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Open clusters (OCs) are groups of stars formed from the same cloud of gas and cosmic dust. They play an important role in studies of star formation and evolution and our understanding of galaxy structure and dynamics. The main objective of this work is to identify stars that belong to OCs using astrometric data from Gaia EDR3 and spectroscopic data from APOGEE DR17. Furthermore, we investigate the metallicity gradients and orbital properties of the OCs in our sample. Methods. By applying the HDBSCAN clustering algorithm to these data, we identified observed stars in our galaxy with similar dynamics, chemical compositions, and ages. The orbits of the OCs were also calculated using the GravPot16 code. Results. We find 1987 stars that tentatively belong to 49 OCs; 941 of these stars have probabilities above 80% of belonging to OCs. Our metallicity gradient presents a two-slope shape for two measures of different Galactic center distances – the projected Galactocentric distance and the guiding center radius to the Galactic center – as already reported in previous work. However, when we separate the OCs by age, we observe no significant difference in the metallicity gradient slope beyond a certain distance from the Galactic center. Our results show a shallower gradient for clusters younger than 2 Gyr than those older than 2 Gyr. All our OCs dynamically assemble the disk-like population very well, and they are in prograde orbits, which is typical for disk-like populations. Some OCs resonate with the Galactic bar at the Lagrange points L4 and L5. 

Context. Barium (Ba) stars are characterised by an abundance of heavy elements made by the slow neutron capture process ( s -process). This peculiar observed signature is due to the mass transfer from a stellar companion, bound in a binary stellar system, to the Ba star observed today. The signature is created when the stellar companion is an asymptotic giant branch (AGB) star. Aims. We aim to analyse the abundance pattern of 169 Ba stars using machine learning techniques and the AGB final surface abundances predicted by the F RUITY and Monash stellar models. Methods. We developed machine learning algorithms that use the abundance pattern of Ba stars as input to classify the initial mass and metallicity of each Ba star’s companion star using stellar model predictions. We used two algorithms. The first exploits neural networks to recognise patterns, and the second is a nearest-neighbour algorithm that focuses on finding the AGB model that predicts the final surface abundances closest to the observed Ba star values. In the second algorithm, we included the error bars and observational uncertainties in order to find the best-fit model. The classification process was based on the abundances of Fe, Rb, Sr, Zr, Ru, Nd, Ce, Sm, and Eu. We selected these elements by systematically removing s -process elements from our AGB model abundance distributions and identifying the elements whose removal had the biggest positive effect on the classification. We excluded Nb, Y, Mo, and La. Our final classification combined the output of both algorithms to identify an initial mass and metallicity range for each Ba star companion. Results. With our analysis tools, we identified the main properties for 166 of the 169 Ba stars in the stellar sample. The classifications based on both stellar sets of AGB final abundances show similar distributions, with an average initial mass of M = 2.23 M ⊙ and 2.34 M ⊙ and an average [Fe/H] = −0.21 and −0.11, respectively. We investigated why the removal of Nb, Y, Mo, and La improves our classification and identified 43 stars for which the exclusion had the biggest effect. We found that these stars have statistically significant and different abundances for these elements compared to the other Ba stars in our sample. We discuss the possible reasons for these differences in the abundance patterns.