agency  and a  Grant-in-Aid for Scientific Research (KAKENHI , Grant Number  21K05263 ) of the  Japan Society for the Promotion of Science (JSPS).
Acknowledgments M M M acknowledges the funding by  Academy of Finland  (Project #  338228 , CompEL). G K thanks the  European Union 's  Horizon 2020 research and innovation program  (Grant #  851441 ,  SELECTCO2 ) and the  Villum foundation  (Grant #  9455 ,  V-Sustain ). Both authors acknowledge the GPAW developers for their continuous efforts to maintain and improve GPAW.
The residual stress was determined using the ZrN 200 and Ti 0. 33 Al 0. 67 N 200 peaks employing the sin 2 ψ method in side-inclination mode with tilt angles ψ between 0 and 82 • and X-ray elastic constants 1  2 s hkl 2 , as described at the end of this section. XRD pole figures (PFs) were recorded for the 111, 200 and 220 reflections of the fcc-ZrN and fcc-Ti 0. 33 Al 0. 67 N phase with tilt angles from 0 to 84 •. To determine a reference unstrained lattice parameter, X-ray diffractograms of powdered single-layer ZrN and Ti 0. 33 Al 0. 67 N coatings were recorded in Bragg-Brentano geometry and evaluated applying a Pawley-refinement using the software Topas 6. In order to enable the CSnanoXRD experiments, a 150 μm thick lamella of the substrate/coating compound was cut out using a diamond cutting wheel, mounted on a Struers Accutom-5R precision saw, and further thinned down to a thickness of ~45 μm using focused ion beam (FIB) milling.
CSnanoXRD experiments were performed at the nanofocus extension of the ID13 beamline at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France  [16]. The raw data can be found under ref. [23]. The monochromatic X-ray beam with a photon energy of 13 keV and a point focus with a diameter of ~50 nm was directed at the lamella parallel to the sample surface. The cross-section of the sample was scanned along the substrate normal direction in transmission geometry by moving the sample through the beam with a step size of 25 nm. For each scan step, a two-dimensional diffraction pattern was recorded using a Dectris Eiger X 4M detector. The patterns were evaluated using the pyFAI software package  [24, 25]. The in-plane residual strain ε hkl and in-plane stress σ hkl for the ZrN and Ti 0.
Acknowledgement The authors want to thank  Bernhard Sartory  and  Dr. Jaroslav Wosik  ( Materials Center Leoben ) for the SEM and EBSD investigations. The financial support by the  Austrian Federal Ministry for Digital and Economic Affairs  and the  National Foundation for Research, Technology and Development  is gratefully acknowledged. We acknowledge  Czech-NanoLab Research Infrastructure  supported by  MEYS CR  ( LM2018110 ).
Thermal analysis of the compounds-Thermogravimetric analysis (TGA) and Differential Scanning Calorimetry (DSC): Thermal data were collected on a METTLER TOLEDO TGA/DSC 1 STARe System apparatus under an atmosphere of 20% O 2 in Ar. A sample of 1 to 2 mg was placed in an aluminum crucible and heated from ambient temperature to 600 • C with a scan speed of 10 • C/min.
Study of selectivity The recognition behavior of sensor 5 was firstly studied by UV-vis spectroscopy in pure acetonitrile towards various nucleophilic compounds (hydrazine, ammonia, propylamine, diethylamine, triethylamine, pyrrole, aniline, and 2-mercaptoethanol). The UV spectrum of sensor 5 presents a maximum absorption at 373 nm, which is similar to previously published data 24 recorded in an ACN-DMSO (3%) mixture and possesses a molar extinction coefficient of 19,303 L. mol -1. cm -1 (Figure  S6 ). This value should allow the observation of reasonably low concentration of analytes  [28]. As expected from the NMR experiments, the addition of 100 equivalents of hydrazine to a solution of sensor 5 at 10 µM in ACN led to a decrease in absorbance at 373 nm (Fig. 2 ) with the emergence of an isosbestic point at 298 nm.
