# Uncertainty Prediction for Machine Learning Models of Material Properties

arXiv: https://arxiv.org/abs/2107.07997 · HTML: https://mlsanity.com/paper/2107.07997/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 39 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates](https://mlsanity.com/paper/1704.07423/) (2017)

## Built on by (most cited first, 10 of 10)

- [Materials Property Prediction with Uncertainty Quantification: A Benchmark Study](https://mlsanity.com/paper/2211.02235/) (2022)
- [Large language models as uncertainty-calibrated optimizers for experimental discovery](https://mlsanity.com/paper/2504.06265/) (2025)
- [Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design](https://mlsanity.com/paper/2207.04994/) (2022)
- [Reliability and Interpretability in Science and Deep Learning](https://mlsanity.com/paper/2401.07359/) (2024)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [An information-matching approach to optimal experimental design and active learning](https://mlsanity.com/paper/2411.02740/) (2024)
- [Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity](https://mlsanity.com/paper/2208.10784/) (2022)
- [Training Variation of Physically-Informed Deep Learning Models](https://mlsanity.com/paper/2510.03416/) (2025)
- [Modelling magnetic material properties with uncertainty-aware neural networks](https://mlsanity.com/paper/2606.11870/) (2026)
- [Distribution-Free Conformal Prediction for Steel Fatigue Strength: Marginal Validity Is Not Enough](https://mlsanity.com/paper/2608.07589/) (2026)

## Abstract (arXiv)

Uncertainty quantification in Artificial Intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in material science. While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i.e., the evaluation of the uncertainty on each prediction, are seldomly available. In this work we compare 3 different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties. Specifically, we investigated using the Quantile loss function, machine learning the prediction intervals directly and using Gaussian Processes. We identify each approachs advantages and disadvantages and end up slightly favoring the modeling of the individual uncertainties directly, as it is the easiest to fit and, in most cases, minimizes over-and under-estimation of the predicted errors. All data for training and testing were taken from the publicly available JARVIS-DFT database, and the codes developed for computing the prediction intervals are available through JARVIS-Tools.