1 citations · 1 across the 1 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2025
The diffusion-driven orthorhombic to tetragonal transition in YBaCuO derived with a machine learning interatomic potential
Davide Gambino, Niccolò Di Eugenio, Jesper Byggmästar +4
Defects in high temperature superconductors such as YBaCuO (YBCO) critically influence their superconducting behavior, as they substantially degrade or even suppress su…
cond-mat.mtrl-sci2025
Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields
Xia Liang, Johan Klarbring, Aron Walsh
Lead halide perovskites (APbX) offer tunable optoelectronic properties but feature an intricate phase-stability landscape. Here we employ on-the-fly data collection and an equi…
cond-mat.mtrl-sci2024★ 1 cited
Point defect formation at finite temperatures with machine learning force fields
Irea Mosquera-Lois, Johan Klarbring, Aron Walsh
Point defects dictate the properties of many functional materials. The standard approach to modelling the thermodynamics of defects relies on a static description, where the change…