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Meeting MS&T23: Materials Science & Technology
Symposium Computational Discovery, Understanding, and Design of Multi-principal Element Materials
Presentation Title Hybrid Machine Learning Approach for Designing Refractory High Entropy Alloys
Author(s) Debasis Sengupta, Stephen Giles, Hugh Shortt, Peter Liaw
On-Site Speaker (Planned) Debasis Sengupta
Abstract Scope Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved materials and reduced time and cost in production. Refractory high entropy alloys (RHEAs) are a class of materials that are capable of excellent high-temperature properties. However, due to their multi-component nature, RHEAs have a vast composition space which presents challenges for traditional experimental exploration. In this work, we have used a number of machine learning method to predict room and high temperature yield strengths of RHEAs. The predicted results are also validated against experimental synthesis and characterization. However, our validation showed that no one method is consistently superior to the others. This work develops a novel graph-based hybrid method to intelligently combine the predictions of a number of machine learning methods. We demonstrated that the predictions of the hybrid method are superior to the all methods used in this work.


A New Modified Embedded Atom Method Potential to Understand Plasticity in VNbTaTiZr High Entropy Alloy
Ab-Initio Investigation of Jahn-Teller Distortions within High Entropy Oxide Systems Using Recently Developed Meta-GGA Functionals
Charge-Density based Convolutional Neural Networks for Property Prediction in High Entropy Alloys
Computational Microstructural Design for Multi-phase Multi-principal Element Alloys
Computational Studies of Deformation Twinning in BCC Complex Concentrated Alloys
Critical Shear Stress Distribution and Average Dislocation Mobility in FeNiCrCoCu High Entropy Alloys Computed via Atomistic Simulations
Effect of Elasticity in Microstructural Evolution of Multi-component, Multi-phase System
Effects of Chemical Short-range Order in Medium Entropy Alloy CoCrNi
First-principles Study for Discovery of High-entropy MXenes
Hybrid Machine Learning Approach for Designing Refractory High Entropy Alloys
Microstructural Engineering via Heat Treatments in Multi-principal Element Alloy Systems with Miscibility Gaps
Modelling and Simulation on Mechanical Behavior of High-entropy Alloys
Phase Field Simulation of AgCuNi Ternary Alloy: Exploring Ag-CuNi Precipitation and Immiscibility
Predicting Ideal Shear Strength of Dilute Multicomponent Ni-based Alloys by an Integrated First-principles, CALPAHD, and Correlation Analysis
The Elastic Properties and Stacking Fault Energy of FeNiMoW
Yield Strength-Plasticity Trade-off and Uncertainty Quantification in ML-based Design of Refractory High-entropy Alloys

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