5 papers
Barocaloric phase transformation from data efficient fine-tuning of machine learned interatomic potentials
Ludwig Hedin, Johan Klarbring
Solid-state cooling based on the barocaloric (BC) effect has emerged as promising environmentally friendly prospective alternative to conventional vapor-compression refrigeration.…
Kinetically Arrested Twin-Domain State in Formamidinium Lead Iodide
Xia Liang, Milos Dubajic, Zezhu Zeng +4
Hybrid lead halide perovskites exhibit a delicate interplay between average crystallographic symmetry, local structural disorder and A-site orientational dynamics, giving rise to u…
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…
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…
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…