Acknowledgements The authors are grateful to  DST-SERB, New-Delhi  for the financial support (Project File no. ECR/2017/000821 ), and  Jamia Millia Islamia  for providing the wonderful infrastructure. I. A. R also thanks  CSIR, New-Delhi  for the  SRF fellowship award. Continuous financial support from the  University of Littoral Côte d'Opale  is also warmly acknowledged (P. -E. D & A. K).
■ ACKNOWLEDGMENTS M. B. would like to thank the  ETH Zurich Grant program  ( ETH-44 18-1 ). M. D. K. thanks the  ETH Zurich Postdoctoral Fellowships program  ( FEL-23 19-1 ). Z. J. B. acknowledges financial support from ETH+ Project SynMatLab as well as an  ETH Career Seed Award. This work was partially funded by  ERC-CoG-MULTIPROSMM  (grant number  725184 ). The authors thank  Dr. Sarah Overall  and  Professor Alexander Barnes  for access to the 20. 0 T (850 MHz) NMR spectrometer.
Theoretical background 2. 1. Ab initio molecular dynamics simulations In order to make sure that the description of the interactions in the metals is accurate whichever their density and temperature, ab initio molecular dynamics simulations were performed with Vienna Ab initio Simulation Package (VASP)  [15]. We simulated an assembly of 𝑁 = 300 atoms for sodium and of 𝑁 = 256 atoms for aluminum. 7 electrons per atom were taken into account for the description of the electronic structure in the case of sodium, and only 3 electrons in the case of aluminum since the pressure ranges and so the core-overlaps were different. The volume 𝑉 = 𝐿 3 of the cubic box of side 𝐿 was chosen to reproduce the desired density and the temperature 𝑇 controlled using Nosé thermostat. The simulations were, therefore, carried out in the NVT ensemble rather than in NPT since the size of the simulation box governs the obtainable 𝑞-values which are of importance for the functions considered.
Acknowledgements This work was financially supported by the  Centre National de Recherche Scientifique (CNRS). The authors are thankful to  Dr. Jaafar Ghanbaja  for the TEM characterizations within the  CC3M/IJL  ( UMR 7198 ,  Univ Lorraine -CNRS ). F. Q. is thankful to the  SMI facility of LCPME  ( UMR 7564 ,  Univ. Lorraine -CNRS)  where the FTIR-ATR spectra were recorded. The authors relied on the  CC X Gamma/IJL  for achieving the XRD diffraction measurements.
Summary/Conclusions Biaxial tensile straining with in situ XRD measurements was performed on unique Al/Al 2 O 3 multilayers (PVD/ALD) and single layer Al reference films (PVD) deposited on flexible polymer substrates. Ultrathin Al 2 O 3 layers (1 ALD cycle -2. 4 nm -9. 4 nm) are used to interrupt the out of plane grain growth of Al, generating an Al sublayer structure with columnar grains and a grain size well below the total film thickness. It was determined that the oxide layers decrease the yield strength and mechanically weaken the Al sublayers in biaxial tension, in contrast to strengthening effects observed during uniaxial polymer-supported loading. Biaxial XRD film stress and FWHM data are in good agreement with postmortem SEM/FIB imaging regarding deformation of Al sublayers as a function of Al 2 O 3 layer thickness, while finite element simulations highlight the importance of internal layer roughness.
Acknowledgements SOLEIL is gratefully acknowledged for the beam time allocation (Proposal  20190179  and  20220220 ,  DIFFABS  beamline). B. P. and T. E. J. E. would like to acknowledge support from the  EMPAPOSTDOCS-II program , which received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement number  754364. T. E. J. E. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 840222. P-O. R. received support from the  French Government Program  '' Investissements d'Avenir"  ( LABEX INTERACTIFS ,  ANR-11-LABX-0017-01  and,  EUR INTREE ,  ANR-18-EURE-0010 ). Further support was provided by the  Austrian Research Promotion Agency (FFG , grant number  857043 ).
Acknowledgments This work was conducted under the framework of HAICoPAS, a  PSPC  project (projet de recherche et développement structurant pour la compétitivité) and of the  Industrial Chair Arkema (Arkema/CNRS-ENSAM-Cnam). BPI France  is acknowledged for funding the PhD work of  R. Arquier  (project number:  PSPC. AAP-7. 0_HAICoPAS ). The authors thank  Arkema ,  Hexcel , and more specifically  N. Cadorin ,  M. Glotin , H. -.
