[27] Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji. GraphEBM: Molecular graph generation with energy-based models. In Energy Based Models Workshop-ICLR 2021, 2021.
[28] Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji. Generating 3D molecules for target protein binding. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 13912-13924. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/liu22m.html.
[29] Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji. Spherical message passing for 3D molecular graphs. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=givsRXsOt9r.
[30] Shitong Luo, Chence Shi, Minkai Xu, and Jian Tang. Predicting molecular conformation via dynamic graph score matching. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 19784-19795. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper/2021/file/a45a1d12ee0fb7f1f872ab91da18f899-Paper.pdf.
[31] Youzhi Luo and Shuiwang Ji. An autoregressive flow model for 3D molecular geometry generation from scratch. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=C03Ajc-NS5W.
[32] Youzhi Luo, Keqiang Yan, and Shuiwang Ji. GraphDF: A discrete flow model for molecular graph generation. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 7192-7203. PMLR, 18-24 Jul 2021.
[33] Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho. Molecular geometry prediction using a deep generative graph neural network. Scientific reports, 9(1):1-13, 2019.
[34] Yashaswi Pathak, Karandeep Singh Juneja, Girish Varma, Masahiro Ehara, and U Deva Priyakumar. Deep learning enabled inorganic material generator. Physical Chemistry Chemical Physics, 22(46):26935-26943, 2020.
[35] Chris J. Pickard. Airss data for carbon at 10gpa and the c+n+h+o system at 1gpa, 2020. URL: https://archive/materialscloud.org/record/2020.0026/v1.
[36] Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G. Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, and Tonio Buonassisi. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. Matter, 5(1):314-335, 2022. ISSN 2590-2385. doi: https://doi.org/10.1016/j.matt.2021.11.032. URL https://www.sciencedirect.com/science/article/pii/S2590238521006251.
[37] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022.
[38] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo-Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi. Photorealistic text-to-image diffusion models with deep language understanding. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=08Yk-n512Al.
[39] Victor Garcia Satorras, Emiel Hoogeboom, Fabian Bernd Fuchs, Ingmar Posner, and Max Welling. E(n) equivariant normalizing flows. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=N5hQI RowsVA.