ProgramMaster Logo
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 Elastic Properties Machine-learning-based Descriptor for a Refractory High Entropy Alloy
Author(s) Guillermo Vazquez Tovar, Prashant Singh, Daniel Sauceda, Raymundo Arroyave
On-Site Speaker (Planned) Guillermo Vazquez Tovar
Abstract Scope The discovery of new materials is crucial for the development of new and existing technologies, and the infinitely big material design space may hinder better options for modern demand. In this work, we address the need for a computational model for the elastic properties of the alloy system MoNbTaVW. The proposed accurate and computationally inexpensive model is based on using elastic data from density functional theory (DFT) stress-strain calculations to build a machine learning-based descriptor: SISSO (Sure Independence Screening Sparsifying Operator). SISSO does a feature selection of a space made from combinations of atomic features. The final descriptor has an accuracy similar to experimental characterization for the elastic constants C11, and bulk modulus K. Since this method relies on the combination of physical features, the final descriptor returns a physically meaningful expression that contains relevant atomic values to tune for the desired material design.
Proceedings Inclusion? Planned:
Keywords High-Entropy Alloys, 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