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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.

The performances of lithium-ion batteries are set by the electrodes materials capacity to exchange lithium ions and electrons faster and reversibly. To this goal Ni-rich layered metal oxides, especially LiNiO2, are attractive electrode candidate to achieve both high voltage and capacities. Despite its attractiveness, several drawbacks for its industrialization are related to different form of surface and bulk instabilities. These instabilities are due to redox process involving the charge transfer between cations and anions. Therefore, a fundamental understanding based on further experimental evidence is required to resolve of charge transfer between the cation and anion from the surface to the bulk in LiNiO2. Herein, we resolve the role of nickel and oxygen in the charge compensation process in LixNiO2 electrodes from the extreme surface down to 30 nm by energy-dependent core-level HAXPES supported by ab initio simulation. We emphasize the central role of oxygen in the bulk charge compensation mechanism from LiNiO2 to NiO2 due to the negative charge transfer and bond/charge-disproportionation characters of LiNiO2. This bulk behavior is in turn responsible for surface deoxygenation and nickel reduction upon delithiation.

The question of if silica nanoparticles can enhance the ionic conductivity of a polymer electrolyte above its crystallization temperature has remained unclear for the two decades following the first experiments on these systems. We use Molecular Dynamics simulations to decipher the atomic scale mechanisms affecting the properties of LiTFSI-poly(ethylene oxide) electrolytes upon the addition of silica nanoparticles. At any ionic concentration, adding nanoparticles significantly decreases the conductivity. Most of this reduction can be simply accounted for by the diffusion equation, resulting from the fact that the space occupied by the nanoparticles is made inactive and unable to sustain ionic diffusion. We identify two distinct regimes, above and below a concentration threshold, corresponding to very different ionic distributions and coordination features of the various species. The lack of conductivity enhancement observed in the simulations supports the conclusions of some recent measurements, and disagrees with the earliest experimental reports on hybrid silica/polyethylene-oxide electrolytes.

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 batteries

Next-generation high-efficiency Li-ion batteries require an electrolyte that is both safe and thermally stable. A possible choice for high performance all-solid-state Li-ion batteries is a liquid crystal, which possesses properties in-between crystalline solids and isotropic liquids. By employing molecular dynamics simulations together with various experimental techniques, we have designed and analyzed a novel liquid crystal electrolyte composed of rigid naphthalene-based moieties as mesogenic units, grafted to flexible alkyl chains of different lengths. We have synthesized novel highly ordered lamellar phase liquid crystal electrolytes at 99% purity and have evaluated the effect of alkyl chain length variation on ionic conduction. We find that the conductivity of the liquid crystal electrolytes is directly dependent on the extent of the nanochannels formed by molecule self-organization, which itself depends non-monotonously on the size of the alkyl chains. In addition, we show that the ion pair interaction between the anionic center of the liquid crystal molecules and the Li+ ions plays a crucial role in the overall conductivity. Based on our results, we suggest that further improvement of the ionic conductivity performance is possible, making this novel family of liquid crystal electrolytes a promising option for the design of entirely solid-state Li+ ion batteries.

Despite important breakthroughs in the last decade, the calculation of temperature dependent properties of solids still remains a challenging task, especially in the vicinity of structural phase transitions. We show that the combination of machine-learning interatomic potentials with quantum self-consistent ab initio lattice dynamics allows to calculate efficiently the temperature dependence of dielectric properties of the quantum paraelectric perovskite KTaO$_3$, with a precision beyond what could be reasonably achieved using plain density functional theory. We first follow the strong anharmonic softening of the polar mode in this incipient ferroelectric material, and the resulting divergence of the dielectric constant that eventually saturates due to the interplay between temperature and quantum fluctuations. Further, we predict the stability range of the quantum paraelectric state under the application of epitaxial strain at 0 K and 300 K. Finally, we calculate the temperature dependence of electrostrictive tensors for this material and show that giant electrostriction in KTaO$_3$ is to be expected also at room temperature under the condition of strain engineering., Comment: 7 pages, 6 figures

