Predicting Density of States via Multi-modal Transformer
Abstract
The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials. In this paper, we propose a model to predict the DOS by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy. Specifically, we integrate the heterogeneous information obtained from the crystal structure and the energies via multi-modal transformer, thereby modeling the complex relationships between the atoms in the crystal structure, and various energy levels. Extensive experiments on two types of DOS, i.e., Phonon DOS and Electron DOS, with various real-world scenarios demonstrate the superiority of DOSTransformer. The source code for DOSTransformer is available at https://github.com/HeewoongNoh/DOSTransformer.
Cite
@article{arxiv.2303.07000,
title = {Predicting Density of States via Multi-modal Transformer},
author = {Namkyeong Lee and Heewoong Noh and Sungwon Kim and Dongmin Hyun and Gyoung S. Na and Chanyoung Park},
journal= {arXiv preprint arXiv:2303.07000},
year = {2023}
}
Comments
ICLR 2023 Workshop on Machine Learning for Materials (ML4Materials)