# Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

arXiv: https://arxiv.org/abs/2211.02235 · HTML: https://mlsanity.com/paper/2211.02235/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement](https://mlsanity.com/paper/2204.09308/) (2022)
- [Uncertainty Quantification Using Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2005.10036/) (2020)
- [Scalable deeper graph neural networks for high-performance materials property prediction](https://mlsanity.com/paper/2109.12283/) (2021)
- [Assigning Confidence to Molecular Property Prediction](https://mlsanity.com/paper/2102.11439/) (2021)
- [Uncertainty Prediction for Machine Learning Models of Material Properties](https://mlsanity.com/paper/2107.07997/) (2021)
- [Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.13425/) (2020)
- [Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks](https://mlsanity.com/paper/1810.06118/) (2018)
- [Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction](https://mlsanity.com/paper/2003.13379/) (2020)
- [Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks](https://mlsanity.com/paper/1809.09060/) (2018)
- [Active learning based generative design for the discovery of wide bandgap materials](https://mlsanity.com/paper/2103.00608/) (2021)

## Built on by (most cited first, 15 of 15)

- [Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles](https://mlsanity.com/paper/2305.01754/) (2023)
- [Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study](https://mlsanity.com/paper/2401.08032/) (2024)
- [Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity](https://mlsanity.com/paper/2309.06240/) (2023)
- [MUBen: Benchmarking the Uncertainty of Molecular Representation Models](https://mlsanity.com/paper/2306.10060/) (2023)
- [Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts](https://mlsanity.com/paper/2303.07170/) (2023)
- [Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances](https://mlsanity.com/paper/2407.10844/) (2024)
- [When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem](https://mlsanity.com/paper/2511.17760/) (2025)
- [Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?](https://mlsanity.com/paper/2605.22593/) (2026)
- [Composition based oxidation state prediction of materials using deep learning](https://mlsanity.com/paper/2211.15895/) (2022)
- [Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles](https://mlsanity.com/paper/2504.12627/) (2025)
- [Aligning Reasoning LLMs for Materials Discovery with Physics-aware Rejection Sampling](https://mlsanity.com/paper/2509.00768/) (2025)
- [Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification](https://mlsanity.com/paper/2511.11697/) (2025)
- [A Critical Examination of Active Learning Workflows in Materials Science](https://mlsanity.com/paper/2601.05946/) (2026)
- [Distribution-Free Conformal Prediction for Steel Fatigue Strength: Marginal Validity Is Not Enough](https://mlsanity.com/paper/2608.07589/) (2026)
- [CrystalJev: calibrated foundation-model probabilities decide what to compute next in materials discovery](https://mlsanity.com/paper/2610.06985/) (2026)

## Abstract (arXiv)

Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on getting new training data from uncertain regions. There are several categories of UQ methods each considering different types of uncertainty sources. Here we conduct a comprehensive evaluation on the UQ methods for graph neural network based materials property prediction and evaluate how they truly reflect the uncertainty that we want in error bound estimation or active learning. Our experimental results over four crystal materials datasets (including formation energy, adsorption energy, total energy, and band gap properties) show that the popular ensemble methods for uncertainty estimation is NOT the best choice for UQ in materials property prediction. For the convenience of the community, all the source code and data sets can be accessed freely at urlhttps://github.com/usccolumbia/materialsUQ.