73 citations · 250 across the 39 of their papers we have counts for
5 papers · 1 filter
Predicting electronic structures at any length scale with machine learning
Lenz Fiedler, Normand A. Modine, Steve Schmerler +5
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects…
PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms
Abdurrahman Yasar, Sivasankaran Rajamanickam, Jonathan W. Berry +1
Designing flexible graph kernels that can run well on various platforms is a crucial research problem due to the frequent usage of graphs for modeling data and recent architectural…
Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening
Brian Kelley, Sivasankaran Rajamanickam
Given a graph, finding the distance-2 maximal independent set (MIS-2) of the vertices is a problem that is useful in several contexts such as algebraic multigrid coarsening or mult…
Training-free hyperparameter optimization of neural networks for electronic structures in matter
Lenz Fiedler, Nils Hoffmann, Parvez Mohammed +6
A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has…
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya +5
Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common ch…