2 citations · 5 across the 7 of their papers we have counts for
7 papers
TriForces: Augmenting Atomistic GNNs for Transferable Representations
Ali Ramlaoui, Alexandre Duval, Hannah Bull +4
Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in practice, they must often be…
LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling
Ali Ramlaoui, Martin Siron, Inel Djafar +4
The development of accurate machine learning interatomic potentials (MLIPs) is limited by the fragmented availability and inconsistent formatting of quantum mechanical trajectory d…
Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis
Brook Wander, Joseph Musielewicz, Raffaele Cheula +1
Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that…
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…
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…
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
Yuge Hu, Joseph Musielewicz, Zachary Ulissi +1
Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and…