no code implementations • 30 Nov 2023 • Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Peter Y. Lu, Thomas Christensen, Marin Soljačić
In this work, we introduce Multimodal Learning for Crystalline Materials (MLCM), a new method for training a foundation model for crystalline materials via multimodal alignment, where high-dimensional material properties (i. e. modalities) are connected in a shared latent space to produce highly useful material representations.
1 code implementation • 10 Feb 2022 • Andrew Ma, Yang Zhang, Thomas Christensen, Hoi Chun Po, Li Jing, Liang Fu, Marin Soljačić
Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications.
1 code implementation • 15 Oct 2021 • Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljacic
Deep learning techniques have been increasingly applied to the natural sciences, e. g., for property prediction and optimization or material discovery.