7 citations · 9 across the 5 of their papers we have counts for
5 papers
Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances
Joseph Musielewicz, Janice Lan, Matt Uyttendaele +1
Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theor…
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Brook Wander, Muhammed Shuaibi, John R. Kitchin +2
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural networ…
Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials
Xiaoxiao Wang, Joseph Musielewicz, Richard Tran +6
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput e…
Chemical Properties from Graph Neural Network-Predicted Electron Densities
Ethan M. Sunshine, Muhammed Shuaibi, Zachary W. Ulissi +1
According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this wo…
WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions
Rohan Yuri Sanspeur, Javier Heras-Domingo, John R. Kitchin +1
This paper introduces WhereWulff, a semi-autonomous workflow for modeling the reactivity of catalyst surfaces. The workflow begins with a bulk optimization task that takes an initi…