Periodic Graph Transformers for Crystal Material Property Prediction
Nov 28, 2022
Speakers
Keqiang Yan
Speaker · 0 followers
Yuchao Lin
Speaker · 0 followers
Shuiwang Ji
Speaker · 0 followers
About
We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a regular lattice in 3D space. How to effectively encode these periodic structures poses unique challenges not present in regular graph representation learning. In addition to being E(3) invariant, periodic graph representations need to be periodic invariant. That is, the learned representations should be invariant to shifts of cell boundaries as they are artificially imposed. Furthermore, the periodic repeating patterns need to be captured explicitly as lattices of different sizes and orientations may correspond to different materials. In this work, we propose a transformer architecture, known as Matformer, for periodic graph representation learning. Our Matformer is designed to be invariant to periodicity and can capture repeating patterns explicitly. In particular, Matformer encodes periodic patterns by efficient use of geometric distances between the same atoms in neighboring cells. Experimental results on multiple common benchmark datasets show that our Matformer outperforms baseline methods consistently. In addition, our results demonstrate the importance of periodic invariance and explicit repeating pattern encoding for crystal representation learning.We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a regular lattice in 3D space. How to effectively encode these periodic structures poses unique challenges not present in regular graph representation learning. In addition to being E(3) invariant, periodic graph representations need to be periodic invariant. That is, the learned representations should be invariant to…
Organizer
NeurIPS 2022
Account · 962 followers
Like the format? Trust SlidesLive to capture your next event!
Professional recording and live streaming, delivered globally.
Sharing
Recommended Videos
Presentations on similar topic, category or speaker
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Watch later
Dandan Guo, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Watch later
Zifeng Ding, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Addressing Resource Scarcity across Sign Languages with Multilingual Pretraining and Unified-Vocabulary Datasets
Watch later
Gokul NC, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
![[Re] Variational Fair Clustering](https://ma.slideslive.com/library/presentations/38992710/thumbnail/re-variational-fair-clustering_7p0Fbd_small.jpg)
[Re] Variational Fair Clustering
Watch later
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Offline RL in the context of Collect and Infer Offline RL as a launchpad
Watch later
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Large-Scale Differentiable Causal Discovery of Factor Graphs
Watch later
Romain Lopez, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%