Semiconducting phase of hafnium dioxide under high pressure: a theoretical studied by quasi-particle GW calculations

The phase stability of the hafnium dioxide compounds HfO2, a novelmaterial with a wide range of application due to its versatility and biocompatibility,is predicted to be achievable by using evolutionary technique, based on first-principlescalculations. Herein, the candidate structure of HfO2 is revealed to adopt a tetragonalstructure under high-pressure phase with P4/nmm space group. This evidentlyconfirms the stability of the HfO2 structures, since the decomposition into thecomponent elements under pressure does not occur until the pressure is at least200GPa. Moreover, phonon calculations can confirm that the P4/nmm structure isdynamically stable. The P4/nmm structure is mainly attributed to the semiconductingproperty within using the Perdew{Burke{Ernzerhof, the modified Becke-Johnsonexchange potential in combination with the generalized gradient approximations, andthe quasi-particle GW approximation, respectively. Our calculation manifests that theP4/nmm structure likely to be metal above 200GPa, arising particularly from GWapproximation. The remarkable results of this work provide more understanding ofthe high-pressure structure for designing metal-oxide-based semiconducting materials.

A machine learning platform for the discovery of materials

For photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

Machine Learning of Quasiparticle Energies in Molecules and Clusters

<div> <div> <div> <p>We present a ∆-Machine Learning approach for the prediction of GW quasiparticle energies (∆MLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive graph-based representations in kernel ridge regression based supervised learning. Coulomb matrix, Bag-of-Bonds, and Bonds-Angles-Torsions representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn–Sham orbital energies, which are both readily available from baseline calculations on the level of density-functional theory (DFT). We first illustrate the effects of different constructions of the orbital-sensitive representations (OSR) on the prediction of frontier orbital energies of 22K molecules of the QM8 dataset, and show that is is possible to predict the full photoelectron spectrum of molecules within the dataset using a single model with a mean-absolute error below 0.1eV. We further demonstrate that the OSR-based ∆MLQP captures the effects of intra- and intermolecular conformations in application to water monomers and dimers. Finally, we show that the approach can be embedded in multiscale simulation workflows, by studying the solvatochromic shifts of quasiparticle and electron-hole excitation energies of solvated acetone in a setup combining Molecular Dynamics, DFT, the GW approximation and the Bethe–Salpeter Equation. Our findings suggest that the ∆MLQP model allows to predict quasiparticle energies and photoelectron spectra of molecules and clusters with GW accuracy at DFT cost. </p> </div> </div> </div>