Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

Ryosuke Jinnouchi; Jonathan Lahnsteiner; Ferenc Karsai; Georg Kresse; Menno Bokdam

doi:10.1103/PhysRevLett.122.225701

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

Ryosuke Jinnouchi
, Jonathan Lahnsteiner
, Ferenc Karsai
, Georg Kresse
, Menno Bokdam (Corresponding author)

Computational Materials Physics

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Article number	225701
Number of pages	5
Journal	Physical Review Letters
Volume	122
Issue number	22
DOIs	https://doi.org/10.1103/PhysRevLett.122.225701
Publication status	Published - 7 Jun 2019

Funding

J. L. and M. B. gratefully acknowledge funding by the Austrian Science Fund (FWF): P30316-N27. Computations were partly performed on the Vienna Scientific Cluster VSC3. All authors gratefully thank Ryoji Asahi for many suggestions on applications of the machine-learning method to materials science and Carla Verdi for proof reading of the manuscript.

Austrian Fields of Science 2012

103018 Materials physics

Keywords

TOTAL-ENERGY CALCULATIONS
CRYSTAL-STRUCTURE
ORGANIC CATIONS
POTENTIALS
DYNAMICS

Access to Document

10.1103/PhysRevLett.122.225701

Cite this

@article{1ad2c78b2d4b4a68b1379d1ab4bb6c25,

title = "Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference",

keywords = "TOTAL-ENERGY CALCULATIONS, CRYSTAL-STRUCTURE, ORGANIC CATIONS, POTENTIALS, DYNAMICS",

author = "Ryosuke Jinnouchi and Jonathan Lahnsteiner and Ferenc Karsai and Georg Kresse and Menno Bokdam",

note = "Publisher Copyright: {\textcopyright} 2019 American Physical Society.",

year = "2019",

month = jun,

day = "7",

doi = "10.1103/PhysRevLett.122.225701",

language = "English",

volume = "122",

journal = "Physical Review Letters",

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TY - JOUR

T1 - Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

AU - Jinnouchi, Ryosuke

AU - Lahnsteiner, Jonathan

AU - Karsai, Ferenc

AU - Kresse, Georg

AU - Bokdam, Menno

PY - 2019/6/7

Y1 - 2019/6/7

KW - TOTAL-ENERGY CALCULATIONS

KW - CRYSTAL-STRUCTURE

KW - ORGANIC CATIONS

KW - POTENTIALS

KW - DYNAMICS

UR - https://arxiv.org/abs/1903.09613

UR - https://www.scopus.com/pages/publications/85066933841

U2 - 10.1103/PhysRevLett.122.225701

DO - 10.1103/PhysRevLett.122.225701

M3 - Article

SN - 0031-9007

VL - 122

JO - Physical Review Letters

JF - Physical Review Letters

IS - 22

M1 - 225701

ER -

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

Funding

Austrian Fields of Science 2012

Keywords

Access to Document

Other files and links

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Dynamics in perovskite photovoltaics

Cite this

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

Funding

Austrian Fields of Science 2012

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Dynamics in perovskite photovoltaics

Cite this