2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 2 cited
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan +7
Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries.…
cs.LG2021★ 1 cited
Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias
Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Realistic models of physical world rely on differentiable symmetries that, in turn, correspond to conservation laws. Recent works on Lagrangian and Hamiltonian neural networks show…