Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

17 November 2022

Albert J. W. Zhu

Simon L. Batzner

Albert Musaelian

Papers citing "Fast Uncertainty Estimates in Deep Learning Interatomic Potentials"

19 / 19 papers shown

Title
Accelerating and enhancing thermodynamic simulations of electrochemical interfaces Xiaochen Du Mengren Liu Jiayu Peng Hoje Chun Alexander Hoffman Bilge Yildiz Lin Li Martin Z. Bazant Rafael Gómez-Bombarelli 51 0 0 22 Mar 2025
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Matthias Holzenkamp Dongyu Lyu Ulrich Kleinekathöfer Peter Zaspel 33 0 0 10 Jan 2025
Learning charges and long-range interactions from energies and forces Dongjin Kim Daniel S. King Peichen Zhong Bingqing Cheng 89 5 0 19 Dec 2024
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning Kisung Kang Thomas A. R. Purcell Christian Carbogno Matthias Scheffler AI4CE 22 0 0 18 Sep 2024
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces Luis Itza Vazquez-Salazar Silvan Käser Markus Meuwly EDL 24 3 0 27 Feb 2024
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables Aik Rui Tan Johannes C. B. Dietschreit Rafael Gómez-Bombarelli 20 2 0 06 Feb 2024
LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields Joshua A. Vita Amit Samanta Fei Zhou Vincenzo Lordi 16 2 0 01 Feb 2024
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials Viktor Zaverkin David Holzmüller Henrik Christiansen Federico Errica Francesco Alesiani Makoto Takamoto Mathias Niepert Johannes Kastner AI4CE 19 13 0 03 Dec 2023
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows Victor J. Leon Noah Ford Honest Mrema Jeffrey Gilbert Alexander New UQCV AI4CE 8 0 0 31 Oct 2023
Band-gap regression with architecture-optimized message-passing neural networks Tim Bechtel Daniel T. Speckhard Jonathan Godwin Claudia Ambrosch-Draxl 11 0 0 12 Sep 2023
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions Janosh Riebesell Rhys E. A. Goodall Philipp Benner Chiang Yuan Bowen Deng A. Lee Anubhav Jain Kristin A. Persson OOD 24 33 0 28 Aug 2023
Accurate machine learning force fields via experimental and simulation data fusion Sebastien Röcken J. Zavadlav AI4CE 18 12 0 17 Aug 2023
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles Aik Rui Tan S. Urata Samuel Goldman Johannes C. B. Dietschreit Rafael Gómez-Bombarelli BDL 24 41 0 02 May 2023
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size Albert Musaelian A. Johansson Simon L. Batzner Boris Kozinsky 22 48 0 20 Apr 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner J. Zavadlav 14 21 0 15 Dec 2022
Transfer learning for chemically accurate interatomic neural network potentials Viktor Zaverkin David Holzmüller Luca Bonfirraro Johannes Kastner 15 24 0 07 Dec 2022
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 190 1,229 0 08 Jan 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,652 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,134 0 06 Jun 2015