dataesr/scientific-paragraphs-categorization huggingface dataset:
As a side note, before 1992-06-25T01:00:00 UTC the CGRO onboard clock was early by 2. 042144 seconds, and this time difference needs to be subtracted from the measured onboard time to get the true arrival time in UTC. In GammaLib, this subtraction is automatically performed when converting onboard times, given in truncated Julian days (TJDs) and tics, into GTime objects using the gammalib::com_time function, and the conversion is undone when using the inverse functions gammalib::com_tjd and gammalib::com_tics. After applying the clock correction, event times are converted using t SSB = t CGRO + ∆t CGRO→SSB -∆t Shapiro + ∆t UTC→TT + ∆t TT→TBD , (D. 1) where ∆t CGRO→SSB corrects for the light travel time from CGRO to the Solar System barycentre, ∆t Shapiro corrects for the gravitational time delay near the Sun, also known as Shapiro delay, ∆t UTC→TT converts from UTC to terrestrial time (TT), and ∆t TT→TBD converts from TT to the TDB time system.
We note that all time correction terms are themselves time dependent, yet we ignore this time dependence in Eq. (D. 1) to simplify the notation. All terms are given in units of seconds. The computation of the three terms ∆t CGRO→SSB -∆t Shapiro + ∆t TT→TBD is implemented by the GEphemerides::geo2ssb method, which regroups all time corrections that depend on planetary ephemerides. GammaLib deals with planetary ephemerides through the GEphemerides class, and the software includes the Jet Propulsion Laboratory (JPL) Development Ephemeris 200 including information about the Sun position, the Earth position and its first three time derivatives, and the time difference between TT and TDB in seconds on a daily basis between Julian day (JD) 2436913 (10 December 1959) and 2469807 (31 December 2049).
e (i)∆t 3 , (D. 6) where i is the index of the nearest entry in time in the JPL database, e(i), ė(i), ë(i), and. e (i) is the Earth position and its first three time derivatives for this entry, and ∆t = t CGRO -t(i) (D. 7) is the time difference between the event time and the nearest entry in the JPL database in units of days, which by definition is in the range -0. 5 and +0. 5. Since the Sun moves only a little around the Solar System barycentre, it is sufficient to take for the Sun vector the nearest entry in the JPL database, which is s = s(i). Finally, the conversion from the TT to the TDB time system at a given time t CGRO is computed using ∆t TT→TDB = ∆t TT→TDB (i) + (∆t TT→TDB (i + 1) -∆t TT→TDB (i))∆t, (D. 8) where ∆t TT→TDB (i) is the nearest entry in the JPL database. The last term in the time correction, ∆t UTC→TT , does not depend on planetary ephemerides and is given by ∆t UTC→TT = 32.
184 + n leap , (D. 9) where n leap is the number of leap seconds. The computation of ∆t UTC→TT is implemented by the GTime::utc2tt method. The HEASARC archive includes for most of the viewing periods files that provide for each superpacket the vectors e + r as well as the correction terms ∆t UTC→TT + ∆t TT→TBD , avoiding the need for planetary ephemerides. These so-called BVC data can be handled by GammaLib through the classes GCOMBvc and GCOMBvcs that manage individual data records as well as entire files. Specifically, the method GCOMBvcs::tdelta computes ∆t CGRO→SSB -∆t Shapiro + ∆t UTC→TT + ∆t TT→TBD , and if the observation that should be binned includes BVC information, GCOMDri::compute_dre will use this method instead of the algorithm described above for the barycentric time correction.
The format most relevant to COMPTEL is the psrtime format, which is an ASCII file format containing the radio pulsar database as maintained during the CGRO mission by the pulsar group at Princeton and nowadays by the Jodrell Bank Centre for Astrophysics. 12 Upon loading of a psrtime file, the GPulsar::load_psrtime method computes from the data the pulsar phase at the reference time using Φ 0 = ν∆t + 1 2 ν∆t 2 + 1 6 ν∆t 3 , (D. 12) where ∆t =(MJD 0 -|MJD 0 |) × 86400 + ∆t CGRO→SSB -∆t Shapiro + ∆t UTC→TT + ∆t TT→TBD (D. 13) and MJD 0 is the modified Julian day of the ephemeris reference (the GEphemerides::geo2ssb method is used for this computation, and hence the Shapiro delay in Eq. (D. 13) includes the displacement of the Sun around the Solar System barycentre, cf. Eq. (D. 3)).
GammaLib implements several options for handling data from D2 modules with failed PMTs, controlled through the GCOMSelection::fpmtflag method that takes an integer value of 0, 1, or 2. This integer value is a user parameter of the ctools scripts comobsbin and compulbin that, by default, is assumed to be 0. For fpmtflag = 0, events registered in D2 modules with failed PMTs are ignored, and the corresponding modules are also excluded in the computation of the geometry function (cf. Appendix F). Conversely, for fpmtflag = 1 the failure of the PMTs is ignored, and events from D2 modules are treated as if the failed PMTs were still operating. Finally, for fpmtflag = 2, circular exclusion regions are applied around the zones of the failed PMTs for dates after their failure, as defined in Table  E.
