(2024) Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, and Tommi Jaakkola. A recipe for charge density prediction. Advances in Neural Information Processing Systems, 37:9727–9752.

(2024) Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro, Tommi Jaakkola, and Tess Smidt. A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. arXiv preprint arXiv:2410.20516.

(2024) Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess Smidt, Emine Kucukbenli, and Joseph Jacobson. EquiJump: Protein Dynamics Simulation via SO (3)-Equivariant Stochastic Interpolants. arXiv preprint arXiv:2410.09667.

(2024) Elyssa Hofgard, Rui Wang, Robin Walters, and Tess Smidt. Relaxed Equivariant Graph Neural Networks. arXiv preprint arXiv:2407.20471.

(2024) Yi-Lun Liao, Tess Smidt, Muhammed Shuaibi, and Abhishek Das. Generalizing denoising to non-equilibrium structures improves equivariant force fields. arXiv preprint arXiv:2403.09549.

(2024) YuQing Xie and Tess Smidt. Equivariant symmetry breaking. Bulletin of the American Physical Society.

(2024) YuQing Xie, Ameya Daigavane, Mit Kotak, and Tess Smidt. The price of freedom: Exploring tradeoffs between expressivity and computational efficiency in equivariant tensor products. ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling.

(2023) Ameya Daigavane, Song Kim, Mario Geiger, and Tess Smidt. Symphony: Symmetry-equivariant point-centered spherical harmonics for 3d molecule generation. arXiv preprint arXiv:2311.16199.

(2023) Allan Dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess Smidt, and Joseph Jacobson. Ophiuchus: Scalable modeling of protein structures through hierarchical coarse-graining SO (3)-equivariant autoencoders. arXiv preprint arXiv:2310.02508.

(2023) Rui Wang, Elyssa Hofgard, Han Gao, Robin Walters, and Tess E Smidt. Discovering symmetry breaking in physical systems with relaxed group convolution. arXiv preprint arXiv:2310.02299.

(2023) Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess Smidt, and Joseph Jacobson. Ophiuchus: Scalable modeling of protein structures through hierarchical coarse-graining SO (3)-equivariant autoencoders. arXiv e-prints:arXiv–2310.

(2023) Yi-Lun Liao, Brandon Wood, Abhishek Das, and Tess Smidt. Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations. arXiv preprint arXiv:2306.12059.

(2023) Joshua A Rackers, Lucas Tecot, Mario Geiger, and Tess E Smidt. A recipe for cracking the quantum scaling limit with machine learned electron densities. Machine Learning: Science and Technology, 4(1):015027.

(2023) Rui Wang, Robin Walters, and Tess Smidt. Relaxed octahedral group convolution for learning symmetry breaking in 3d physical systems. NeurIPS 2023 AI for Science Workshop.

(2022) Ameya Daigavane, Arthur Kosmala, Miles Cranmer, Tess Smidt, and Shirley Ho. Learning integrable dynamics with action-angle networks. arXiv preprint arXiv:2211.15338.

(2022) Helena A Merker, Harry Heiberger, Linh Nguyen, Tongtong Liu, Zhantao Chen, Nina Andrejevic, Nathan C Drucker, Ryotaro Okabe, Song Eun Kim, Yao Wang, Tess Smidt, and Mingda Li. Machine learning magnetism classifiers from atomic coordinates. iScience, 25(10).

(2022) Mario Geiger and Tess Smidt. e3nn: Euclidean neural networks. arXiv preprint arXiv:2207.09453.

(2022) Yi-Lun Liao and Tess Smidt. Equiformer: Equivariant graph attention transformer for 3d atomistic graphs. arXiv preprint arXiv:2206.11990.

(2022) Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky. E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature communications, 13(1):2453.

(2022) Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, and Rafael Gómez-Bombarelli. Generative coarse-graining of molecular conformations. arXiv preprint arXiv:2201.12176.

(2022) Joshua A Rackers, Lucas Tecot, Mario Geiger, and Tess E Smidt. Cracking the quantum scaling limit with machine learned electron densities. arXiv preprint arXiv:2201.03726.

(2021) Oliver Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, and Klaus-Robert Müller. SE (3)-equivariant prediction of molecular wavefunctions and electronic densities. Advances in Neural Information Processing Systems, 34:14434–14447.

(2021) Tess Smidt. e3nn tutorial v0. 2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States).

(2021) Tess E Smidt. Euclidean symmetry and equivariance in machine learning. Trends in Chemistry, 3(2):82–85.

(2021) Tess E Smidt, Mario Geiger, and Benjamin Kurt Miller. Finding symmetry breaking order parameters with euclidean neural networks. Physical Review Research, 3(1):L012002.

(2020) Kostiantyn Lapchevskyi, Benjamin Miller, Mario Geiger, and Tess Smidt. Euclidean neural networks (e3nn) v1. 0. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States).

(2020) Benjamin Kurt Miller, Mario Geiger, Tess E Smidt, and Frank Noé. Relevance of rotationally equivariant convolutions for predicting molecular properties. arXiv preprint arXiv:2008.08461.