Predicting polarizabilities of silicon clusters using local chemical environments
File(s)
Published version
Author(s)
Zauchner, Mario G
Dal Forno, Stefano
Cśanyi, Gábor
Horsfield, Andrew
Lischner, Johannes
Type
Journal Article
Abstract
Calculating polarizabilities of large clusters with first-principles techniques is challenging because of the unfavorable scaling of computational cost with cluster size. To address this challenge, we demonstrate that polarizabilities of large hydrogenated silicon clusters containing thousands of atoms can be efficiently calculated with machine learning methods. Specifically, we construct machine learning models based on the smooth overlap of atomic positions (SOAP) descriptor and train the models using a database of calculated random-phase approximation polarizabilities for clusters containing up to 110 silicon atoms. We first demonstrate the ability of the machine learning models to fit the data and then assess their ability to predict cluster polarizabilities using k-fold cross validation. Finally, we study the machine learning predictions for clusters that are too large for explicit first-principles calculations and find that they accurately describe the dependence of the polarizabilities on the ratio of hydrogen to silicon atoms and also predict a bulk limit that is in good agreement with previous studies.
Date Issued
2021-12-01
Date Acceptance
2021-10-05
Citation
Machine Learning: Science and Technology, 2021, 2 (4), pp.1-16
ISSN
2632-2153
Publisher
IOP Publishing
Start Page
1
End Page
16
Journal / Book Title
Machine Learning: Science and Technology
Volume
2
Issue
4
Copyright Statement
© 2021 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Identifier
https://iopscience.iop.org/article/10.1088/2632-2153/ac2cfe
Publication Status
Published
Date Publish Online
2021-10-22