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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

Quantum Computers and Hybrid Machine Learning Models for the Discovery of Lightweight Structural Alloys

Author(s)

Soham Suryakant Panchal, Megh Raval, Raj Pandya, Vishvesh Badheka

On-Site Speaker (Planned)

Soham Suryakant Panchal

Abstract Scope

This research explores new paradigms that help with the critical obstacles faced in the discovery of novel lightweight alloys by integrating Machine learning models and quantum computing. We explore a suite of quantum-enhanced computational techniques, including the Variational Quantum Eigensolver (VQE), quantum annealing, Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNN). The efficiency of this hybrid model is demonstrated with the help of targeted case studies, including the accurate prediction of phase stability in high-entropy alloys, the determination of stacking fault energies in magnesium-based alloys with over 90%(Theoretical) validation accuracy, and high-fidelity simulations of corrosion inhibitor binding on aluminium surfaces. The full scale discovery of these models is still a future goal, these models serve as a powerful tool, solving computationally prohibitive subproblems to drastically accelerate the materials characterization pipeline. This work establishes transferable and viable workflow, paving the way for the accelerated design of next-generation structural materials.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning

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