(CoxMn1-x)3O4 is a promising candidate material for solar thermochemical energy storage. A high-temperature model for this system would provide a valuable tool for evaluating its potential. However, predicting phase diagrams of complex systems with ab initio calculations is challenging due to the varied sources affecting the free energy, and with the prohibitive amount of configurations needed in the configurational entropy calculation. In this work, we compare three different machine learning (ML) approaches for sampling the configuration space of (CoxMn1-x)3O4, including a simpler ML approach, which would be suitable for application in high-throughput studies. We use experimental data for a feature of the phase diagram to assess the accuracy of model predictions. We find that with some methods, data pre-treatment is needed to obtain accurate predictions due to inherently composition-imbalanced training data for a mixed phase. We highlight that the important entropy contributions depend on the physical regimes of the system under investigation and that energy predictions with ML models are more challenging at compositions where there are energetically competing ground state crystal structures. Similar methods to those outlined here can be used to screen other candidate materials for thermochemical energy storage

The promise enabled by BAs high thermal conductivity in power electronics cannot be assessed without taking into account the reduction incurred when doping the material. Using first principles calculations, we determine the thermal conductivity reduction induced by different group IV impurities in BAs as a function of concentration and charge state. We unveil a general trend, where neutral impurities scatter phonons more strongly than the charged ones. $\text{C}_{\text{B}}$ and $\text{Ge}_{\text{As}}$ impurities show by far the weakest phonon scattering and retain BAs $\kappa$ values of over $\sim$ 1000 $\text{W}\cdot\text{K}^{-1}\cdot\text{m}^{-1}$ even up to high densities making them ideal n-type and p-type dopants. Furthermore, going beyond the doping compensation threshold associated to Fermi level pinning triggers observable changes in the thermal conductivity. This informs design considerations on the doping of BAs, and it also suggests a direct way to determine the onset of compensation doping in experimental samples.

The Quantum Self-Consistent Ab-Initio Lattice Dynamics package (QSCAILD) is a python library that computes temperature-dependent effective 2nd and 3rd order interatomic force constants in crystals, including anharmonic effects. QSCAILD's approach is based on the quantum statistics of a harmonic model. The program requires the forces acting on displaced atoms of a solid as an input, which can be obtained from an external code based on density functional theory, or any other calculator. This article describes QSCAILD's implementation, clarifies its connections to other methods, and illustrates its use in the case of the SrTiO3 cubic perovskite structure., Comment: Code available at https://github.com/vanroeke/qscaild

Lattice anharmonicity is thought to strongly affect vacancy concentrations in metals at high temperatures. It is however non-trivial to account for this effect directly using density functional theory (DFT). Here we develop a deep neural network potential for aluminum that overcomes the limitations inherent to DFT, and we use it to obtain accurate anharmonic vacancy formation free energies as a function of temperature. While confirming the important role of anharmonicity at high temperatures, the calculation unveils a markedly nonlinear behavior of the vacancy formation entropy and shows that the vacancy formation free energy only violates Arrhenius law at temperatures above 600 K, in contrast with previous DFT calculations.

The magnetic correlations in the austenite phase and the consequent martensitic transition in inverse magnetocaloric alloys, Ni$_{2}$Mn$_{1+x}$Sn$_{1-x}$, have been a matter of debate for decades. We conclusively establish using {\it ab initio} phonon calculations that the spin alignment of excess Mn at the Sn site (Mn$_{Sn}$) with the existing Mn in the unit cell in the high temperature cubic phase of Ni-Mn-Sn alloy is ferromagnetic (FM), and not ferrimagnetic (FI), resolving a long lasting controversy. Using first principles density functional perturbation theory (DFPT), we observe an instability of the TA$_{2}$ mode along the $\Gamma$-M direction in the FM phase, very similar to that observed in the prototypical ferromagnetic shape memory alloy (FSMA) Ni$_{2}$MnGa. This specific instability is not observed in the FI phase. Further finite temperature first principles lattice dynamics calculations reveal that at 300 K the FM phase becomes mechanically stable, while the FI phase continue to remain unstable providing credence to the fact that the high-temperature phase has FM order. These results will be primordial to understand the magneto-structural properties of this class of compounds., Comment: Supplemental materials are uploaded as a separate .pdf file

Compositionally graded interfaces in power electronic devices eliminate dislocations, but they can also decrease thermal conduction, leading to overheating. We quantify the thermal resistances of GaN/AlN graded interfaces of varying thickness using ab initio Green's functions, and compare them with the abrupt interface case. A non-trivial power dependence of the thermal resistance versus interface thickness emerges from the interplay of alloy and mismatch scattering mechanisms. We show that the overall behavior of such graded interfaces is very similar to that of a thin-film of an effective alloy in the length scales relevant to real interfaces.

