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Modelling the growth of carbon nanoclusters in shock experiments is computationally demanding. Here the authors employ a machine-learned reactive interatomic model to perform large-scale simulations of nanocarbon formation from prototypical shocked C/O-containing precursor.

Carbon nanomaterials have widespread application but fundamental aspects of their formation are still unclear. Here the authors explore the shock-induced synthesis of carbon nanoallotropes from liquid CO by time-resolved reflectivity and computations identifying the growth mechanism at the sub-nanosecond timescale

We detail the estimation of activation energies and quantum nuclear vibrational tunneling effects for hydrogen diffusion in PuO2 based on Density Functional Theory calculations and a quantum double well approximation. We find that results are relatively insensitive to choice of exchange correlation functional. In addition, the representation of spin in the system and use of an extended Hubbard U correction has only a small effect on hydrogen point defect formation energies when the PuO2 lattice is held fixed at the experimental density. We then compute approximate activation energies for transitions between hydrogen interstitial sites seeded by a semi-empirical quantum model and determine the quantum tunneling enhancement relative to classical kinetic rates. Our model indicates that diffusion rates in H/PuO2 systems could be enhanced by more than one order of magnitude at ambient conditions and that these effects persist at high temperature. The method we propose here can be used as a fast screening tool for assessing possible quantum nuclear vibrational effects in any number of condensed phase materials and surfaces, where hydrogen hopping tends to follow well defined minimum energy pathways.