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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Hume-Rothery Symposium: Accelerated Measurements and Predictions of Thermodynamics and Kinetics for Materials Design and Discovery
Presentation Title High-throughput experiments and machine learning modeling for designing next generation superalloys
Author(s) Akane Suzuki, Chen Shen
On-Site Speaker (Planned) Akane Suzuki
Abstract Scope High-throughput experiments and machine learning modeling are increasingly becoming essential tools for efficiently and successfully designing new alloy chemistries that are tailored to achieve desired combinations of properties. In this talk, we will present examples of applying high-throughput experiments using diffusion multiples and machine learning modeling of physical, mechanical and environmental properties using historical and/or new datasets in designing next generation Ni-based and Co-based superalloys for industrial power generation gas turbines and aircraft engines. Current limitations of these tools and challenges for future industrial applications will be discussed.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Diffusion Mobility Database for γ/ γ' Co-Superalloys
A Tale of Two Approaches: From Phase Equilibria to Materials Properties
A Thermodynamic and Molar Volume Database for Co-base Superalloys
An Atom-Probe Tomogaphy Study of the Temporal Evolution of Concentration Retention Excesses and Depletions at gamma-f.c.c/gamma-prime-L12 Interfaces in a Ni-Al-Cr-Re Superalloy
An Integrated Computational Materials Engineering (ICME) Framework for Additive Manufacturing (AM) of Ni-based Superalloys
Combinatorial design of high-entropy alloys
Computational Modeling-Assisted Development of Cast Alumina-forming Austenitic Stainless Steels for High Temperature Corrosive Environments
Computational Thermodynamics and its Applications
Design of Cobalt Base Superalloys for 3D Printing
Emerging Capabilities for the High-Throughput Characterization of Structural Materials
Extended Applications of the CALPHAD Simulations
Genomic Materials Design: From CALPHAD Data to Flight
High-throughput experiments and machine learning modeling for designing next generation superalloys
High-Throughput Hot-Isostatic-Pressing Micro-Synthesis for Accelerated Studies of High Entropy Alloys
High-Throughput Synthesis, Characterization and Prediction of Metallic Glass Formation
High-throughput Testing and Characterization of Novel Additive Manufactured Materials
Insights from a Comprehensive Assessment of Diffusion Coefficients of 20 Binary Systems and a Comprehensive Diffusion Mobility Database for Magnesium Alloys
Integrated Predictive Materials Science – Filling the ICME Pipeline
Integration of computational tools and advanced characterization methods to understand phase transformations in additively manufactured steels
Machine Learning-assisted ICME Approaches to Explore the Alloy and Process Space in Metals Additive Manufacturing
Modeling of diffusion and intermetallic phase formation in Al-Mg bimetallic structures
Multi-cell Monte Carlo method for phase prediction
Phase stability and kinetic considerations in materials processing and performance
Phonon Anharmonicity Causes the Large Thermal Expansion of NaBr
Printability and Properties of Metallic Alloys for Laser Powder Bed Fusion Additive Manufacturing
Some properties if the multicomponent diffusivity matrix
Unexpected phenomena observed in metallurgical studies
Visualizing and Rationalizing Synthesis Pathways in Oxides
William Hume-Rothery Award Lecture: High-throughput Measurements of Composition-dependent Properties of Alloy Phases for Accelerated Alloy Design

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