Using finite-temperature phonon calculations and the Lyddane-Sachs-Teller relations, we calculate ab initio the static dielectric constants of 78 semiconducting oxides and fluorides with cubic perovskite structures at 1000 K. We first compare our method with experimental measurements, and we find that it succeeds in describing the temperature dependence and the relative ordering of the static dielectric constant $\epsilon_{DC}$ in the series of oxides BaTiO$_{3}$, SrTiO$_{3}$, KTaO$_{3}$. We show that the effects of anharmonicity on the ion-clamped dielectric constant, on Born charges, and on phonon lifetimes, can be neglected in the framework of our high-throughput study. Based on the high-temperature phonon spectra, we find that the dispersion of $\epsilon_{DC}$ is one order of magnitude larger amongst oxides than fluorides at 1000 K. We display the correlograms of the dielectric constants with simple structural descriptors, and we point out that $\epsilon_{DC}$ is actually well correlated with the infinite-frequency dielectric constant $\epsilon_{\infty}$, even in those materials with phase transitions in which $\epsilon_{DC}$ is strongly temperature-dependent.

Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on only 121 metastable structures of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\textrm\AA^2$ for the interatomic force constants, and it is much less expensive than training the complete force field for such compound. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures., Comment: 18 pages, 4 figures

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 Japan

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 tables

almaBTE is a software package that solves the space- and time-dependent Boltzmann transport equation for phonons, using only ab-initio calculated quantities as inputs. The program can predictively tackle phonon transport in bulk crystals and alloys, thin films, superlattices, and multiscale structures with size features in the nm-$\mu$m range. Among many other quantities, the program can output thermal conductances and effective thermal conductivities, space-resolved average temperature profiles, and heat-current distributions resolved in frequency and space. Its first-principles character makes almaBTE especially well suited to investigate novel materials and structures. This article gives an overview of the program structure and presents illustrative examples for some of its uses.

Computing vibrational free energies ($F_{vib}$) and entropies ($S_{vib}$) has posed a long standing challenge to the high-throughput ab initio investigation of finite temperature properties of solids. Here we use machine-learning techniques to efficiently predict $F_{vib}$ and $S_{vib}$ of crystalline compounds in the Inorganic Crystal Structure Database. By employing descriptors based simply on the chemical formula and using a training set of only 300 compounds, mean absolute errors of less than 0.04 meV/K/atom (15 meV/atom) are achieved for $S_{vib}$ ($F_{vib}$), whose values are distributed within a range of 0.9 meV/K/atom (300 meV/atom.) In addition, for training sets containing fewer than 2,000 compounds the chemical formula alone is shown to perform as well as, if not better than, four other more complex descriptors previously used in the literature. The accuracy and simplicity of the approach mean that it can be advantageously used for the fast screening of phase diagrams or chemical reactions at finite temperatures.

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 figures

We calculate the strength of the frequency-dependent on-site electronic interactions in the iron pnictides LaFeAsO, BaFe2As2, BaRu2As2, and LiFeAs and the chalcogenide FeSe from first principles within the constrained random phase approximation. We discuss the accuracy of an atomic-like parametrization of the two-index density-density interaction matrices based on the calculation of an optimal set of three independent Slater integrals, assuming that the angular part of the Fe-d localized orbitals can be described within spherical harmonics as for isolated Fe atoms. We show that its quality depends on the ligand-metal bonding character rather than on the dimensionality of the lattice: it is excellent for ionic-like Fe-Se (FeSe) chalcogenides and a more severe approximation for more covalent Fe-As (LaFeAsO, BaFe2As2) pnictides. We furthermore analyze the relative importance of different screening channels, with similar conclusions for the different pnictides but a somewhat different picture for the benchmark oxide SrVO3: the ligand channel does not appear to be dominant in the pnictides, while oxygen screening is the most important process in the oxide. Finally, we analyze the frequency dependence of the interaction. In contrast to simple oxides, in iron pnictides its functional form cannot be simply modeled by a single plasmon, and the actual density of modes enters the construction of an effective Hamiltonian determining the low-energy properties., Comment: 18 pages, 5 figures

