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.

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We carry out molecular statics simulations of the indentation of bare and graphene-covered Pt (111) surfaces with smooth and rough indenters of radius 1.5 to 10 nm. Our simulations show that the plastic yield of bare surfaces strongly depends on atomic-scale indenter roughness such as terraces or amorphous disorder. Covering surfaces with graphene regularizes this response to the results obtained for ideally smooth indenters. Our results suggest that graphene monolayers and other 2D materials mitigate the effect of roughness, which could be exploited to improve the fidelity of experiments that probe the mechanical properties of interfaces.