59 citations · 262 across the 14 of their papers we have counts for
8 papers · 1 filter
A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
Keqiang Yan, Montgomery Bohde, Andrii Kryvenko +10
Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLI…
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
Keqiang Yan, Xiner Li, Hongyi Ling +8
We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior…
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Cong Fu, Xiaofeng Qian +2
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 crys…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Yi Liu, Yuchao Lin +1
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…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo, Keqiang Yan, Shuiwang Ji
We consider the problem of molecular graph generation using deep models. While graphs are discrete, most existing methods use continuous latent variables, resulting in inaccurate m…
GraphEBM: Molecular Graph Generation with Energy-Based Models
Meng Liu, Keqiang Yan, Bora Oztekin +1
We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative…