Direct Prediction of Phonon Density of States With Euclidean Neural Networks
Name
advs.202004214.pdf
Description
Published version
Size
1.76 MB
Format
Adobe PDF
Checksum (MD5)
0bd84ebe1c42e3b21f62f73eec210795
Author(s)
Chen, Zhantao
Andrejevic, Nina
Smidt, Tess
Ding, Zhiwei
Xu, Qian
Chi, Yen‐Ting
Nguyen, Quynh T
Alatas, Ahmet
Kong, Jing
Li, Mingda
Date Issued
2021
Journal
Advanced Science
Publisher
Wiley
Version
Final published version
Abstract
© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density-of-states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of (Formula presented.) examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/ADVS.202004214
