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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Accelerated Discovery and Insertion of Next Generation Structural Materials
Presentation Title Machine Learning-assisted Discovery of Novel High Temperature Ni-rich NiTiHfZr Multi-component Shape Memory Alloys
Author(s) John Broucek, Daniel Salas, William Trehern, Ibrahim Karaman
On-Site Speaker (Planned) John Broucek
Abstract Scope Machine learning (ML) and artificial intelligence (AI) are emerging in material science and engineering as methods to discover novel materials for various applications. AI/ML methods can survey small and unexplored regions of a material design space with desired shape memory characteristics, increasing the rate of material discovery by limiting the number of experiments needed to demonstrate the properties of interest. In this work, we utilized ML to predict the martensitic transformation characteristics of Ni-rich NiTiHfZr multi-component shape memory alloys intended for high-temperature aerospace applications. These alloys are characterized by having multiple principal elements and typically demonstrate martensitic transformation between B2 austenite and B19’ martensite occurring at temperatures as high as 600C. Experimental examination of thermal properties such as transformation temperatures, and enthalpy of transformation was conducted to validate predictions through direct comparison. Active learning was used during data collection to further enhance the accuracy of predictions.
Proceedings Inclusion? Planned:
Keywords High-Entropy Alloys, High-Temperature Materials, Machine Learning

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Design Space for Tunable Ceramic-polymer Composites
A Diffusion Couple Approach to β-Ti Alloy Development: Evaluating the Oxidation Performance of Ti-Fe-X+ Alloys
A High-throughput Setup for Materials Exposure to Simultaneous Irradiation-corrosion Conditions
Accelerated Discovery of Novel Titanium Alloys using High-throughput Manufacturing, Characterization and Testing
Accelerating Multimodal Data Collection: A Workflow for Metallic Films
AI and Machine Learning Tools for Development and Analysis of Image Driven 2D Materials
Combinatorial Mechanical Microscopy via Correlated Nanoindentation and EDX Mapping
Computational Design of an Ultra-strong High-entropy Alloy
Computational Design of High Entropy Alloy Hardmetals
Design of a Compact Morphology Cobalt-based Superalloy for Additive Manufacturing
Efficient Conductivity and Hardness Optimization in Cu-Ag-Ni Alloys using Bayesian Active Learning
High-throughput Electric-Field-assisted Sintering and Characterization Techniques for Materials Discovery
High-throughput Prediction of Fracture and Brittle to Ductile Transition in Tungsten using Variable Temperature Nanoindentation
High-throughput Synthesis and Mechanical Characterization of Sputtered Metallic Alloys
How Should You Select an Algorithm for a Materials Discovery Campaign with Multiple Objectives, Complex and High-dimensional Structure-processing-property Relationships, and a Small Adaptive Design Budget?
Machine Learning-assisted Discovery of Novel High Temperature Ni-rich NiTiHfZr Multi-component Shape Memory Alloys
Rapid Characterisation of Active Slip Systems in Titanium Ordered-bcc Compounds using an Algorithm for Automated Indentation Slip Trace Analysis.
Using Machine Intuitive Learning to Predict Advanced Steel Properties

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