872 citations · 2.9k across the 35 of their papers we have counts for
5 papers · 1 filter
Rapid Exploration of Optimization Strategies on Advanced Architectures using TestSNAP and LAMMPS
Rahulkumar Gayatri, Stan Moore, Evan Weinberg +5
The exascale race is at an end with the announcement of the Aurora and Frontier machines. This next generation of supercomputers utilize diverse hardware architectures to achieve t…
Simple and efficient algorithms for training machine learning potentials to force data
Justin S. Smith, Nicholas Lubbers, Aidan P. Thompson +1
Abstract Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is…
Modeling nanoconfinement effects using active learning
Javier E. Santos, Mohammed Mehana, Hao Wu +5
Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge…
Automated discovery of a robust interatomic potential for aluminum
Justin S. Smith, Benjamin Nebgen, Nithin Mathew +8
Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM…
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu +1
In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of PDEs is enforcing…