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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Bayesian Optimization Driven Atomistic Simulation Alloy Co-design for Additive Manufacturing
Author(s) Sk Md Ahnaf Akif Alvi, Jan Janssen, Danial Khatamsaz, Douglas Allaire, Danny Perez, Raymundo Arroyave
On-Site Speaker (Planned) Sk Md Ahnaf Akif Alvi
Abstract Scope To address the inverse materials design challenge of identifying complex alloys suitable for additive manufacturing (AM), we propose a hierarchical Bayesian optimization (BO) approach based on atomistic simulation coupling different levels of theory. We show how atomistic simulations with different levels of accuracy can be combined in a multi-information source fusion-based BO to predict key macroscopic material properties. We demonstrate that this approach can predict the concentration-dependent melting temperature for a complex alloy with high accuracy by combining a hierarchy of material properties calculated with varying levels of accuracy. This automated workflow is enabled by our in-house BO techniques integrated in the pyiron framework to optimize the utilization of computing resources. This approach can provide critical information that would allow for the systematic design of new alloys for a broad range of AM applications.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Additive Manufacturing, High-Entropy Alloys

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