Revealing the Low-Temperature Phase of FAPbI3 Using a Machine-Learned Potential
Journal article, 2025
Author
Sangita Dutta
Chalmers, Physics, Condensed Matter and Materials Theory
Erik Fransson
Chalmers, Physics, Condensed Matter and Materials Theory
Tobias Hainer
Chalmers, Physics, Condensed Matter and Materials Theory
Benjamin M. Gallant
University of Birmingham
Dominik J. Kubicki
University of Birmingham
Paul Erhart
Chalmers, Physics, Condensed Matter and Materials Theory
Julia Wiktor
Chalmers, Physics, Condensed Matter and Materials Theory
Journal of the American Chemical Society
0002-7863 (ISSN) 1520-5126 (eISSN)
Vol. In PressProton- och hydridjon-ledning i perovskiter
Swedish Energy Agency (45410-1), 2018-01-01 -- 2021-12-31.
Phase behavior and electronic properties of mixed halide perovskites from atomic scale simulations
Swedish Research Council (VR) (2020-04935), 2020-12-01 -- 2024-11-30.
Atomistic Design of Photoabsorbing Materials
Swedish Research Council (VR) (2019-03993), 2020-01-01 -- 2023-12-31.
Analysis and Modelling Service for Engineering Materials Studied with Neutrons
Swedish Research Council (VR) (2018-06482), 2018-11-01 -- 2020-12-31.
Harnessing Localized Charges for Advancing Polar Materials Engineering (POLARISE)
European Commission (EC) (EC/HE/101162195), 2025-01-01 -- 2029-12-31.
Subject Categories (SSIF 2025)
Atom and Molecular Physics and Optics
Condensed Matter Physics
Physical Chemistry
Areas of Advance
Nanoscience and Nanotechnology
Infrastructure
Chalmers e-Commons (incl. C3SE, 2020-)
DOI
10.1021/jacs.5c05265
PubMed
40810555
Related datasets
Revealing the Low Temperature Phase of FAPbI3 using A Machine-Learned Potential - Datasets [dataset]
DOI: 10.5281/zenodo.16805881 URI: https://zenodo.org/records/16805881