Conference Tools for 2021 TMS Annual Meeting & Exhibition

Login

Register as a New User

Help

Submit An Abstract

Propose A Symposium

Presenter/Author Tools

Organizer/Editor Tools

About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title	Determining Solute Site Preference and Correlations to Antiphase Boundary Energy in Ni-based Superalloys
Author(s)	Enze Chen, Tao Wang, Mario Epler, Timofey Frolov, Mark Asta
On-Site Speaker (Planned)	Enze Chen
Abstract Scope	Ni-based superalloys are a superior class of structural materials used in aircraft turbines and power plants due to their excellent strength, creep resistance, and corrosion resistance at high temperatures. In particular, their high-temperature strength is linked to high antiphase boundary (APB) energy in the Ni3Al precipitates, which motivates a better understanding for how chemical heterogeneity affects the APB energy. The APB energy varies with not only solute chemistry and concentration, but also sublattice site preference in the ordered (L12 structure) Ni3Al precipitates. We use a thermodynamic model implemented in PyDII combined with density functional theory calculations to predict the site preference of alloying additions to Ni3Al and derive descriptors that correlate with high APB energy. We discuss how this methodology allows us to intelligently screen for promising superalloy chemistries through validation with a subset of common alloying elements.
Proceedings Inclusion?	Planned:
Keywords	High-Temperature Materials, Modeling and Simulation, Machine Learning

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

Questions about ProgramMaster? Contact programming@programmaster.org