Our group works in the development and application of state-of-the-art ab-initio methods to systems of both fundamental and technological interest. Until 2014 the group was located at the Institut Lumière Matière, situated at the Université Claude Bernard Lyon 1, in Lyon, France. Since then we are at the Institut für Physik of the Martin-Luther-Universität Halle-Wittenberg. We are also members of the European Theoretical Spectroscopy Facility.

We are also sponsored by the following institutions:



Highlights (all highlights)

February 20, 2018

Local hybrid density functional for interfaces

Local hybrid density functional for interfaces Hybrid functionals in density functional theory are becoming the state-of-the-art for the calculation of electronic properties of solids. The key of their performance is the way in which an amount of Fock exchange is mixed with semi-local exchange-correlation functionals. We propose here a local mixing dependent on the density alone, extending the results of a previously reported functional [Phys. Rev. B 83, 035119 (2011)] to enable accurate calculations for interfaces and nanostructures. We verify that this hybrid functional has the potential to yield results of comparable quality as GW for band alignments and defects energy levels at interfaces, at the reduced cost of a hybrid density functional. This is possible as the form of the mixing is derived from GW theory, accounting for the electronic screening through its dependence on a density estimator of the local dielectric function. In contrast with other recent self-consistent schemes for the mixing parameter, our approach does not require to calculate the dielectric function and therefore it leads to a negligible increase of the computation time. This work has just been accepted in J. Chem. Theory Comput..

December 01, 2017

Libxc 4.0

Libxc We are happy two annouce the release of version 4.0 of libxc. This is a library of exchange-correlation functionals for density-functional theory. We are concerned with semi-local functionals (or the semi-local part of hybrid functionals), namely local-density approximations, generalized- gradient approximations, and meta-generalized-gradient approximations. Currently we include around 400 functionals for the exchange, correlation, and the kinetic energy, spanning more than 50 years of research. Moreover, libxc is by now included in more than 20 codes, not only from the atomic, molecular, and solid-state physics, but also from the quantum chemistry community. This work has just been published in Software X. The software can be downloaded here.

May 29, 2017

Predicting the stability of solids with machine learning

Predicting the stability of solids with machine learning We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamical stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems. This includes all possible perovskite and anti-perovskite crystals that can be generated with elements from hydrogen to bismuth, and neglecting rare gases and lanthanides. Incidentally, these calculations already reveal a large number of systems (around 500) that are thermodynamically stable, but that are not present in crystal structure databases. Moreover, some of these phases have unconventional compositions and define completely new families of perovskites. This data set is then used to train and test a series of machine learning algorithms to predict the energy distance to the convex hull of stability. In particular, we study the performance of ridge regression, random forests, extremely randomized trees (including adaptive boosting), and neural networks. We find that extremely randomized trees give the best results, achieving errors in the test set of around 120 meV/atom when trained in 20000 prediction accuracy is not uniform across the periodic table, being worse for first-row elements and elements forming magnetic compounds. Our results point to the fact that machine learning can be successfully used to guide high-throughput density functional theory calculations to speed up by at least a factor of 5 systematic searches of new materials, without any degradation of the accuracy. This work has just been accepted in Chemistry of Materials.

March 08, 2017

High-throughput search of ternary chalcogenides for p-type transparent electrodes

High-throughput search of ternary chalcogenides for p-type transparent electrodes Delafossite crystals are fascinating ternary oxides that have demonstrated transparent conductivity and ambipolar doping. We used a high-throughput approach based on density functional theory to find delafossite and related layered phases of composition ABX2, where A and B are elements of the periodic table, and X is a chalcogen (O, S, Se, and Te). From the 15624 compounds studied in the trigonal delafossite prototype structure, 285 are within 50 meV/atom from the convex hull of stability. These compounds were further investigated using global structural prediction methods to obtain their lowest-energy crystal structure. We find 79 systems not present in the materials project database that are thermodynamically stable and crystallize in the delafossite or in closely related structures. These novel phases were then characterized by calculating their band gaps and hole effective masses. This characterization unveils a large diversity of properties, ranging from normal metals, magnetic metals, and some candidate compounds for p-type transparent electrodes. This work has just been accepted for publication in Scientific Reports.

May 03, 2016

Prediction of a new topological crystalline insulator

Calculated Fermi surface and spin-texture of Bi-139 Topological crystalline insulators are a type of topological insulators whose topological surface states are protected by a crystal symmetry, thus the surface gap can be tuned by applying strain or an electric field. In this paper we predicted by means of ab initio calculations a new phase of Bi which is a topological crystalline insulator characterized by a mirror Chern number nM = −2, but not a Z2 strong topological insulator. This system presents an exceptional property: at the (001) surface its Dirac cones are pinned at the surface high-symmetry points. As a consequence they are also protected by time-reversal symmetry and can survive against weak disorder even if in-plane mirror symmetry is broken at the surface. Taking advantage of this dual protection, we presented a strategy to tune the band-gap based on a topological phase transition unique to this system. Since the spin-texture of these topological surface states reduces the back-scattering in carrier transport, this effective band-engineering is expected to be suitable for electronic and optoelectronic devices with reduced dissipation. This work has just been published in Scientific Reports.

May 03, 2016

Prediction and synthesis of a novel Be-doped Si clathrate

Predicted and synthesized Be-doped Si clathrate We used computational high-throughput techniques to study the thermodynamic stability of ternary type-I Si clathrates. Two strategies to stabilize the structures were investigated: through endohedral doping of the 2a and 6d Wyckoff positions (located at the center of the small and the large cages respectively), and by substituting the Si 6c positions. Our results agree with the overwhelming majority of experimental results, and predict a series of unknown clathrate phases. Many of the stable phases can be explained by the simple Zintl-Klemm rule, but some are unexpected. We then successfully synthesized one of the latter compounds, a new type-I silicon clathrate containing Ba (inside the cages) and Be (in the 6c position). These results prove the predictive power and reliability of our strategy, and motivate the use of high-throughput screening of materials properties for the accelerated discovery of new clathrate phases. This work has just been accepted for publication in Chemistry of Materials.