1 citations · 2 across the 4 of their papers we have counts for
4 papers
AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms
Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang +5
We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution…
Implicit Delta Learning of High Fidelity Neural Network Potentials
Stephan Thaler, Cristian Gabellini, Nikhil Shenoy +1
Neural network potentials (NNPs) offer a fast and accurate alternative to ab-initio methods for molecular dynamics (MD) simulations but are hindered by the high cost of training da…
ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
Majdi Hassan, Nikhil Shenoy, Jungyoon Lee +3
Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches eit…
Role of Structural and Conformational Diversity for Machine Learning Potentials
Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi +3
In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity,…