[16] Pierre-Paul De Breuck, Matthew L Evans, and Gian-Marco Rignanese. Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on modnet. Journal of Physics: Condensed Matter, 33(40):404002, 2021. 27
[17] Mehmet F Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, and Yingyu Liang. Attentive walk-aggregating graph neural networks. TMLR, 2022. 1, 6, 21
[18] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 6
[19] Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, and Zhi-Ming Ma. A new perspective on building efficient and expressive 3d equivariant graph neural networks, 2023. 29, 32, 33, 37
[20] 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, 2022. 5, 10, 29, 31, 32, 33
[21] Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain. Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm. arXiv.org, 6, 2020. 9, 27, 51
[22] Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain. Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm. npj Computational Materials, 6(1):138, 2020. 27, 48
[23] David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gomez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams. Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292, 2015. 1, 6, 21
[24] Nadav Dym and Haggai Maron. On the universality of rotation equivariant point cloud networks. arXiv preprint arXiv:2010.02449, 2020. 33
[25] Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, and Heng Ji. Translation between molecules and natural language. arXiv preprint arXiv:2204.11817, 2022. 10
[26] Carl Edwards, ChengXiang Zhai, and Heng Ji. Text2mol: Cross-modal molecule retrieval with natural language queries. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 595-607, 2021. 10
[27] Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Wang Yu, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, and Burkhard Rost. Protrans: Towards cracking the language of lives code through self-supervised deep learning and high performance computing. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 1
[28] Thomas Engel and Johann Gasteiger. Applied chemoinformatics: achievements and future opportunities. John Wiley & Sons, 2018. 50
[29] Hehe Fan, Zhangyang Wang, Yi Yang, and Mohan Kankanhalli. Continuous-discrete convolution for geometry-sequence modeling in proteins. In The Eleventh International Conference on Learning Representations, 2023. 9, 31, 34, 37, 50, 51
[30] Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang. Chemr-gem: Geometry enhanced molecular representation learning for property prediction. arXiv preprint arXiv:2106.06130, 2021. 3
[31] Matthias Fey and Jan E. Lenssen. Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019. 2
[32] Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson. Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data. In International Conference on Machine Learning, pages 3165-3176. PMLR, 2020. 5, 32
[33] Daniel Flam-Shepherd and Alán Aspuru-Guzik. Language models can generate molecules, materials, and protein binding sites directly in three dimensions as xyz, cif, and pdb files. arXiv preprint arXiv:2305.05708, 2023. 1, 32