Classical molecular dynamics simulations using a reactive force field, which allows simulation of bond-breaking and bond-forming, are carried out to investigate the several stages of a catalysed synthesis process of single-wall carbon nanotubes. The simulations assume instantaneous catalysis of a precursor gas on the surface of metallic nanoclusters, illustrating how carbon atoms dissolve in the metal cluster and then precipitate on its surface, evolving into various carbon structures, finally forming a cap which eventually grows to a single-wall nanotube. The results are discussed in the context of experimental synthesis results.

Effective force fields for Ni-C interactions developed by Yamaguchi and Maruyama for the formation of metallofullerenes are modified to simulate the catalyzed growth of single-wall carbon nanotubes on Ni(n) clusters with n >20, and the reactive empirical bond order Brenner potential for C-C interactions is also revised to include the effect of the metal atoms on such interactions.

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasihar-monic contributions.

The crystalline structure of di-lithium phthalocyanine (Li2Pc) is analyzed via a sequence of theoretical methods starting with ab-initio optimizations of a single molecule and dimers, followed by a series of classical molecular dynamics simulations that emulate four alternative crystalline structures. Calculated X-ray spectra are compared with those from experiments, and the results suggest that the features correspond to a dominant β-phase, although similarities in the calculated spectrum of alternative phases may imply the possible existence of polymorphism in this material. Since Li2Pc has been proposed as a solid electrolyte for lithium-ion batteries, the existence of ion-conducting channels is examined through the analyses of the simulated structures. Dynamical properties such as the lithium-ionic diffusion coefficient are determined through the velocity autocorrelation function and compared to experimental values.