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Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants: iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations. Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).
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This content will become publicly available on December 3, 2025
GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules
Pretraining molecular representations is crucial for drug and material discovery. Recent methods focus on learning representations from geometric structures, effectively capturing 3D position information. Yet, they overlook the rich information in biomedical texts, which detail molecules’ properties and substructures. With this in mind, we set up a data collection effort for 200K pairs of ground-state geometric structures and biomedical texts, resulting in a PubChem3D dataset. Based on this dataset, we propose the GeomCLIP framework to enhance geometric pretraining and understanding by biomedical texts. During pre-training, we design two types of tasks, i.e., multimodal representation alignment and unimodal denoising pretraining, to align the 3D geometric encoder with textual information and, at the same time, preserve its original representation power. Experimental results show the effectiveness of GeomCLIP in various tasks such as molecule property prediction, zero-shot text-molecule retrieval, and 3D molecule captioning. Our code and collected dataset are available at https://github.com/xiaocui3737/GeomCLIP.
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- Award ID(s):
- 2020243
- PAR ID:
- 10566045
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-8622-6
- Page Range / eLocation ID:
- 1250 to 1256
- Format(s):
- Medium: X
- Location:
- Lisbon, Portugal
- Sponsoring Org:
- National Science Foundation
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