504 publications from this institution
First-principles calculations of the band offsets between Cu(2)ZnSnS(4) (CZTS) and XS (X = Cd, Zn) are performed. While the interface dipole contribution for the band offsets is calculated using the Perdew-Burke-Ernzerhof functional, the Heyd-Scuseria-Ernzerhof hybrid functional is employed to introduce the quasiparticle corrections to the band offsets. The calculated conduction band offset between CZTS and CdS is 0.2 eV, validating CdS for the buffer layer of the CZTS solar cell. The small conduction band offset stems from the band gap narrowing of CdS under the interface strain caused by the lattice misfit with CZTS. A large valence band offset over 0.9 eV between CZTS and ZnS indicates that precipitated ZnS is regarded as an inactive insulator phase in CZTS absorbers.
We present large-scale simulations of photovoltaics materials in the Vienna Ab initio Simulation Package [1] (VASP) that are only possible by pushing the boundaries of practically solvable eigenproblems. Thus, we enable analyses for semiconductors that were inaccessible within state-of-the-art predictive methods before. To achieve this, we a) implemented a distributed eigensolver in cuSOLVERMp that fully runs on the GPU, b) adapted VASP's Bethe-Salpeter Equation (BSE) algorithm for GPUs and to employ cuSOLVERMp, and c) dramatically improved the BSE workload distribution to yield near perfect load-balancing at scale. With a size of two million, we solve one of the largest dense, complex eigenproblems reported so far in under 5 hours sustaining 7.8 PFLOPS using 34.5 GJ on 4096 GPUs of NVIDIA's Selene supercomputer. Our results facilitate new breakthroughs in material science. These improvements in compute and energy efficiency apply to other domains relying on solving large eigenproblems, as well.
Electronic correlation energies from the random-phase approximation converge slowly with respect to the plane wave basis set size. We study the conditions under which a short-range local density functional can be used to account for the basis set incompleteness error. Furthermore, we propose a one-shot extrapolation scheme based on the Lindhard response function of the homogeneous electron gas. The different basis set correction methods are used to calculate equilibrium lattice constants for prototypical solids of different bonding types.
For ab initio electronic structure calculations, the random-phase approximation to the correlation energy is supposed to be a suitable complement to the exact exchange energy. We show that lattice constants, atomization energies of solids, and adsorption energies on metal surfaces evaluated using this approximation are in very good agreement with experiment. Since the method is fairly efficient and handles ionic, metallic, and van der Waals bonded systems equally well, it is a very promising choice to improve upon density functional theory calculations, without resorting to more demanding diffusion Monte Carlo or quantum chemical methods.
We present a low-complexity algorithm to calculate the correlation energy of periodic systems in second-order M\o ller-Plesset perturbation theory (MP2). In contrast to previous approximation-free MP2 codes, our implementation possesses a quartic scaling, $\mathcal O (N^4)$, with respect to the system size $N$ and offers an almost ideal parallelization efficiency. The general issue that the correlation energy converges slowly with the number of basis functions is eased by an internal basis set extrapolation. The key concept to reduce the scaling is to eliminate all summations over virtual orbitals which can be elegantly achieved in the Laplace transformed MP2 (LTMP2) formulation using plane wave basis sets and Fast Fourier transforms. Analogously, this approach could allow to calculate second order screened exchange (SOSEX) as well as particle-hole ladder diagrams with a similar low complexity. Hence, the presented method can be considered as a step towards systematically improved correlation energies.
We present an embedding approach to treat local electron correlation effects in periodic environments. In a single consistent framework, our plane wave based scheme embeds a local high-level correlation calculation [here, Coupled Cluster (CC) theory], employing localized orbitals, into a low-level correlation calculation [here, the direct Random Phase Approximation (RPA)]. This choice allows for an accurate and efficient treatment of long-range dispersion effects. Accelerated convergence with respect to the local fragment size can be observed if the low-level and high-level long-range dispersions are quantitatively similar, as is the case for CC in RPA. To demonstrate the capabilities of the introduced embedding approach, we calculate adsorption energies of molecules on a surface and in a chabazite crystal cage, as well as the formation energy of a lattice impurity in a solid at the level of highly accurate many-electron perturbation theories. The absorption energy of a methane molecule in a zeolite chabazite is converged with an error well below 20 meV at the CC level. As our largest periodic benchmark system, we apply our scheme to the adsorption of a water molecule on titania in a supercell containing more than 1000 electrons.
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We develop a strategy that integrates machine learning and first-principles calculations to achieve technically accurate predictions of infrared spectra. In particular, the methodology allows one to predict infrared spectra for complex systems at finite temperatures. The method’s effectiveness is demonstrated in challenging scenarios, such as the analysis of water and the organic–inorganic halide perovskite MAPbI3, where our results consistently align with experimental data. A distinctive feature of the methodology is the incorporation of derivative learning, which proves indispensable for obtaining accurate polarization data in bulk materials and facilitates the training of a machine learning surrogate model of the polarization adapted to rotational and translational symmetries. We achieve polarization prediction accuracies of about 1% for the water dimer by training only on the predicted Born effective charges.
