[14] Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, and Yoshua Bengio. Data-driven approach to encoding and decoding 3-D crystal structures. arXiv preprint arXiv:1909.00949, 2019.
[15] Emiel Hoogeboom, Víctor Garcia Satorras, Clément Vignac, and Max Welling. Equivariant diffusion for molecule generation in 3D. 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 8867-8887. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/hoogeboom22a.html.
[16] John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, and Gevorg Grigoryan. Illuminating protein space with a programmable generative model. bioRxiv, 2022. doi: 10.1101/2022.12.01.518682. URL https://www.biorxiv.org/content/early/2022/12/02/2022.12.01.518682.
[17] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation.  $APL$  materials, 1(1):011002, 2013.
[18] Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, and Yang Liu. Crystal structure prediction by joint equivariant diffusion on lattices and fractional coordinates. In Workshop on "Machine Learning for Materials" ICLR 2023, 2023. URL https://openreview.net/forum?id=VPByphdu24j.
[19] Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Junction tree variational autoencoder for molecular graph generation. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 2323-2332, 2018.
[20] Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi S. Jaakkola. Torsional diffusion for molecular conformer generation. 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=w6fj2r62r_H.
[21] Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, and Yousung Jung. Generative adversarial networks for crystal structure prediction. ACS central science, 6(8):1412-1420, 2020.
[22] Diederik P Kingma and Max Welling. Auto-Encoding variational bayes. In 2nd International Conference on Learning Representations, 2014.
[23] Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. On density estimation with diffusion models. 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=2LdBqxc1Yv.
[24] Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. DiffWave: A versatile diffusion model for audio synthesis. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=a-xFK8Ymz5J.
[25] Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, and Shuiwang Ji. Efficient approximations of complete interatomic potentials for crystal property prediction. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 21260-21287. PMLR, 23-29 Jul 2023. URL https://proceedings.mlr.press/v202/lin23m.html.
[26] 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 M Oztekin, Xuan Zhang, and Shuiwang Ji. DIG: A turnkey library for diving into graph deep learning research. Journal of Machine Learning Research, 22(240):1-9, 2021. URL http://jmlr.org/papers/v22/21-0343.html.