Conference Tools for 2021 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title	Toward High Throughput Design and Development of Multi-principal Element Alloys for Corrosion and Oxidation Resistance (MPEAs)
Author(s)	Mitra L. Taheri, Todd Hufnagel, Chris Wolverton, James Rondinelli, Jason Hattrick-Simpers, Brian DeCost, Elizabeth Opila, John Scully, Jean-Philippe Couzinie, Nick Birbilis
On-Site Speaker (Planned)	Mitra L. Taheri
Abstract Scope	Multi-Principal Element Alloys (MPEAs) are the subject of emerging interest due to their compositional profile, which holds the promise of superior mechanical properties and thermal stability. It is critical to understand the atomic to mesoscale tuning parameters for MPEAs to harness critical properties, such as corrosion/oxidation resistance, for coatings and extreme applications. With millions of permutations of MPEAs in existence, however, it’s virtually impossible to nail down the “right” combination without innovation. Recent advances in high throughput approaches present an opportunity for alloy design and testing that enabling tracking, curation, and dissemination of thousands of MPEAs. This talk reviews results from a combination of materials design, machine learning, and high throughput characterization in a team effort to (1) explore currently untapped compositional space, (2) predict and control passivation/complex oxide evolution, and (3) define alloy/corrosive environment operating parameters based on bulk and surface phenomena.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, High-Entropy Alloys, Characterization

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advanced Data SCiENce Toolkit for Non-data Scientists (ASCENDS) - A Case Study of the Oxidation Kinetics of NiCr-based Alloys

Coupling of Data Mining, Thermodynamics and Multi-objective Genetic Algorithms for the Design of High-temperature Alloys

Determining Solute Site Preference and Correlations to Antiphase Boundary Energy in Ni-based Superalloys

Domain and Uncertainty Quantification in Machine Learning Models of Alloy Properties

Domain Knowledge-informed, Process-mapping AI Graph for Designing Fe-based Alloys

Elastic Properties Machine-learning-based Descriptor for a Refractory High Entropy Alloy

Expanding Materials Selection via Transfer Learning for High-temperature Oxide Selection

Exploring the Compositional Space of High Entropy Alloys via Sequential Learning

Knowledge-driven Platform for Federated Multimodal Big Data Storage & Analytics

Machine Learning Augmented Predictive & Generative Models for Rupture Life in High Temperature Alloys

Optimal Design of High-temperature, Oxidation-resistant Complex Concentrated Alloys

Predicting Vibrational Entropy of FCC Solids Uniquely from Bond Chemistry Using Machine Learning

Predicting Yield Stress of High Temperature Alloys via Computer Vision and Machine Learning

Revealing Nanoscale Features Controlling Diffusion Within Multi-component Alloys through Machine Learning

Toward High Throughput Design and Development of Multi-principal Element Alloys for Corrosion and Oxidation Resistance (MPEAs)

Uncertainty Quantification for Thermo-mechanical Behavior of Aircraft Engine Materials in Elevated Temperatures

Uncertainty Reduction for Calculated Phase Equilibria

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