Materials Property Prediction with Uncertainty Quantification: A
Benchmark Study

Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

4 November 2022

Daniel Varivoda

Sadman Sadeed Omee

Papers citing "Materials Property Prediction with Uncertainty Quantification: A Benchmark Study"

9 / 9 papers shown

Title
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles Tirtha Vinchurkar Kareem Abdelmaqsoud John R. Kitchin AI4CE 91 0 0 17 Apr 2025
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study Sadman Sadeed Omee Nihang Fu Rongzhi Dong Ming Hu Jianjun Hu OOD 23 17 0 16 Jan 2024
Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity Pascal Pernot 17 9 0 12 Sep 2023
MUBen: Benchmarking the Uncertainty of Molecular Representation Models Yinghao Li Lingkai Kong Yuanqi Du Yue Yu Yuchen Zhuang Wenhao Mu Chao Zhang 22 9 0 14 Jun 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
Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts P. Pernot 11 6 0 13 Mar 2023
Composition based oxidation state prediction of materials using deep learning Nihang Fu Jeffrey Hu Yingqi Feng G. Morrison H. Loye Jianjun Hu 14 1 0 29 Nov 2022
Scalable deeper graph neural networks for high-performance materials property prediction Sadman Sadeed Omee Steph-Yves M. Louis Nihang Fu Lai Wei Sourin Dey Rongzhi Dong Qinyang Li Jianjun Hu 68 73 0 25 Sep 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