The empty perovskite ScF3 exhibits negative thermal expansion up to 1100 K. We demonstrate that ab-initio calculations of temperature-dependent effective phonon spectra allow to quantitatively describe the behavior of this compound and the suppression of negative thermal expansion. Based on this result, we predict an anomalous temperature dependence of the thermal conductivity and interpret it as a general feature of the perovskite class. Finally, we comment on the fact that the suppression of negative thermal expansion at such a high temperature is beyond the reach of the quasi-harmonic approximation and we discuss this suppression based on the temperature-dependence of the mode Gr\"uneisen parameters., Comment: 5 pages, 4 figures

The use of effective local Coulomb interactions that are dynamical, that is, frequency-dependent, is an efficient tool to describe the effect of long-range Coulomb interactions and screening thereof in solids. The dynamical character of the interaction introduces the coupling to screening degrees of freedom such as plasmons or particle-hole excitations into the many-body description. We summarize recent progress using these concepts, putting emphasis on dynamical mean field theory (DMFT) calculations with dynamical interactions ("doubly dynamical mean field theory"). We discuss the relation to the combined GW+DMFT method and its simplified version "Screened Exchange DMFT", as well as the cumulant schemes of many-body perturbation theory. On the example of the simple transition metal SrVO3, we illustrate the mechanism of the appearance of plasmonic satellite structures in the spectral properties, and discuss implications for the low-energy electronic structure., Comment: Contribution to special issue of Journal of Electron Spectroscopy and Related Phenomena on the occasion of the 60th birthday of D.D. Sarma

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.

The determination of the effective Coulomb interactions to be used in low-energy Hamiltonians for materials with strong electronic correlations remains one of the bottlenecks for parameter-free electronic structure calculations. We propose and benchmark a scheme for determining the effective local Coulomb interactions for charge-transfer oxides and related compounds. Intershell interactions between electrons in the correlated shell and ligand orbitals are taken into account in an effective manner, leading to a reduction of the effective local interactions on the correlated shell. Our scheme resolves inconsistencies in the determination of effective interactions as obtained by standard methods for a wide range of materials, and allows for a conceptual understanding of the relation of cluster model and dynamical mean field-based electronic structure calculations., Comment: 5 pages, 2 figures + Supplemental material

We present a study of the tetragonal to collapsed-tetragonal transition of CaFe2As2 using angle-resolved photoemission experiments and dynamical mean field theory-based electronic structure calculations. We observe that the collapsed-tetragonal phase exhibits reduced correlations and a higher coherence temperature due to the stronger Fe-As hybridization. Furthermore, a comparison of measured photoemission spectra and theoretical spectral functions shows that momentum-dependent corrections to the density functional band structure are essential for the description of low-energy quasiparticle dispersions. We introduce those using the recently proposed combined "Screened Exchange + Dynamical Mean Field Theory" scheme., Comment: 7 pages, 7 figures

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 figures

The electronic structure of the intercalated iron-based superconductor Ba2Ti2Fe2As4O (Tc - 21.5 K) has been investigated by using angle-resolved photoemission spectroscopy and combined local density approximation and dynamical mean field theory calculations. The electronic states near the Fermi level are dominated by both the Fe 3d and Ti 3d orbitals, indicating that the spacing layers separating different FeAs layers are also metallic. By counting the enclosed volumes of the Fermi surface sheets, we observe a large self-doping effect, i.e. 0.25 electrons per unit cell are transferred from the FeAs layer to the Ti2As2O layer, leaving the FeAs layer in a hole-doped state. This exotic behavior is successfully reproduced by our dynamical mean field calculations, in which the self-doping effect is attributed to the electronic correlations in the Fe 3d shell. Our work provides an alternative route of effective doping without element substitution for iron-based superconductors., Comment: 5 pages, 3 figures + Supplementary material

Understanding the Fermi surface and low-energy excitations of iron or cobalt pnictides is crucial for assessing electronic instabilities such as magnetic or superconducting states. Here, we propose and implement a new approach to compute the low-energy properties of correlated electron materials, taking into account both screened exchange beyond the local density approximation and local dynamical correlations. The scheme allows us to resolve the puzzle of BaCo2As2, for which standard electronic structure techniques predict a ferromagnetic instability not observed in nature., Comment: 6 pages, 4 figures + Supplementary material