To avoid double counting, we disregarded the contribution of power consumption since it is already accounted for in the electricity to power the office building. For emission from lunch meals and home-to-office commuting we took the specific habits of the co-authors weighted by their contribution to the research into account. We note that our research work did not require any professional travelling. The footprint for a couple of video conferences that we organised for this research is included under the 'Data flow' source. A84, page 31 of 31.
Acknowledgements. We would like to thank the anonymous referee for the very careful reading of the manuscript and the many constructive suggestions. This research made use of ctools, a community-developed gamma-ray astronomy science analysis software (Knödlseder et al. 2016). ctools is based on GammaLib, a community-developed toolbox for the scientific analysis of astronomical gammaray data (Knödlseder et al. 2011). This work has made use of the Python 2D plotting library matplotlib (Hunter 2007). This research has made use of data and/or software provided by the  High Energy Astrophysics Science Archive Research Center (HEASARC) , which is a service of the  Astrophysics Science Division at NASA/GSFC. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research is part of the #LowCarbon-Science initiative that aims in reducing the carbon footprint of scientific research, and has benefitted from discussions held in the context of the GDR Labos 1point5 and the Astro4Earth initiative.
The use of machine learning for the prediction of physical and chemical properties of crystals based on their structure alone is currently an area of intense research in computational materials science. In this work, we studied the possibility of using machine learning-trained algorithms in order to calculate the thermal properties of siliceous zeolite frameworks. We used as training data the thermal properties of 120 zeolites, calculated at the DFT level, in the quasi-harmonic approximation. We compared the statistical accuracy of trained models (based on the gradient boosting regression technique) using different types of descriptors, including ad hoc geometrical features, topology, pore space, and general geometric descriptors. While geometric descriptors were found to perform best, we also identified limitations on the accuracy of the predictions, especially for a small group of materials with very highly negative thermal expansion coefficients.
e. , the nature of the atoms that make up the framework (and the extra-framework ions, when they are present). Computational prediction of zeolite physical and chemical properties is therefore a research topic of high interest, and a large body of work has been published on the calculation of properties such as fluid adsorption  3  and transport,  4  gas mixture separation,  5, 6  catalytic activity,  [7] [8] [9]  etc. Recently, a new trend has emerged in the computational prediction of properties, it is the large-scale computational screening of materials databases to identify promising candidates for specific applications. 10, 11 In particular, there is a body of research trying to identify materials featuring unusual (or counter-intuitive, or "abnormal") physical or chemical properties, also called metamaterials.
29 It has been applied to a wide diversity of problems, including prediction of potential energy surfaces and stability, 30 electronic properties, 31 magnetic properties, 32 mechanical properties  33, 34  and stability,  35  catalytic activity,  36  and the characterization of energetic materials,  37  to name only a few examples. We refer the reader to Refs. 28 and 25 for more comprehensive reviews of the principles and applications, respectively. In the field of zeolites, where a large number of hypothetical frameworks are known,  38  the applications of machine learning for the identification and characterization of frameworks has long been recognized. 39, 40 More recently, Helfrecht et al. have analysed the structural diversity of hypothetical zeolites databases, showing the power of smooth overlap of atomic position (SOAP) descriptors in order to classify local environments and identify zeolite building blocks.
These predictions have yet to be directly verified experimentally, due to the difficulty in systematic characterization of crystal elastic constants (or mechanical behavior under anisotropic stress), but in cases where data is available, the mechanical properties are usually found to be in good agreement with DFT values. 21, 49  this work, we used a previously calculated database of thermal properties of pure silica zeolites and investigated the feasibility of its use for machine learning purposes. In the next sections, we first introduce the computational methods, including a brief presentation of the databases we used, as well as the algorithm we chose and a definition of all the materials descriptors. We then assess the accuracy of different models trained on our DFT-calculated database, and compare the results using different types of materials descriptors.
DFT calculations The present work is based on zeolite properties data calculated at the quantum chemical level, previously obtained in the group. We used the data on thermal and mechanical properties of pure SiO 2 zeolites from these DFT calculations as a basis for the ML study reported herein. We refer the reader to Ref. 21 for a full methodological description, but provide here a short summary for the sake of convenience. The DFT calculations were done on 134 structures for which we optimized the geometry. Those calculations were performed starting from the IZA models for 190 zeolites with fewer than 150 atoms in their unit cell; from those 190, 134 structures achieved convergence within reasonable time constraints. For these structures, we calculated the thermal properties using the quasi-harmonic approximation.
