15 citations · 20 across the 4 of their papers we have counts for
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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.LG2023★ 1 cited
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…