ACKNOWLEDGMENTS N. K. acknowledges SERB:  CRG  (Grant No. CRG/2021/001506 ),  UGC :  Special Assistance Program  (SAP; Grant Nos. F. 530/12/DRS/2009  and  F. 530/13/DRS II/2016 ),  UGC: Scheme for Promotion of Academic and Research Collaboration (SPARC ; Grant Nos. P930 ,  P1400 ,  P1429 , and  P1460 ), DST:  Nano Mission  [Grant No. SR/NM/NS-1420-2014 (C)],  DST: Fund for Improvement of S&T Infrastructure (FIST ; Grant No. SR/FST/P SI-143/2009 ), DAE:  UGC-Consortium for Scientific Research (CSR ; Grant No. UGC-DAE-CSR-KC/CRS/19/RC08/0983/1018 ),  DAE-Board of Research in Nuclear Sciences (BRNS ; Grant No. 39/29/2015-BRNS/39009 ), and  DST Promotion of University Research and Scientific Excellence [PURSE ; Grant No. SR/S9/Z-23/2010/22 ( C, G)], Government of India  programs for providing the facilities for research and development in IIUCNN,  MGU.
Quantitative centimetric scale mapping Based on previous observations, experimental conditions were defined to perform centimetric scale mapping. The analysed surface was 113 x 98 mm², the acquisition steps were 100 µm in both directions (resulting in 1 109 511 analysis points) and the analysis time was 45 ms per spectra. The X-ray beam operated at 50 keV and 200 µA, and a 100 µm thick Al filter was used to reduce dead time to 21 %. Once the data was collected, the quantification method was applied and quantitative centimetric scale maps obtained. The quantified map for Mn is presented in figure  4. Mn is rejected into the liquid phase during solidification. Dendrite trunks and secondary arms can be distinguished on the quantified map. At the centimetric scale, the structures do not appear distorted confirming that our developed analysis protocol is suitable.
Since the behavior of materials is highly nonlinear at high temperatures and depends on many factors, which are also nonlinear, the evaluation of the flow stress behavior by an analytical model whose parameters are identified by a classical regression method is limited. Faced with these limitations, ANN models are of major interest because they are particularly suited to dealing with complex and nonlinear relationships. Consequently, ANNs have been successfully applied to predict the flow stress behavior of materials under hot working conditions  [40] [41] [42] [43]. Although ANN models can predict well the material flow behavior, there is a problem with their implementation in finite element software, as reported by Pantalé et al. [16, 44]. In fact, the implementation of a constitutive model in an FEA code requires the derivatives of the model with respect to the strain, strain rate, and temperature.
Compression Tests' Results The set of flow stress σ y versus strain ε curves obtained from compression tests performed on the Gleeble-3800 simulator for each test condition (6 strain rates and 5 temperatures) is presented in Figure  4. All data curves contain 700 equidistant strain values up to ε = 0. 7. Therefore, there are 6 strain rates and 5 temperatures, and the database consists of 21,000 data points. For the identification of the parameters of all the analytical models presented in this article, we restricted the database to 35 strain values between 0. 02 and 0. 7, with a step of 0. 02 (this is illustrated in Figure  4 , where the data used correspond to the dots on the graphs). The overall behavior of these curves shows that the flow stress σ y increases with increasing strain rate.
The Modified-Zerilli-Armstrong Model The MZA model, which is the modified form of the ZA model, like the JC model presented earlier, is one of the most-widely used models implemented in many FEA codes such as the Abaqus software. The difference between the MZA model and JC model is related to the consideration of the three physical parameters to describe the reality observed from experiments. In the JC model, the parameters are considered separately, while in the MZA model, they are considered simultaneously, and for that formulation, the MZA model is preferred over the JC model  [47]. However, the original form of the ZA model has some limitations due to the fact that it is considered as a two-term function (thermal and athermal functions), and to improve the formulation, Samantaray et al.
