Accelerated Structure Prediction of Halide Perovskites with Machine Learning

Show full item record



Permalink

http://urn.fi/URN:NBN:fi:hulib-202104071827
Title: Accelerated Structure Prediction of Halide Perovskites with Machine Learning
Author: Laakso, Jarno
Other contributor: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta
University of Helsinki, Faculty of Science
Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten
Publisher: Helsingin yliopisto
Date: 2021
Language: eng
URI: http://urn.fi/URN:NBN:fi:hulib-202104071827
http://hdl.handle.net/10138/328800
Thesis level: master's thesis
Degree program: Materiaalitutkimuksen maisteriohjelma
Master's Programme in Materials Research
Magisterprogrammet i materialforskning
Specialisation: Laskennallinen materiaalifysiikka
Computational Material Physics
Beräkningsmaterialfysik
Abstract: Halide perovskites are a promising materials class for solar energy production. The photovoltaic efficiency of halide perovskites is remarkable but their toxicity and instability have prevented commercialization. These problems could be addressed through compositional engineering in the halide perovskite materials space but the number of different materials that would need to be considered is too large for conventional experimental and computational methods. Machine learning can be used to accelerate computations to the level that is required for this task. In this thesis I present a machine learning approach for compositional exploration and apply it to the composite halide perovskite CsPb(Cl, Br)3 . I used data from density functional theory (DFT) calculations to train a machine learning model based on kernel ridge regression with the many-body tensor representation for the atomic structure. The trained model was then applied to predict the decomposition energies of CsPb(Cl, Br)3 materials from their atomic structure. The main part of my work was to derive and implement gradients for the machine learning model to facilitate efficient structure optimization. I tested the machine learning model by comparing its decomposition energy predictions to DFT calculations. The prediction accuracy was under 0.12 meV per atom and the prediction time was five orders of magnitude faster than DFT. I also used the model to optimize CsPb(Cl, Br)3 structures. Reasonable structures were obtained, but the accuracy was qualitative. Analysis on the results of the structural optimizations exposed shortcomings in the approach, providing important insight for future improvements. Overall, this project makes a successful step towards the discovery of novel perovskite materials with designer properties for future solar cell applications.
Subject: Materials physics
Machine learning
Perovskite solar cells


Files in this item

Total number of downloads: Loading...

Files Size Format View
Laakso_Jarno_tutkielma_2021.pdf 6.011Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record