Deem database To compare our results with predictions made on a much larger number of zeolitic structures, we used the PCOD2 database of hypothetical zeolites created by Deem and co-workers. 38, 50 e development of this database started in 1992 51 with 2,000 structures and continued its growth reaching around 600,000 structures in 2006,  52  which is the number of structures we studied here. The database, no longer accessible on its original academic website, was mirrored from our own local archives and made available at the following stable URL: https: //doi. org/10. 5281/zenodo. 4030232 These structures were obtained from combined Monte-Carlo simulations, simulated annealing and refinement using a classical force field, the Sanders-Leslie-Catlow (SLC) interatomic pair potential. 10% of the 2.
7 millions unique structures obtained in their work were found to have an energy per SiO 2 unit that is within 30 kJ/mol from the well-known αquartz dense phase. Many of these structures were expected to be stable and achievable through synthesis, and the authors emphasized that their database could drive the discovery and synthesis of novel materials as well as support identification of materials through powder pattern searching and matching. In addition, they also calculated the stiffness tensor of second-order elastic constants -with the same force field approach. As observed before however,  34  the results obtained on mechanical properties were not very accurate, as K values reported range from -27,000 (unstable) to 20,500 GPa (unphysically high). Nevertheless, this structural database of hypothetical zeolites has been widely used in the exploration and systematic prediction of structure/property relationships.
Machine learning In order to apply a machine learning strategy to the task at hand, we chose a gradient boosting regression (GBR)  55, 56  for the algorithm, as it has been proven to be quite robust and efficient for small datasets, like in our case. 57 This algorithm is a stage-wise additive model which trains decision trees that are built in a greedy fashion to minimize the loss function, which was chosen to be a least squares function in our case. We have used this methodology in the past for the prediction of physical properties in dense and porous frameworks, including mechanical stiffness,  34  and anisotropic elastic properties such as negative Poisson's ratio. We used the GBR implementation from the scikit-learn Python package. 58 We used a 3-fold cross-validation procedure as implemented in Sci-kit learn package which we repeated 50 times in order to obtain relevant accuracy scores and errors by averaging them over all the simulations.
Volumetric descriptors Because zeolites are nanoporous materials, another possible choice of structural descriptors would be focused on the characterization of their pore volume, and more generally, nonlocal or volumetric information. Indeed, as described earlier, the quasi-harmonic approximation we used to determine the thermal properties of zeolites is considering the harmonic expression of the Helmholtz free energy to which we added the vibrational part of the energy (which depends on the volume). Thus volumetric descriptors such as the density, the accessible volume or the surface area could also play an important role in the prediction through machine learning. These quantities were obtained from the optimized zeolite structures using the Zeo++ software package. 63, 64 The surface area, accessible volume and volume being dependent of the choice of unit cell, we normalized them per number of SiO 2 units.
The properties obtained by DFT calculations were obtained through a rather long and difficult process which comes with a certain degree of uncertainty. In order to perform a systematic study of thermal properties (to build the database), we had to fix a certain set of parameters for all the frameworks (ranges of temperature, volume expansion, number of points in numerical derivatives, etc): more fine-tuned parameters for each zeolite could have resulted in a better accuracy for the calculated properties, but was not feasible in a systematic approach. We note that a deviation of the same magnitude is also observed with other types of descriptors, for which representations can be found in Figure  S4. We note here that, in this study, our main interest is in evaluating the feasibility of the ML models and the comparison of descriptors, for a physical property (thermal expansion) that has never been studied in framework materials at that scale before.
Applying ML models to non-DFT optimised structures We have proven above that the prediction of thermal properties is possible, with reasonable accuracy, from the structure of a zeolitic framework optimized at the DFT level. While interesting in itself, the need for a DFT-optimized structure can strongly limit the applicability of the ML model in a high-throughput screening scenario. Indeed, while the structure optimization is computationally less intensive than the quasi-harmonic calculation of thermal properties (and can be performed in hundreds of structures  47  ), it is not scalable to the size of available hypothetical databases of zeolitic materials (which can contain hundreds of thousands of structures). Therefore we found interesting to investigate the performance of a ML model trained on non DFT-optimised structures to predict the same thermal and mechanical properties (still using for those properties the data obtained by DFT).
This is, in effect, a test of the sensitivity of the ML model to the accuracy of the geometries used as input. For this, we created a data set containing the same 120 structures as the DFT-optimised data set but using the zeolites structures from the IZA database (obtained by a distance least-squares refinement technique) and used them directly as a training set without optimising them. We used the same hyperparameters as well as the same combination of descriptors as the above section, as we proved that the results were slightly better than with just only one type of descriptor. All the features required were retrieved on the non-optimised structures following the same procedure described in the computational methods. We reported the cross-validated predictions along with the feature importance plot in supporting information.
