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

2025/06/13 by Sk Md Ahnaf Akif Alvi, Mrinalini Mulukutla, Alvi, Sk Md Ahnaf Akif +15 · 1 citation
Computer Science · Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #FOS: Computer and information sciences #FOS: Physical sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2506.14828

openalex publication_date 2025/06/13 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

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.

Citations

• Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science
• Hierarchical Gaussian Process-Based Bayesian Optimization for Materials Discovery in High Entropy Alloy Spaces
• Machine learning-enabled high-entropy alloy discovery
• A Unified Approach to Interpreting Model Predictions
• Doubly Stochastic Variational Inference for Deep Gaussian Processes
• Gaussian Processes for Big Data
• Deep Gaussian Processes
• Kernels for Vector-Valued Functions: a Review
• Taking the Human Out of the Loop: A Review of Bayesian Optimization

Cited by

• Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization for Complex Materials Design Campaigns

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