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The potential energy curves (PECs) for the homonuclear He–He, Ar–Ar, Cu–Cu, and Si–Si dimers, as well as heteronuclear Cu–He, Cu–Ar, Cu–Xe, Si–He, Si–Ar, and Si–Xe dimers, are obtained in quantum Monte Carlo (QMC) calculations. It is shown that the QMC method provides the PECs with an accuracy comparable with that of the state-of-the-art coupled cluster singles and doubles with perturbative triples corrections [CCSD(T)] calculations. The QMC data are approximated by the Morse long range (MLR) and (12-6) Lennard-Jones (LJ) potentials. The MLR and LJ potentials are used to calculate the deflection angles in binary collisions of corresponding atom pairs and transport coefficients of Cu and Si vapors and their mixtures with He, Ar, and Xe gases in the range of temperature from 100 K to 10 000 K. It is shown that the use of the LJ potentials introduces significant errors in the transport coefficients of high-temperature vapors and gas mixtures. The mixtures with heavy noble gases demonstrate anomalous behavior when the viscosity and thermal conductivity can be larger than that of the corresponding pure substances. In the mixtures with helium, the thermal diffusion factor is found to be unusually large. The calculated viscosity and diffusivity are used to determine parameters of the variable hard sphere and variable soft sphere molecular models as well as parameters of the power-law approximations for the transport coefficients. The results obtained in the present work include all information required for kinetic or continuum simulations of dilute Cu and Si vapors and their mixtures with He, Ar, and Xe gases. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.