11 citations · 11 across the 4 of their papers we have counts for
4 papers
Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
YuQing Xie, Ameya Daigavane, Mit Kotak +1
-equivariant neural networks have proven to be effective in a wide range of 3D modeling tasks. A fundamental operation of such networks is the tensor product, which allows in…
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
Teddy Koker, Abhijeet Gangan, Mit Kotak +2
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on ene…
The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products
YuQing Xie, Ameya Daigavane, Mit Kotak +1
-equivariant neural networks have demonstrated success across a wide range of 3D modelling tasks. A fundamental operation in these networks is the tensor product, which inter…
High-performance training and inference for deep equivariant interatomic potentials
Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak +11
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in a…