RESUMEN
Epitaxial growth of WTe2 offers significant advantages, including the production of high-quality films, possible long-range in-plane ordering, and precise control over layer thicknesses. However, the mean island size of WTe2 grown by molecular beam epitaxy (MBE) in the literature is only a few tens of nanometers, which is not suitable for the implementation of devices at large lateral scales. Here we report the growth of Td -WTe2 ultrathin films by MBE on monolayer (ML) graphene, reaching a mean flake size of ≃110 nm, which is, on overage, more than three times larger than previous results. WTe2 films thicker than 5 nm have been successfully synthesized and exhibit the expected Td phase atomic structure. We rationalize the epitaxial growth of Td-WTe2 and propose a simple model to estimate the mean flake size as a function of growth parameters that can be applied to other transition metal dichalcogenides (TMDCs). Based on nucleation theory and the Kolmogorov-Johnson-Meh-Avrami (KJMA) equation, our analytical model supports experimental data showing a critical coverage of 0.13 ML above which WTe2 nucleation becomes negligible. The quality of monolayer WTe2 films is demonstrated by electronic band structure analysis using angle-resolved photoemission spectroscopy (ARPES), which is in agreement with first-principles calculations performed on free-standing WTe2 and previous reports. We found electron pockets at the Fermi level, indicating a n-type doping of WTe2 with an electron density of n = 2.0 ± 0.5 × 1012 cm-2 for each electron pocket.
RESUMEN
We build new material descriptors to predict the band gap and the work function of 2D materials by tree-based machine-learning models. The descriptor's construction is based on vectorizing property matrices and on empirical property function, leading to mixing features that require low-resource computations. Combined with database-based features, the mixing features significantly improve the training and prediction of the models. We find R[Formula: see text] greater than 0.9 and mean absolute errors (MAE) smaller than 0.23 eV both for the training and prediction. The highest R[Formula: see text] of 0.95, 0.98 and the smallest MAE of 0.16 eV and 0.10 eV were obtained by using extreme gradient boosting for the bandgap and work-function predictions, respectively. These metrics were greatly improved as compared to those of database features-based predictions. We also find that the hybrid features slightly reduce the overfitting despite a small scale of the dataset. The relevance of the descriptor-based method was assessed by predicting and comparing the electronic properties of several 2D materials belonging to new classes (oxides, nitrides, carbides) with those of conventional computations. Our work provides a guideline to efficiently engineer descriptors by using vectorized property matrices and hybrid features for predicting 2D materials properties via ensemble models.