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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

Bayesian Optimization for Inverse Design of Pt-Au Films with Custom Friction Properties

Author(s)

Nathan Brown, David Montes de Oca Zapiain, Tomas Babuska, John Curry

On-Site Speaker (Planned)

Nathan Brown

Abstract Scope

Determining surface friction evolution often requires resource-intensive experiments, especially for complex materials like platinum-gold (Pt-Au) alloys, where variability stems from intricate properties and surface interactions. Rapidly designing alloys with custom surface friction properties adds further challenges. This study employs a Bayesian optimization-based inverse design approach combined with multi-modal machine learning surrogate models to predict the compositional and manufacturing parameters of Pt-Au films necessary for achieving a wide range of dynamic friction evolutions. The multi-modal surrogate model, based on an encoder-decoder regression architecture, was trained on both experimental and simulated data, including modulus and hardness measurements, X-ray fluorescence (XRF) spectra, and SimTra analysis. The inverse design approach successfully generated Pt-Au alloy films with friction evolution properties closely matching the desired specifications. This method significantly accelerates the process compared to traditional trial-and-error approaches for material discovery. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions...

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Mechanical Properties, Computational Materials Science & Engineering

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