Screened exchange dynamical mean field theory and its relation to density functional theory: SrVO3 and SrTiO3

We present the first application of a recently proposed electronic-structure scheme to transition metal oxides: screened exchange dynamical mean-field theory includes non-local exchange beyond the local density approximation and dynamical correlations beyond standard dynamical mean-field theory. Our results for the spectral function of SrVO3 are in agreement with the available experimental data, including photoemission spectroscopy and thermodynamics. Finally, the 3d0 compound SrTiO3 serves as a test case to illustrate how the theory reduces to the band structure of standard electronic-structure techniques for weakly correlated compounds.Comment: 6 pages, 4 figure

Spectral properties of transition metal pnictides and chalcogenides: angle-resolved photoemission spectroscopy and dynamical mean field theory

Electronic Coulomb correlations lead to characteristic signatures in the spectroscopy of transition metal pnictides and chalcogenides: quasi-particle renormalizations, lifetime effects or incoherent badly metallic behavior above relatively low coherence temperatures are measures of many-body effects due to local Hubbard and Hund's couplings. We review and compare the results of angle-resolved photoemission spectroscopy experiments (ARPES) and of combined density functional dynamical mean field theory (DFT+DMFT) calculations. We emphasize the doping-dependence of the quasi-particle mass renormalization and coherence properties

High throughput thermal conductivity of high temperature solid phases: The case of oxide and fluoride perovskites

Using finite-temperature phonon calculations and machine-learning methods, we calculate the mechanical stability of about 400 semiconducting oxides and fluorides with cubic perovskite structures at 0 K, 300 K and 1000 K. We find 92 mechanically stable compounds at high temperatures -- including 36 not mentioned in the literature so far -- for which we calculate the thermal conductivity. We demonstrate that the thermal conductivity is generally smaller in fluorides than in oxides, largely due to a lower ionic charge, and describe simple structural descriptors that are correlated with its magnitude. Furthermore, we show that the thermal conductivities of most cubic perovskites decrease more slowly than the usual $T^{-1}$ behavior. Within this set, we also screen for materials exhibiting negative thermal expansion. Finally, we describe a strategy to accelerate the discovery of mechanically stable compounds at high temperatures.Comment: 9 pages, 6 figure

Self-regulated ligand-metal charge transfer upon lithium ion de-intercalation process from LiCoO2 to CoO2

Understanding the role of metal and oxygen in the redox process of layered 3d transition metal oxides is crucial to build high density and stable next generation Li-ion batteries. We combine hard X-ray photoelectron spectroscopy and ab-initio-based cluster model simulations to study the electronic structure of prototypical end-members LiCoO2 and CoO2. The role of cobalt and oxygen in the redox process is analyzed by optimizing the values of d-d electron repulsion and ligand-metal p-d charge transfer to the Co 2p spectra. We clarify the nature of oxidized cobalt ions by highlighting the transition from positive to negative ligand-to-metal charge transfer upon Li+ de-intercalation.Comment: Using X-ray photoelectron spectroscopy and ab-initio simulations, this study reveals the changes in electronic structure upon discharge for LixCoO2, an archetypal cathode material for lithium-ion batterie

Novel approaches to spectral properties of correlated electron materials: From generalized Kohn-Sham theory to screened exchange dynamical mean field theory

The most intriguing properties of emergent materials are typically consequences of highly correlated quantum states of their electronic degrees of freedom. Describing those materials from first principles remains a challenge for modern condensed matter theory. Here, we review, apply and discuss novel approaches to spectral properties of correlated electron materials, assessing current day predictive capabilities of electronic structure calculations. In particular, we focus on the recent Screened Exchange Dynamical Mean-Field Theory scheme and its relation to generalized Kohn-Sham theory. These concepts are illustrated on the transition metal pnictide BaCo$_2$As$_2$ and elemental zinc and cadmium.Comment: Accepted for publication in the Journal of the Physical Society of Japa

Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.Comment: 11 pages, 5 figures, 2 table

Two-step growth mechanism of the solid electrolyte interphase in argyrodyte/Li-metal contacts

The structure and growth of the Solid Electrolyte Interphase (SEI) region between an electrolyte and an electrode is one of the most fundamental, yet less-well understood phenomena in solid-state batteries. We present a parameter-free atomistic simulation of the SEI growth for one of the currently promising solid electrolytes (Li$_6$PS$_5$Cl), based on \textit{ab initio} trained machine learning (ML) interatomic potentials, for over 30,000 atoms during 10 ns, well-beyond the capabilities of conventional MD. This unveils a two-step growth mechanism: Li-argyrodite chemical reaction leading to the formation of an amorphous phase, followed by a kinetically slower crystallization of the reaction products into a 5Li$_2$SLi$_3$PLiCl solid solution. The simulation results support the recent, experimentally founded hypothesis of an indirect pathway of electrolyte reduction. These findings shed light on the intricate processes governing SEI evolution, providing a valuable foundation for the design and optimization of next-generation solid-state batteries