Publications

[Foundations and Trends in Machine Learning 25] Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, ..., Tommi Jaakkola, Connor W Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji

A 263-page AI4Sience survey paper by 63 authors from 14 institutions, including 41 figures and 36 tables. Keqiang Yan is the lead author of Protein Backbone Structure Generation and Material Representation Learning (Sec. 6.4 and 7.2).

[Paper][Code (more than 480 stars)][Website]

[NeurIPS 24] Invariant Tokenization for Language Model Enabled Crystal Materials Generation

Keqiang Yan, Xiner Li, Hongyi Ling, Kenna Ashen, Carl Edwards, Raymundo Arroyave, Marinka Zitnik, Heng Ji, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

[Paper][Code]

[ICML 24] A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction

Keqiang Yan, Alexandra Saxton, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

Proceedings of the 41th International Conference on Machine Learning (ICML), 2024

[Paper][Code]

[ICLR 24] Complete and Efficient Graph Transformers for Crystal Material Property Prediction

Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian and Shuiwang Ji

The Twelfth International Conference on Learning Representations (ICLR), 2024

[Paper][Code]

[LoG 23] A Latent Diffusion Model for Protein Structure Generation

Cong Fu*, Keqiang Yan*, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian and Shuiwang Ji

Learning on Graphs Conference (LoG), 2023

[Paper][Code]

[Science Advances] Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity

Sheng Gong, Keqiang Yan, Tian Xie, Yang Shao-Horn, Rafael Gomez-Bombarelli, Shuiwang Ji and Jeffrey C. Grossman

Science Advances, 2023

[Paper]

[Npj Computational Materials] Large Scale Benchmark of Materials Design Methods

Kamal Choudhary, Daniel Wines, Kangming Li, Kevin F Garrity, Vishu Gupta, Aldo H Romero, Jaron T Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh, Paul RC Kent, Keqiang Yan, ..., Andrew Dale Rohskopf, Jason Hattrick-Simpers, Shih-Han Wang, Luke EK Achenie, Hongliang Xin, Maureen Williams, Adam J Biacchi, Francesca Tavazza

Npj Computational Materials, 2023

[Paper][Code]

[ICML 23] Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian and Shuiwang Ji

Proceedings of the 40th International Conference on Machine Learning (ICML), 2023

[Paper][Code]

[NeurIPS 22] Periodic Graph Transformers for Crystal Material Property Prediction

Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji

Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022

[Paper][Code]

[JMLR 21] DIG: A Turnkey Library for Diving into Graph Deep Learning Research

Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji

Journal of Machine Learning Research (JMLR), 2021

[Paper][Code (more than 1.9k stars)][Documentation]

[ICML 21] GraphDF: A Discrete Flow Model for Molecular Graph Generation

Youzhi Luo, Keqiang Yan, and Shuiwang Ji

The 38th International Conference on Machine Learning (ICML), 2021

[Paper][Code]

[ICLR-W 21] GraphEBM: Molecular Graph Generation with Energy-Based Models

Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji

EBM Workshop at ICLR, 2021

[Paper][Code]

[ICME 20] Multitask attentive network for text effects quality assessment

Keqiang Yan, Shuai Yang, Wenjing Wang and Jiaying Liu

IEEE International Conference on Multimedia and Expo (ICME), 2020

[Paper]