Callum J Court, Batuhan Yildirim, Apoorv Jain, and Jacqueline M Cole. 3-d inorganic crystal structure generation and property prediction via representation learning. Journal of Chemical Information and Modeling, 60(10):4518-4535, 2020.
Nils ER Zimmermann and Anubhav Jain. Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity. RSC advances, 10(10): 6063-6081, 2020.
Logan Ward, Ankit Agrawal, Alok Choudhary, and Christopher Wolverton. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2(1):1-7, 2016.
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development. Advances in neural information processing systems, 2021.
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Genisses, Aneesh S Pappu, Karl Leswing, and Vijay Pande. Moleculenet: a benchmark for molecular machine learning. Chemical science, 9(2):513-530, 2018.
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller. Machine learning of accurate energy-conserving molecular force fields. Science advances, 3(5):e1603015, 2017.
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller. Schnet-a deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148(24):241722, 2018.
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. E (n) equivariant graph neural networks. In International conference on machine learning, pages 9323-9332. PMLR, 2021.
Johannes Gasteiger, Shankari Giri, Johannes T Marggraf, and Stephan Gunnemann. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. arXiv preprint arXiv:2011.14115, 2020.
Johannes Gasteiger, Florian Becker, and Stephan Gunnemann. Gemnet: Universal directional graph neural networks for molecules. Advances in Neural Information Processing Systems, 34: 6790-6802, 2021.
Yi-Lun Liao and Tess Smidt. Equifomer: Equivariant graph attention transformer for 3d atomistic graphs.
Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, and Zhi-Ming Ma. Efficient and expressive equivariant graph neural networks. under review, 2023.
Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, and Tie-Yan Liu. Se (3) equivariant graph neural networks with complete local frames. In International Conference on Machine Learning, pages 5583-5608. PMLR, 2022b.
Logan Ward, Alexander Dunn, Alireza Faghaninia, Nils ER Zimmermann, Saurabh Bajaj, Qi Wang, Joseph Montoya, Jiming Chen, Kyle Bystrom, Maxwell Dylla, et al. Matminer: An open source toolkit for materials data mining. Computational Materials Science, 152:60-69, 2018.
Ryan Jacobs, Tam Mayeshiba, Ben Afflerbach, Luke Miles, Max Williams, Matthew Turner, Raphael Finkel, and Dane Morgan. The materials simulation toolkit for machine learning (mast-ml): An automated open source toolkit to accelerate data-driven materials research. Computational Materials Science, 176:109544, 2020.
Jun Xia, Yanqiao Zhu, Yuanqi Du, and Stan Z Li. A systematic survey of chemical pre-trained models.
Yingheng Wang, Yaosen Min, Erzhuo Shao, and Ji Wu. Molecular graph contrastive learning with parameterized explainable augmentations. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1558-1563. IEEE, 2021a.