Search Results for author: Thomas Christensen

Found 3 papers, 2 papers with code

Multimodal Learning for Crystalline Materials

no code implementations30 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.

Property Prediction

Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials

1 code implementation10 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.

Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science

1 code implementation15 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.

Contrastive Learning Property Prediction

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