Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction release_zejpbpo7bzbq7ftd2uq6vvxbkm

by Xavier Brumwell and Paul Sinz and Kwang Jin Kim and Yue Qi and Matthew Hirn

Released as a article .

2019  

Abstract

A general machine learning architecture is introduced that uses wavelet scattering coefficients of an inputted three dimensional signal as features. Solid harmonic wavelet scattering transforms of three dimensional signals were previously introduced in a machine learning framework for the regression of properties of small organic molecules. Here this approach is extended for general steerable wavelets which are equivariant to translations and rotations, resulting in a sparse model of the target function. The scattering coefficients inherit from the wavelets invariance to translations and rotations. As an illustration of this approach a linear regression model is learned for the formation energy of amorphous lithium-silicon material states trained over a database generated using plane-wave Density Functional Theory methods. State-of-the-art results are produced as compared to other machine learning approaches over similarly generated databases.
In text/plain format

Archived Files and Locations

application/pdf  755.0 kB
file_r7bwirif4jdcrmzhfyeqv5bhsi
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2019-01-28
Version   v2
Language   en ?
arXiv  1812.02320v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 78015766-634b-45ff-a582-21a1df3d1064
API URL: JSON