Therefore, this validates the prediction of physical properties from structures optimised at a level lower than DFT (for example, force field optimised structures), making it possible to investigate very large-scale zeolitic data sets, such as the PCOD2 database created by Deem et al. 38, 50  Deem database We now apply our ML model to the PCOD2 database,  38, 50  which contains around 600,000 hypothetical pure silica zeolite structures, obtained from combining Monte-Carlo simulations, simulated annealing, and structure refinement using a classical force field (the Sanders-Leslie-Catlow (SLC) interatomic pair potential  71  ). It constitutes a great tool for the machine learning-based exploration of new synthesizable structures, or the identification of candidate zeolites with targeted properties.
For example, we have used it in the past to identify new frameworks with auxetic behavior. 47 In this work, our interest lies in comparing our data set of calculated zeolites with the full database of hypothetical structures and ultimately, trying to predict the distribution of thermal expansion and bulk modulus of the PCOD2 database. In order to do this, we first calculated different descriptors for the whole PCOD2 database. For around 600,000 structures, the computational effort is as follows (timing reported for nonparallel, single-CPU calculations): a couple of days for the bond distances and angles, one week for the SOAP features and around two weeks for the topological descriptors. Due to the size of some of the systems within this database, volumetric descriptors could not be retrieved as the time needed to calculate them was excessively long.
We reported histograms of values of Si-O distances and Si-O-Si angles on Figure  4  for both our training data set, and the entire PCOD2 database -with occurrences normalised for the sake of clarity. First, we can see that the span of values for both distances and angles is close between IZA structures All these observations are in contrast with the case of the principal components of SOAP features. Indeed one can see that for the first three principal components there is a shift in the distribution of IZA structures compared to the PCOD2 database. This would mean that the frameworks we are currently working on do not represent all of the possible environments present in the database of theoretical structures: the geometrical diversity of the PCOD2 database is higher. This could also mean that some of the structures in the PCOD2 database are outside of the realm of "feasible zeolite structures", as defined by the convex hull of features from the experimentally known frameworks (in the IZA database).
This is an interesting new take on the question of experimental feasibility of frameworks,  42, 49  which we intend to pursue further, but is outside of the scope of our current work. We can only conclude that a prediction on the PCOD2 database using the principal components of SOAP features as descriptors would probably result in a poor prediction. Therefore, we decided instead to use the ad hoc geometrical descriptors and train a model to try to predict the properties of the database of theoretical zeolite structures. We used the 20 same set of statistic metrics on bond and angles as described earlier in this paper and used them to predict the thermal expansion and bulk modulus on the full PCOD2 database. Results are shown in Figure  5  -where they are plotted as a heat map, with the harmonic mean of Si-O-Si angle, using a gradient of color as a third dimension.
We can observe some general trends on the PCOD2 structures, with a statistical correlation between thermal expansion and bulk modulus: structures with a higher bulk modulus are often found to correspond to larger (negative) values of negative thermal expansion (NTE). We can actually see a hint of this behavior, already, in our training data set with properties computed at the DFT level (Figure  S3 ). We find that both physical properties are apparently linked to the same geometrical feature, namely, the Si-O-Si angle -which we found previously to be the most important feature when training ML models. Materials with higher values of Si-O-Si angles have both higher bulk modulus, and more pronounced NTE. This correlation between mechanical and thermal behavior through a relatively simple geometric feature is an interesting new development, and would have to be confirmed -for example, through systematic calculations of representative structures within the PCOD2 database.
Conclusions In this work, we used our previously calculated database to train a series of machine learning models for the prediction of thermal properties of zeolites: including thermal expansion, as well as pressure and temperature dependence of bulk moduli. After identifying the optimal hyperparameters for our gradient boosting regression models, we compared the accuracy of models built on different types of structure descriptors: From these comparisons, we gained insight into the structure-property relationships in zeolite frameworks. With regards to geometric descriptors, we saw that the description of angles is much more important than distances for the zeolite frameworks as angles were found to be the most important parameter when training the model. We also found that "agnostic" geometric descriptors such as SOAP outperform the ad hoc descriptors that we had identified, with lower root-mean-squared and mean-absolute errors.
Moreover, even with SOAP descriptors, the most important component was found to be directly related to Si-O-Si angles, confirming our initial analysis. These conclusions are true regardless of the specific thermal property under study (i. e. n the thermal expansion, the bulk modulus or its derivatives), but we hypothesize that this would apply more broadly for a large scope of physical properties of zeolite frameworks. Finally, we applied our predictive models to the PCOD2 database of hypothetical structures of zeolites, to confront our data set of DFT-calculated structures with a much larger set. Comparing the descriptors for both data set showed that the distributions of Si-O distances and Si-O-Si angles are within the same range, which hints that our database is well suited to predict properties of zeolites using the geometrical descriptors.