LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force
Fields

LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields

1 February 2024

Fei Zhou

Papers citing "LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields"

7 / 7 papers shown

Title
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 16 41 0 02 May 2023
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs Kelvin Guu Albert Webson Ellie Pavlick Lucas Dixon Ian Tenney Tolga Bolukbasi TDI 63 33 0 14 Mar 2023
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training Krishnateja Killamsetty D. Sivasubramanian Ganesh Ramakrishnan A. De Rishabh K. Iyer OOD 78 184 0 27 Feb 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 183 1,218 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 207 370 0 20 Oct 2020
Estimating Example Difficulty Using Variance of Gradients Chirag Agarwal Daniel D'souza Sara Hooker 190 103 0 26 Aug 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016