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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title Revealing Nanoscale Features Controlling Diffusion Within Multi-component Alloys through Machine Learning
Author(s) S. Mohadeseh Taheri-Mousavi, S. Sina Moeini-Ardakani, Ryan W. Penny, Ju Li, A. John Hart
On-Site Speaker (Planned) S. Mohadeseh Taheri-Mousavi
Abstract Scope The immense compositional breadth of non-dilute multi-component and concentrated alloys has made their well-targeted design extremely challenging. Here, we present a newly developed numerical framework whereby deep learning algorithms supervised by atomistic-scale simulations are used to explore the nanoscale features controlling the diffusivity of atomic components in heavily alloyed compounds. Due to inherent non-linear optimization of the machine learning algorithms, the prediction accuracy is at least 10-fold improved over a conventional clustering method. Analysis of all possible atomic configurations and compositions within a model NiAl alloy reveals how the propensity of Al to form short-range-order near vacancies correlates with the generalized stacking fault energy of configurations with mobile Ni atoms. In the future, this approach can guide the selection of composition and processing parameters for conventional as well as additive manufacturing techniques, and it could enable design of metals with tailored gradient diffusivity for high temperature applications.
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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