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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title Uncertainty Reduction for Calculated Phase Equilibria
Author(s) Richard A. Otis, Brandon Bocklund, Zi-Kui Liu
On-Site Speaker (Planned) Richard A. Otis
Abstract Scope The development of a consistent framework for Calphad model sensitivity is necessary for the rational reduction of uncertainty via new models and experiments. In the present work, a sensitivity theory for Calphad was developed, and a closed-form expression for the log-likelihood gradient and Hessian of a multi-phase equilibrium measurement was derived. A case study of the Cr-Ni system was used to demonstrate visualizations and analyses enabled by the developed theory. Criteria based on the classical Cramér–Rao bound were shown to be a useful diagnostic in assessing the accuracy of Bayesian parameter covariance estimates from Markov Chain Monte Carlo. The developed sensitivity framework was applied to estimate the statistical value of phase equilibria measurements in comparison with thermochemical measurements, with implications for Calphad model uncertainty reduction, as well as the design of new experiments.
Proceedings Inclusion? Planned:

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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