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Solid-state electrolytes are essential in the development of all-solid-state batteries. While density functional theory (DFT)-based nudged elastic band (NEB) and ab initio molecular dynamics (AIMD) methods provide fundamental insights on…
We introduce a machine learning prediction workflow to study the impact of defects on the Raman response of 2D materials. By combining the use of machine-learned interatomic potentials, the Raman-active $\Gamma$-weighted density of states…
The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not…
A gap in understanding the link between continuum theories of ion transport in ionic liquids and the underlying microscopic dynamics has hindered the development of frameworks for transport phenomena in these concentrated electrolytes.…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal…
Molecular dynamics simulations are a powerful tool to study diffusion processes in battery electrolyte and electrode materials. From a single molecular dynamics simulation many properties relevant to diffusion can be obtained, including the…
In the context of novel solid electrolytes for solid-state batteries, first-principles calculations are becoming increasingly more popular due to their ability to reproduce and predict accurately the energy, structural, and dynamical…
Visualizing the electron dynamics in four dimensions of space and time is crucial to the understanding of several ubiquitous processes in nature. Hence, ultrafast X-ray and electron imaging tools have been developed to probe the dynamics of…
Self-trapping and acceleration of ions in laser-driven relativistically transparent plasma are investigated with the help of particle-in-cell simulations. A theoretical model based on ion wave breaking is established in describing ion…
The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal…
A method is proposed for producing monoergetic, high-quality ion beams in vacuum, via direct acceleration by the electromagnetic field of two counterpropagating, variable-frequency lasers: ions are trapped and accelerated by a beat-wave…
For being used as an electrolyte in All Solid State Batteries (ASSB), a solid electrolyte must possess ionic conductivity comparable to that of conventional liquid electrolytes. To achieve this conductivity range, the series…
We present an efficient criterion for probing the critical temperature of hydrogen based superconductors. We start by expanding the applicability of 3D descriptors of electron localization to superconducting states within the framework of…
This work presents conclusive observations about the long lasting topic of eumelanins' electronic vs. ionic conduction, allowing to access unprecedented conductivity of this mammal pigment. Key strengths of the study include, the easy…
The field-dependent ion transport in thin samples of different glasses is characterised by means of nonlinear conductivity spectroscopy. AC electric fields with strengths up to 77 kV/cm are applied to the samples, and the Fourier components…
Ionic conductors have great potential for interesting tunable physical properties via ionic liquid gating and novel energy storage applications such as all-solid-state lithium batteries. In particular, low migration barriers and high…
Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the…
We present a computational screening of experimental structural repositories for fast Li-ion conductors, with the goal of finding new candidate materials for application as solid-state electrolytes in next-generation batteries. We start…
Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of…