793 citations · 1.7k across the 34 of their papers we have counts for
3 papers · 2 filters
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…
Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati +6
The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in inter…
Spherical Channels for Modeling Atomic Interactions
C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru +5
Modeling the energy and forces of atomic systems is a fundamental problem in computational chemistry with the potential to help address many of the world's most pressing problems,…