No Arabic abstract
The DFT-1/2 method in density functional theory [L. G. Ferreira et al., Phys. Rev. B 78, 125116 (2008)] aims to provide accurate band gaps at the computational cost of semilocal calculations. The method has shown promise in a large number of cases, however some of its limitations or ambiguities on how to apply it to covalent semiconductors have been pointed out recently [K.-H. Xue et al., Comput. Mater. Science 153, 493 (2018)]. In this work, we investigate in detail some of the problems of the DFT-1/2 method with a focus on two classes of materials: covalently bonded semiconductors and transition-metal oxides. We argue for caution in the application of DFT-1/2 to these materials, and the condition to get an improved band gap is a spatial separation of the orbitals at the valence band maximum and conduction band minimum.
Using the strongly constrained and appropriately normed (SCAN) and SCAN+U approximations for describing electron exchange-correlation (XC) within density functional theory, we investigate the oxidation energetics, lattice constants, and electronic structure of binary Ce-, Mn-, and Fe-oxides, which are crucial ingredients for generating renewable fuels using two-step, oxide-based, solar thermochemical reactors. Unlike other common XC functionals, we find that SCAN does not over-bind the O2 molecule, based on direct calculations of its bond energy and robust agreement between calculated formation enthalpies of main group oxides versus experiments. However, in the case of transition-metal oxides (TMOs), SCAN systematically overestimates (i.e., yields too negative) oxidation enthalpies due to remaining self-interaction errors in the description of their ground-state electronic structure. Adding a Hubbard U term to the transition-metal centers, where the magnitude of U is determined from experimental oxidation enthalpies, significantly improves the qualitative agreement and marginally improves the quantitative agreement of SCAN+U-calculated electronic structure and lattice parameters, respectively, with experiments. Importantly, SCAN predicts the wrong ground-state structure for a few oxides, namely, Ce2O3, Mn2O3, and Fe3O4, while SCAN+U predicts the right polymorph for all systems considered in this work. Hence, the SCAN+U framework, with an appropriately determined U, will be required to accurately describe ground-state properties and yield qualitatively consistent electronic properties for most transition- metal and rare earth oxides.
The outstanding optoelectronics and photovoltaic properties of metal halide perovskites, including high carrier motilities, low carrier recombination rates, and the tunable spectral absorption range are attributed to the unique electronic properties of these materials. While DFT provides reliable structures and stabilities of perovskites, it performs poorly in electronic structure prediction. The relativistic GW approximation has been demonstrated to be able to capture electronic structure accurately, but at an extremely high computational cost. Here we report efficient and accurate band gap calculations of halide metal perovskites by using the approximate quasiparticle DFT-1/2 method. Using AMX3 (A = CH3NH3, CH2NHCH2, Cs; M = Pb, Sn, X=I, Br, Cl) as demonstration, the influence of the crystal structure (cubic, tetragonal or orthorhombic), variation of ions (different A, M and X) and relativistic effects on the electronic structure are systematically studied and compared with experimental results. Our results show that the DFT-1/2 method yields accurate band gaps with the precision of the GW method with no more computational cost than standard DFT. This opens the possibility of accurate electronic structure prediction of sophisticated halide perovskite structures and new materials design for lead-free materials.
During the last decade, ab initio methods to calculate electronic structure of materials based on hybrid functionals are increasingly becoming widely popular. In this Letter, we show that, in the case of small gap transition metal oxides, such as VO2, with rather subtle physics in the vicinity of the Fermi-surface, such hybrid functional schemes without the inclusion of expensive fully self-consistent GW corrections fail to yield this physics and incorrectly describe the features of the wave function of states near the Fermi-surface. While a fully self-consistent GW on top of hybrid functional approach does correct these wave functions as expected, and is found to be in general agreement with the results of a fully self-consistent GW approach based on semilocal functionals, it is much more computationally demanding as compared to the latter approach for the benefit of essentially the same results.
The electronic structure in alkaline earth AeO (Ae = Be, Mg, Ca, Sr, Ba) and post-transition metal oxides MeO (Me = Zn, Cd, Hg) is probed with oxygen K-edge X-ray absorption and emission spectroscopy. The experimental data is compared with density functional theory electronic structure calculations. We use our experimental spectra of the oxygen K-edge to estimate the bandgaps of these materials, and compare our results to the range of values available in the literature.
The discovery of intrinsic magnetic topological order in $rm MnBi_2Te_4$ has invigorated the search for materials with coexisting magnetic and topological phases. These multi-order quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute over 27,000 unique magnetic orderings for over 3,000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.