Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes

TL;DR

This paper compares various surrogate models for predicting multiple properties of high-entropy alloys, demonstrating that deep Gaussian processes with priors outperform others in accuracy and uncertainty estimation, aiding materials discovery.

Contribution

It systematically evaluates and compares surrogate models for multi-property prediction in HEAs, highlighting the superior performance of prior-guided deep Gaussian processes.

Findings

Deep Gaussian processes with priors outperform other models.

Hierarchical models effectively handle heteroscedastic and incomplete data.

Enhanced uncertainty quantification improves materials property predictions.

Abstract

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the fitting performance of four prominent surrogate models conventional Gaussian Processes(cGP), Deep Gaussian Processes(DGP), encoder-decoder neural networks for multi-output regression and XGBoost applied to a hybrid dataset of experimental and computational properties in the AlCoCrCuFeMnNiV HEA system. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic and quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical and deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials informatics. Our findings illustrate that DGP infused with machine learning-based prior outperform other surrogates by effectively capturing inter-property correlations and input-dependent uncertainty. This enhanced predictive accuracy positions advanced surrogate models as powerful tools for robust and data-efficient materials design.