1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
OpenQDC: Open Quantum Data Commons
Cristian Gabellini, Nikhil Shenoy, Stephan Thaler +5
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and fo…
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,…
A Case for Rejection in Low Resource ML Deployment
Jerome White, Pulkit Madaan, Nikhil Shenoy +3
Building reliable AI decision support systems requires a robust set of data on which to train models; both with respect to quantity and diversity. Obtaining such datasets can be di…