(2026) Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, and Tess Smidt. PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials. arXiv preprint arXiv:2601.07742.

(2025) Ameya Daigavane, YuQing Xie, Bodhi Vani, Saeed Saremi, Joseph Kleinhenz, and Tess Smidt. Matching the Optimal Denoiser in Point Cloud Diffusion with (Improved) Rotational Alignment. arXiv preprint arXiv:2510.03335.

(2025) Hannah Lawrence, Elyssa Hofgard, Vasco Portilho, Yuxuan Chen, Tess Smidt, and Robin Walters. To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking. arXiv preprint arXiv:2510.01349.

(2025) Julia Balla, Jeremiah Bailey, Ali Backour, Elyssa Hofgard, Tommi Jaakkola, Tess Smidt, and Ryley McConkey. Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution. NeurIPS 2025 Machine Learning and the Physical Sciences Workshop.

(2025) Allan dos Santos Costa, Manvitha Ponnapati, Dana Rubin, Tess Smidt, and Joseph Jacobson. Accelerating Protein Molecular Dynamics Simulation with DeepJump. NeurIPS 2025 AI for Science Workshop.

(2025) Teddy Koker, Mit Kotak, and Tess Smidt. Training a Foundation Model for Materials on a Budget. NeurIPS 2025 AI for Materials Workshop.

(2025) YuQing Xie, Ameya Daigavane, Mit Kotak, and Tess Smidt. The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products. International Conference on Machine Learning.

(2025) YuQing Xie and Tess Smidt. A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models. Advances in Neural Information Processing Systems.

(2025) Chun Wei Tan, Marc Descoutaux, Mit Kotak, Gabriel de Miranda Nascimento, Sean Kavanagh, Laura Zichi, Menghang Wang, Aadit Saluja, Yizhong Hu, Tess Smidt, Anders Johansson, William Witt, Boris Kozinsky, and Albert Musaelian. High-performance training and inference for deep equivariant interatomic potentials. arXiv preprint arXiv:2504.16068.

(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. Transactions on Machine Learning Research.

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

(2023) Ameya Daigavane, Song Kim, Mario Geiger, and Tess Smidt. Symphony: Symmetry-equivariant point-centered spherical harmonics for 3d molecule generation. International Conference on Learning Representations.

(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.

(2024) Yi-Lun Liao, Brandon Wood, Abhishek Das, and Tess Smidt. Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations. International Conference on Learning Representations.

(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. International Conference on Learning Representations.

(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. International Conference on Machine Learning.

(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.