arXiv subjects

Ambroise van Roekeghem

Publications and source records attributed to Ambroise van Roekeghem.

At least 19 recordsLinked to original sources

arXiv2026

A molecular perspective on coordination, screening, and emergent length scales in lithium electrolytes

Lithium electrolytes are commonly described using separate conceptual frameworks for local coordination chemistry, electrostatic screening, and ionic transport. This separation is effective in dilute conditions but breaks down at higher concentration, where coordination, ion pairing, clustering, and collective dynamics become intrinsically coupled. In this Perspective, we develop a unified multiscale framework that links local coordination motifs, mesoscopic ionic organization, and macroscopic transport within a single physical picture. Guided primarily by representative examples from molecular simulations, spanning carbonate liquids, polymer electrolytes, concentrated systems, and confinement, we show that increasing concentration drives a systematic evolution from solvent-dominated Li$^+$ coordination to ion pairing, clustering, and correlated ionic domains. In this regime, screening and transport are not independent phenomena but emerge from the same underlying correlated structures. The resulting hierarchy provides both a conceptual framework for interpreting electrolyte behavior and a foundation for multiscale computational approaches, including machine-learning-assisted coarse-graining, that connect atomistic chemistry to macroscopic electrochemical performance. This Perspective implies that rational electrolyte design must simultaneously control short-range coordination, mesoscale organization, and collective electrostatic response.

cond-mat.soft↗

arXiv2026

Upscaling from ab initio atomistic simulations to electrode scale: The case of manganese hexacyanoferrate, a cathode material for Na-ion batteries

We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology employs an active-learning strategy to train a Moment Tensor Potential through iterative hybrid grand-canonical Monte Carlo--molecular dynamics sampling, robustly capturing configuration spaces at all sodiation levels. The resulting machine learning interatomic potential accurately reproduces experimental properties including volume expansion, operating voltage, and sodium concentration-dependent structural transformations, while revealing a four-order-of-magnitude difference in sodium diffusivity between the rhombohedral (sodium-rich) and tetragonal (sodium-poor) phases at 300 K. We directly compute all critical parameters -- temperature- and concentration-dependent diffusivities, interfacial and strain energies, and complete free-energy landscapes -- to feed them into pseudo-2D phase-field simulations that predict phase-boundary propagation and rate-dependent performances across electrode length scales. This multiscale workflow establishes a blueprint for rational computational design of next-generation insertion-type materials, such as battery electrode materials, demonstrating how atomistic insights can be systematically translated into continuum-scale predictions.

cond-mat.mtrl-sci↗

arXiv2024

Can silica nanoparticles improve lithium transport in polymer electrolytes?

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.

cond-mat.soft↗

arXiv2024

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.

cond-mat.mtrl-sci↗

arXiv2023

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.

cond-mat.mtrl-sci↗

arXiv2022

Finite temperature dielectric properties of KTaO$_3$ from first principles and machine learning: Phonon spectra, Barrett law, strain engineering and electrostriction

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.

cond-mat.mtrl-sci↗

arXiv2021

Free energy of (CoxMn1-x)3O4 mixed phases from machine-learning-enhanced ab initio calculations

(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

cond-mat.mtrl-sci↗

arXiv2021

Quantum Self-Consistent Ab-Initio Lattice Dynamics

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.

cond-mat.mtrl-sci↗

arXiv2020

High-throughput study of the static dielectric constant at high temperatures in oxide and fluoride cubic perovskites

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 $ε_{DC}$ 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 $ε_{DC}$ 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 $ε_{DC}$ is actually well correlated with the infinite-frequency dielectric constant $ε_{\infty}$, even in those materials with phase transitions in which $ε_{DC}$ is strongly temperature-dependent.

cond-mat.mtrl-sci↗

arXiv2020

How do defects limit the ultrahigh thermal conductivity of BAs? A first principles study

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 $κ$ 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.

cond-mat.mtrl-sci↗

arXiv2019

Anharmonic Thermodynamics of Vacancies Using a Neural Network Potential

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.

cond-mat.mtrl-sci↗

arXiv2019

Thermal resistance of GaN/AlN graded interfaces

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.

cond-mat.mtrl-sci↗

arXiv2018

Magnetic Order and Lattice Instabilities in Ni$_{2}$Mn$_{1+x}$Sn$_{1-x}$ Heusler based Magnetic Shape-Memory Alloys

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 $Γ$-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.

cond-mat.str-el↗

arXiv2018

Vibrational properties of metastable polymorph structures by machine learning

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/$\textrmÅ^2$ 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.

cond-mat.mtrl-sci↗

arXiv2017

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.

cond-mat.str-el↗

arXiv2017

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.

cond-mat.mtrl-sci↗

arXiv2017

almaBTE: a solver of the space-time dependent Boltzmann transport equation for phonons in structured materials

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-$μ$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.

physics.comp-ph↗

arXiv2017

How the Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

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.

cond-mat.mtrl-sci↗