16 citations · 46 across the 13 of their papers we have counts for
4 papers · 2 filters
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.…
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
Suresh Bishnoi, Ravinder Bhattoo, Jayadeva +2
The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derive…
Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics
Suresh Bishnoi, Jayadeva, Sayan Ranu +1
Neural networks (NNs) that exploit strong inductive biases based on physical laws and symmetries have shown remarkable success in learning the dynamics of physical systems directly…
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry +2
Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, ma…