Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds. - Nathaniel Thomas; Tess E. Smidt; Steven Kearnes; Lusann Yang; Li Li 060; Kai Kohlhoff; Patrick Riley 001 (CoRR, 2018)

Toward the Systematic Design of Complex Materials from Structural Motifs. - Tess E. Smidt (2018)

Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. - Tess E. Smidt; Mario Geiger; Benjamin Kurt Miller (CoRR, 2020)

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. - Benjamin Kurt Miller; Mario Geiger; Tess E. Smidt; Frank Noé (CoRR, 2020)

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. - Oliver T. Unke; Mihail Bogojeski; Michael Gastegger; Mario Geiger; Tess E. Smidt; Klaus-Robert Müller (NeurIPS, 2021)

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. - Simon L. Batzner; Tess E. Smidt; Lixin Sun; Jonathan P. Mailoa; Mordechai Kornbluth; Nicola Molinari; Boris Kozinsky (CoRR, 2021)

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning. - Alice Gatti; Zhixiong Hu; Pieter Ghysels; Esmond G. Ng; Tess E. Smidt (CoRR, 2021)

Generative Coarse-Graining of Molecular Conformations. - Wujie Wang; Minkai Xu; Chen Cai; Benjamin Kurt Miller; Tess E. Smidt; Yusu Wang 001; Jian Tang 005; Rafael Gómez-Bombarelli (ICML, 2022)

Deep Learning and Spectral Embedding for Graph Partitioning. - Alice Gatti; Zhixiong Hu; Tess E. Smidt; Esmond G. Ng; Pieter Ghysels (PP, 2022)

e3nn: Euclidean Neural Networks. - Mario Geiger; Tess E. Smidt (CoRR, 2022)

Learning Integrable Dynamics with Action-Angle Networks. - Ameya Daigavane; Arthur Kosmala; Miles D. Cranmer; Tess E. Smidt; Shirley Ho (CoRR, 2022)

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks. - Alice Gatti; Zhixiong Hu; Tess E. Smidt; Esmond G. Ng; Pieter Ghysels (J. Mach. Learn. Res., 2022)

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. - Yi-Lun Liao; Tess E. Smidt (ICLR, 2023)

Sign and Basis Invariant Networks for Spectral Graph Representation Learning. - Derek Lim; Joshua David Robinson; Lingxiao Zhao; Tess E. Smidt; Suvrit Sra; Haggai Maron; Stefanie Jegelka (ICLR, 2023)

A General Framework for Equivariant Neural Networks on Reductive Lie Groups. - Ilyes Batatia; Mario Geiger; Jose M. Munoz; Tess E. Smidt; Lior Silberman; Christoph Ortner (NeurIPS, 2023)

Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems. - Rui Wang 086; Robin Walters 001; Tess E. Smidt (CoRR, 2023)

Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders. - Allan dos Santos Costa; Ilan Mitnikov; Mario Geiger; Manvitha Ponnapati; Tess E. Smidt; Joseph Jacobson (CoRR, 2023)

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. - Ameya Daigavane; Song Kim; Mario Geiger; Tess E. Smidt (CoRR, 2023)

A recipe for cracking the quantum scaling limit with machine learned electron densities. - Joshua A. Rackers; Lucas Tecot; Mario Geiger; Tess E. Smidt (Mach. Learn. Sci. Technol., 2023)

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation. - Ameya Daigavane; Song Kim; Mario Geiger; Tess E. Smidt (ICLR, 2024)

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. - Yi-Lun Liao; Brandon M. Wood; Abhishek Das; Tess E. Smidt (ICLR, 2024)

Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution. - Rui Wang 086; Elyssa F. Hofgard; Hang Gao 004; Robin Walters 001; Tess E. Smidt (ICML, 2024)

A Recipe for Charge Density Prediction. - Xiang Fu 005; Andrew S. Rosen; Kyle Bystrom; Rui Wang 086; Albert Musaelian; Boris Kozinsky; Tess E. Smidt; Tommi S. Jaakkola (NeurIPS, 2024)

Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields. - Yi-Lun Liao; Tess E. Smidt; Abhishek Das (CoRR, 2024)

Relaxed Equivariant Graph Neural Networks. - Elyssa F. Hofgard; Rui Wang 086; Robin Walters 001; Tess E. Smidt (CoRR, 2024)

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

A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. - Julia Balla; Siddharth Mishra-Sharma; Carolina Cuesta-Lázaro; Tommi S. Jaakkola; Tess E. Smidt (CoRR, 2024)

Equivariant Symmetry Breaking Sets. - Yuqing Xie 006; Tess E. Smidt (Trans. Mach. Learn. Res., 2024)

Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields. - Yi-Lun Liao; Tess E. Smidt; Muhammed Shuaibi; Abhishek Das (Trans. Mach. Learn. Res., 2024)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

High-performance training and inference for deep equivariant interatomic potentials. - Chuin Wei Tan; Marc L. Descoteaux; Mit Kotak; Gabriel de Miranda Nascimento; Seán R. Kavanagh; Laura Zichi; Menghang Wang; Aadit Saluja; Yizhong R. Hu; Tess E. Smidt; Anders Johansson; William C. Witt; et al. (CoRR, 2025)

A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models. - Yuqing Xie 006; Tess E. Smidt (CoRR, 2025)

The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products. - Yuqing Xie 006; Ameya Daigavane; Mit Kotak; Tess E. Smidt (CoRR, 2025)