203 citations · 205 across the 3 of their papers we have counts for
3 papers
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
Daniel T. Speckhard, Tim Bechtel, Sebastian Kehl +2
Graph neural networks (GNN) have shown promising results for several domains such as materials science, chemistry, and the social sciences. GNN models often contain millions of par…
Band-gap regression with architecture-optimized message-passing neural networks
Tim Bechtel, Daniel T. Speckhard, Jonathan Godwin +1
Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we trai…