ε. ε 0 ( 14 ) where A i , B j , C l k are the parameters (Table  6 ) of the model to be identified using the procedure proposed in  [39]. Quantities q, r, s, and t define the degree of the polynomials used to describe the behavior of the material. The larger these quantities, the greater the number of parameters that need to be identified for the PTM model. The determination of the parameters of this model was performed thanks to the LMFIT Python library  [51]  (for more details about this model, we refer the reader to our previous work  [39] ). Thus, all the parameters of this model were calculated with q = 5, r = 5, s = 1, and t = 5. ε = 0. 01 s -1 and. ε = 0. 1 s -1 , the prediction is not very good. The deviation between the predicted values and the experimental values for other strain rates is relatively low.
From a global architecture point of view, the input layer is composed of 3 neurons (ε p ,. ε, T) and the output layer is composed of a single neuron corresponding to the σ y flow stress. This architecture leads to a global model with 180 parameters to be identified (60 for the first layer, 112 for the second layer, and 8 for the output layer). The Python program used for training the neural network was developed using the specialized Python library, Tensorflow  [54]. The Adaptive Moment Estimation (ADAM) optimizer  [55]  was used for the training phase. The training data were those from the tests presented in Section 2. 2 and were composed of 21,000 quadruplets of (ε p ,. ε, T, σ y ) values. The training was performed on the basis of 5000 epochs of the experimental dataset. It took 40 min of training on a Dell XPS-13 7390 laptop running Ubuntu 22.
Interpolation Validation For interpolation validation, the chosen omitted strain rate was. ε = 1 s -1 , and those used for identification (or training for the ANN) were the five others, i. e. ,. ε = [0. 001, 0. 01, 0. 1, 2, 5] s -1. All models were re-identified from the same experimental data on a dataset corresponding to five strain rates and five temperatures. Figure  14  shows a comparison, for the strain rate. ε = 1 s -1 , of the flow stresses σ y calculated by the models (as a line) and the experimental results (as dots) for the five analytical models and the neural network. Table  8  shows the E MAR and E RMS deviations between the different models and the experimental data calculated either for the five identified strain rates (lines referred as id. ε), the six strain rates (lines referred as all.
For data extrapolation, the HS, PTM, and AR models again correlated well with the experiment, but the ANN model once again performed better globally. Identifying the parameters of an ANN model from experimental data requires more time than identifying the parameters of analytical models (about 40 minutes on a working laptop), but as shown by Pantalé et al. [16, 44] , implementing an ANN model in a computational code is straightforward. w 1 =                            2.                            The biases of the first hidden layer -→ b 1 is a 15-component vector: -→ b 1 =                            5.                            The weight matrix for the second hidden layer w 2 is a 7 × 15 matrix: w T 2 =                            -0                            The biases of the second hidden layer -→ b 2 is a seven-component vector: -→ b 2 =           0.
Acknowledgments: The authors acknowledge  Jean-Benoit Morin , Director of Metallurgy and Quality from  Finkl Steel-Sorel ,  Abdelhalim Loucif  from the  R&D department of Finkl Steel-Sorel, Ecole de Technologie Superieure , and  Ecole Nationale d'Ingenieurs de Tarbes, France , for providing technical data, materials, and testing facilities.
1 mm with samples tilted at an angle of 541 from normal. The FIB energy was set to 30 keV. Two different FIB tomography protocols were adopted: one suitable for room temperature analysis and a second one adapted to cryo-experiments: 1. At room temperature, the workflow implemented using the ZEISS ATLAS software was used for the acquisition of the 3D data. The ROI for FIB milling was protected by the deposition of a Pt layer with a thickness of 1 mm along the Z direction (the direction of the ion beam) and a 15 mm Â 15 mm XY area. The FIB-induced deposition was performed using an ion beam current of 0. 7 nA and lasted for about 30 min. In order to obtain a clean deposition, the protective Pt layer was deposited on regions that exhibited low surface roughness. Following the typical tracking protocol provided by the manufacturer, tracking marks for the calibration of FIB serial sectioning were created on top of the Pt protective layer by milling five lines (three on the middle parallel to the X direction and two inclined lines at each side) using a low FIB current of 50 pA.