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

2025-06-13

AI Summary

Addressing the challenges of heteroscedasticity, heterogeneous data structures, and missing values in predicting multiple mechanical properties—yield strength, hardness, modulus, tensile strength, elongation, and dynamic/quasi-static average hardness—of the AlCoCrCuFeMnNiV high-entropy alloy (HEA), this work proposes a prior-guided deep Gaussian process (DGP) multi-task surrogate model. The model uniquely integrates physics-informed priors with a deep Gaussian process architecture to enable joint modeling of strongly correlated output properties and input-dependent uncertainty quantification. Evaluated on hybrid experimental–computational datasets, it achieves 12–28% higher multi-task prediction accuracy compared to cGP, XGBoost, and encoder–decoder networks, while reducing uncertainty calibration error by 35%. These improvements significantly enhance the reliability and generalizability of HEA accelerated design.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Predicting HEA properties with uncertainty-aware models
• Evaluating surrogate models for multi-task material predictions
• Enhancing accuracy in correlated property predictions

Innovation

• Deep Gaussian Processes for multi-task prediction
• Machine learning-based prior enhances DGP performance
• Handles heteroscedastic and incomplete data effectively