Nucleation mechanism for the direct graphite-to-diamond phase transition

Graphite and diamond have comparable free energies, yet forming diamond from graphite is far from easy. In the absence of a catalyst, pressures that are significantly higher than the equilibrium coexistence pressures are required to induce the graphite-to-diamond transition. Furthermore, the formation of the metastable hexagonal polymorph of diamond instead of the more stable cubic diamond is favored at lower temperatures. The concerted mechanism suggested in previous theoretical studies cannot explain these phenomena. Using an ab initio quality neural-network potential we performed a large-scale study of the graphite-to-diamond transition assuming that it occurs via nucleation. The nucleation mechanism accounts for the observed phenomenology and reveals its microscopic origins. We demonstrated that the large lattice distortions that accompany the formation of the diamond nuclei inhibit the phase transition at low pressure and direct it towards the hexagonal diamond phase at higher pressure. The nucleation mechanism proposed in this work is an important step towards a better understanding of structural transformations in a wide range of complex systems such as amorphous carbon and carbon nanomaterials

Regularized SCAN functional

We propose modifications to the functional form of the Strongly Constrained and Appropriately Normed (SCAN) density functional to eliminate numerical instabilities. This is necessary to allow reliable, automatic generation of pseudopotentials (including projector augmented-wave potentials). The regularized SCAN is designed to match the original form very closely, and we show that its performance remains comparable

Tin chemical shift anisotropy in tin dioxide: on ambiguity of CSA asymmetry derived from MAS spectra

Two different axial symmetries of theandnbsp;119Snandnbsp;chemical shiftandnbsp;anisotropyandnbsp;(CSA) inandnbsp;tin dioxideandnbsp;with theandnbsp;asymmetryandnbsp;parameter (andeta;) of 0 and 0.27 were reported previously based on the analysis of MASandnbsp;NMR spectra. By analyzing the static powder pattern, we show that theandnbsp;119Sn CSA is axially symmetric. A nearly axial symmetry and the principal axis system of theandnbsp;119Sn chemical shift tensor in SnO2andnbsp;were deduced from periodic scalar-relativistic density functional theory (DFT) calculations of NMR parameters. The implications of fast small-angle motions on CSA parameters were also considered, which could potentially lead to a CSA symmetry in disagreement with aandnbsp;crystal symmetry. Our analysis of experimental spectra usingandnbsp;spectral simulationsandnbsp;and iterative fittings showed that MAS spectra recorded at relatively high frequencies do not show sufficiently distinct features in order to distinguish CSAs with andeta;andnbsp;andasymp;andnbsp;0 and andeta;andnbsp;andasymp;andnbsp;0.4. The example of SnO2andnbsp;shows that both the MASandnbsp;lineshapeandnbsp;and spinning sideband analyses may overestimate the andeta; value by as much as andsim;0.3 and andsim;0.4, respectively. The results confirm that a static powder pattern must be analysed in order to improve the accuracy of the CSA asymmetry measurements. The measurements on SnO2andnbsp;nanoparticlesandnbsp;showed that the asymmetry parameter of theandnbsp;119Sn CSA increases for nm-sized particles with a larger surface area compared to andmu;m-sized particles. The increase of the andeta; value for tin atoms near the surface in SnO2andnbsp;was also confirmed by DFT calculations.</p

Analyzing the errors of DFT approximations for compressed water systems

We report an extensive study of the errors of density functional theory (DFT) approximations for compressed water systems. The approximations studied are based on the widely used PBE and BLYP exchange-correlation functionals, and we characterize their errors before and after correction for 1- and 2-body errors, the corrections being performed using the methods of Gaussian approximation potentials. The errors of the uncorrected and corrected approximations are investigated for two related types of water system: first, the compressed liquid at temperature 420 K and density 1.245 g/cm(3) where the experimental pressure is 15 kilobars; second, thermal samples of compressed water clusters from the trimer to the 27-mer. For the liquid, we report four first-principles molecular dynamics simulations, two generated with the uncorrected PBE and BLYP approximations and a further two with their 1- and 2-body corrected counterparts. The errors of the simulations are characterized by comparing with experimental data for the pressure, with neutron-diffraction data for the three radial distribution functions, and with quantum Monte Carlo (QMC) benchmarks for the energies of sets of configurations of the liquid in periodic boundary conditions. The DFT errors of the configuration samples of compressed water clusters are computed using QMC benchmarks. We find that the 2-body and beyond-2-body errors in the liquid are closely related to similar errors exhibited by the clusters. For both the liquid and the clusters, beyond-2-body errors of DFT make a substantial contribution to the overall errors, so that correction for 1- and 2-body errors does not suffice to give a satisfactory description. For BLYP, a recent representation of 3-body energies due to Medders, Babin, and Paesani [J. Chem. Theory Comput. 9, 1103 (2013)] gives a reasonably good way of correcting for beyond-2-body errors, after which the remaining errors are typically 0.5 mE(h) ≃ 15 meV/monomer for the liquid and the clusters

Machine learning for predictive condensed-phase simulation

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water