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ORCIDTess E. Smidt
Person information
- affiliation: Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, USA
- affiliation: Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- affiliation (PhD 2018): University of California, Berkeley, CA, USA
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2020 – today
- 2024
[c9]Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt:
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation. ICLR 2024- [c8]Yi-Lun Liao, Brandon M. Wood, Abhishek Das, Tess E. Smidt:
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. ICLR 2024 - [c7]Rui Wang, Elyssa F. Hofgard, Hang Gao, Robin Walters, Tess E. Smidt:
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution. ICML 2024 - [i23]Yuqing Xie, Tess E. Smidt:
Equivariant Symmetry Breaking Sets. CoRR abs/2402.02681 (2024) - [i22]Yi-Lun Liao, Tess E. Smidt, Abhishek Das:
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields. CoRR abs/2403.09549 (2024) - [i21]Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola:
A Recipe for Charge Density Prediction. CoRR abs/2405.19276 (2024) - [i20]Elyssa F. Hofgard, Rui Wang, Robin Walters, Tess E. Smidt:
Relaxed Equivariant Graph Neural Networks. CoRR abs/2407.20471 (2024) - [i19]Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess E. Smidt, Emine Küçükbenli, Joseph Jacobson:
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants. CoRR abs/2410.09667 (2024) - [i18]Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lázaro, Tommi S. Jaakkola, Tess E. Smidt:
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. CoRR abs/2410.20516 (2024) - 2023
- [j2]Joshua A. Rackers
, Lucas Tecot, Mario Geiger, Tess E. Smidt:
A recipe for cracking the quantum scaling limit with machine learned electron densities. Mach. Learn. Sci. Technol. 4(1): 15027 (2023) - [c6]Yi-Lun Liao, Tess E. Smidt:
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. ICLR 2023 - [c5]Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR 2023 - [c4]Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner:
A General Framework for Equivariant Neural Networks on Reductive Lie Groups. NeurIPS 2023 - [i17]Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner:
A General Framework for Equivariant Neural Networks on Reductive Lie Groups. CoRR abs/2306.00091 (2023) - [i16]Yi-Lun Liao, Brandon M. Wood, Abhishek Das, Tess E. Smidt:
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. CoRR abs/2306.12059 (2023) - [i15]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i14]Rui Wang, Robin Walters, Tess E. Smidt:
Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems. CoRR abs/2310.02299 (2023) - [i13]Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess E. Smidt, Joseph Jacobson:
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders. CoRR abs/2310.02508 (2023) - [i12]Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt:
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. CoRR abs/2311.16199 (2023) - 2022
- [j1]Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels:
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks. J. Mach. Learn. Res. 23: 303:1-303:28 (2022) - [c3]Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli:
Generative Coarse-Graining of Molecular Conformations. ICML 2022: 23213-23236 - [c2]Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels:
Deep Learning and Spectral Embedding for Graph Partitioning. PP 2022: 25-36 - [i11]Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli:
Generative Coarse-Graining of Molecular Conformations. CoRR abs/2201.12176 (2022) - [i10]Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. CoRR abs/2202.13013 (2022) - [i9]Yi-Lun Liao, Tess E. Smidt:
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. CoRR abs/2206.11990 (2022) - [i8]Mario Geiger, Tess E. Smidt:
e3nn: Euclidean Neural Networks. CoRR abs/2207.09453 (2022) - [i7]Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho:
Learning Integrable Dynamics with Action-Angle Networks. CoRR abs/2211.15338 (2022) - 2021
- [c1]Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller:
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. NeurIPS 2021: 14434-14447 - [i6]Simon L. Batzner, Tess E. Smidt, Lixin Sun, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Boris Kozinsky:
SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. CoRR abs/2101.03164 (2021) - [i5]Alice Gatti, Zhixiong Hu, Pieter Ghysels, Esmond G. Ng, Tess E. Smidt:
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning. CoRR abs/2104.03546 (2021) - [i4]Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels:
Deep Learning and Spectral Embedding for Graph Partitioning. CoRR abs/2110.08614 (2021) - 2020
- [i3]Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller:
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. CoRR abs/2007.02005 (2020) - [i2]Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé:
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR abs/2008.08461 (2020)
2010 – 2019
- 2018
- [b1]Tess E. Smidt:
Toward the Systematic Design of Complex Materials from Structural Motifs. University of California, Berkeley, USA, 2018 - [i1]Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley:
Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds. CoRR abs/1802.08219 (2018)
Coauthor Index
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