Conference Tools for 2024 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Computational Discovery and Design of Materials
Presentation Title	Methodology And Performance of a Deep Learning Model for Property Predictions and Discovery of Ni-based Superalloys
Author(s)	Vanessa Oklejas, Scott W. Cochran, James H Lynch, Brian T Gockel
On-Site Speaker (Planned)	Vanessa Oklejas
Abstract Scope	Nickel-based superalloys exhibit resistance to creep at temperatures approaching their melting point; They are an essential class of structural materials used in high temperature industrial applications (aircraft engines, generators, etc.). The inexorable drive toward lower costs and improved energy efficiency places a premium on the discovery of new superalloys with improved creep resistance at industrially relevant temperatures with materials that are cheaper and/or are more readily processed. Toward that end, results from a deep learning-based approach for novel Ni-based superalloys discovery will be reported. The impact of an array of descriptors derived from experimental and computational sources, inspired in part by PHACOMP-based methods, on model performance and transferability will be presented. Additionally, strategies for the development of representations of the design space to enable generative models for inverse design (and global optimization of alloy composition) will be discussed.
Proceedings Inclusion?	Planned:
Keywords	ICME, High-Temperature Materials, Machine Learning

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