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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title Exploring the Compositional Space of High Entropy Alloys via Sequential Learning
Author(s) Juan Carlos Verduzco, Zachary McClure, David Farache, Saaketh Desai, Alejandro H Strachan
On-Site Speaker (Planned) Juan Carlos Verduzco
Abstract Scope Refractory complex concentrated alloys (RCCAs) have shown high temperature strength surpassing superalloys and are of interest for a range of applications. Despite this interest, melting temperature values are scarce as experimental measurements are challenging. To address this challenge, we combine molecular dynamics simulations with sequential learning to develop predictive models for the melting temperature RCCAs as a function of composition and find high-melting temperature alloys. Using MD simulations as a service using nanoHUB, we create fully autonomous research workflows and show efficient exploration of the high-dimensional compositional space towards the optimal alloy composition. We will discuss challenges that arise from the stochastic nature of MD simulations and the uncertainties associated in the data-drive and physics-driven models.
Proceedings Inclusion? Planned:
Keywords High-Entropy Alloys, Machine Learning, Computational Materials Science & Engineering

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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