1 citations · 1 across the 1 of their papers we have counts for
3 papers
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…
How big is Big Data?
Daniel T. Speckhard, Tim Bechtel, Luca M. Ghiringhelli +3
Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {\it big} means in the context of typical materials-science machi…
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…