Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls

15 December 2022

Papers citing "Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls"

5 / 5 papers shown

Title
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks Tim Rensmeyer Benjamin Craig D. Kramer Oliver Niggemann BDL 22 3 0 05 Apr 2023
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification Youngseog Chung I. Char Han Guo J. Schneider W. Neiswanger 30 70 0 21 Sep 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 192 1,232 0 08 Jan 2021
Coarse Graining Molecular Dynamics with Graph Neural Networks B. Husic N. Charron Dominik Lemm Jiang Wang Adria Pérez ... Yaoyi Chen Simon Olsson Gianni de Fabritiis Frank Noé C. Clementi AI4CE 29 158 0 22 Jul 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,660 0 05 Dec 2016