22 citations · 59 across the 16 of their papers we have counts for
6 papers · 1 filter
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
Tirtha Vinchurkar, Kareem Abdelmaqsoud, John R. Kitchin
Machine-learned potentials (MLPs) have revolutionized materials discovery by providing accurate and efficient predictions of molecular and material properties. Graph Neural Network…
A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations
Laurens R. Lueg, Victor Alves, Daniel Schicksnus +3
Scientific machine learning is an emerging field that broadly describes the combination of scientific computing and machine learning to address challenges in science and engineerin…
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…
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
Adeesh Kolluru, John R Kitchin
Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for th…
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John R. Kitchin +3
Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has b…