Predicting Density of States via Multi-modal Transformer
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Abstract (arXiv)
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.
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References · 7
- 2017Neural Message Passing for Quantum Chemistrycited by 3,460
- 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
- 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
- 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
- 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 348
- 2018Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Networkcited by 168
- 2015Learning Deep Structure-Preserving Image-Text Embeddingscited by 139
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