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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Beyond Gaussian Noise: Symmetric and Asymmetric Likelihood Models for Robust Materials Optimization

Author(s)

Andrew Falkowski, Taylor Sparks, Stanley Wessman

On-Site Speaker (Planned)

Andrew Falkowski

Abstract Scope

Machine learning-driven materials optimization is a prominent means of accelerating the discovery and development of new materials. However, the Gaussian process models commonly used for these tasks are easily derailed by outliers. To address this, we investigated alternative symmetric and asymmetric likelihood distributions for automatically filtering outliers and capturing true trends in active learning campaigns. Specifically, we examine the performance of Student's t, Laplace, and skew-normal likelihood models in optimizing across three synthetic and five experimental materials datasets. In all but one case, the alternative likelihood models consistently identify optimal parameter combinations in fewer iterations and more efficiently locate top-performing candidates compared with a standard Gaussian process model. In addition to demonstrating effectiveness, we provide heuristics for model selection based on observed noise characteristics in preliminary data. The methods developed in this work provide researchers with new tools for improving the robustness of optimization campaigns to random and systematic outliers.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning,

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