Leah Wairimu Mungai | Searcharxiv

arXiv2026

Thermoelastic Properties Of The Ti2AlC MAX Phase: An Ab Initio Study

MAX phases are used on an industrial scale in the transportation, armour, and furnace development sectors, among others. However, data on the dynamical properties of these materials under varying temperature and pressure conditions are rare or unavailable. This study reports on the dynamical properties of the elastic constants of Ti2AlC, under these conditions, obtained from first-principles calculations. Both static and dynamical results are presented and discussed. The dynamical results show that the elastic moduli are degraded; specifically, the bulk and shear moduli show a reduction ranging from 15 to 29% and 13 to 31%, respectively, between pressures of 10 - 30 GPa and in the temperature range of 300 - 1200 K. The reduction in these moduli is likely caused by anharmonic lattice effects that lead to thermal-induced softening, particularly in the high-pressure and temperature range. The lattice parameters of this material under the conditions of study did not vary significantly. Such data is useful as part of decision support that can inform applications as well as the limitations of use.

cond-mat.mtrl-sci↗

arXiv2025

OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes

Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a database of $\sim$600 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities gathered from literature and curated by domain experts. Each material is described by their measured composition, space group and lattice parameters. A full-crystal description in the form of a crystallographic information file (CIF) is provided for $\sim$320 structures for which atomic positions were available. We discuss various statistics and features of the dataset and provide training and testing splits carefully designed to avoid data leakage. Finally, we benchmark seven existing ML models on the task of predicting ionic conductivity and discuss their performance. The goal of this work is to facilitate the use of machine learning for solid-state electrolyte materials discovery.

cond-mat.mtrl-sci↗