Tackling Disorder in γ-Ga2 O3

Ultra-Large Compressive Plasticity of E-Ga2O3 Thin Films at the Submicron Scale

Gallium oxide (Ga2O3) usually fractures in the brittle form, and achieving large plastic deformability to avoid catastrophic failure is in high demand. Here, ε-Ga2O3 thin films with columnar crystals and partial unoccupied Ga sites are synthesized, and it is demonstrated that the ε-Ga2O3 at the submicron scale can be compressed to an ultra-large plastic strain of 48.5% without cracking. The compressive behavior and related mechanisms are investigated by in situ transmission electron microscope nanomechanical testing combined with atomic-resolution characterizations. The serrated plastic flow and large strain burst are two major deformation forms of ε-Ga2O3 during compression, which are attributed to the dislocation nucleation and avalanches, formation of new grains, and amorphization. The ultra-large compressive plasticity of ε-Ga2O3 thin films at the submicron scale can inspire new applications of Ga2O3 in micro- or nano- electronic and optoelectronic devices, especially those that require impact resistance during processing or service.

Electron-vibrational renormalization in fullerenes through ab initio and machine learning methods

The effect of nuclear vibrations on the electronic eigenvalues and the HOMO-LUMO gap is known for several kinds of carbon-based materials, like diamond, diamondoids, carbon nanoclusters, carbon nanotubes and others, like hydrogen-terminated oligoynes and polyyne. However, it has not been widely analysed in another remarkable kind which presents both theoretical and technological interest: fullerenes. In this article we present the study of the HOMO, LUMO and gap renormalizations due to zero-point motion of a relatively large number (163) of fullerenes and fullerene derivatives. We have calculated this renormalization using density-functional theory with the frozen-phonon method, finding that it is non-negligible (above 0.1 eV) for systems with relevant technological applications in photovoltaics and that the strength of the renormalization increases with the size of the gap. In addition, we have applied machine learning methods for classification and regression of the renormalizations, finding that they can be approximately predicted using the output of a computationally cheap ground state calculation. Our conclusions are supported by recent research in other systems.